Instruct-SkillMix: A Powerful Pipeline for LLM Instruction Tuning
Authors: Simran Kaur
Simon Park
Anirudh Goyal
Sanjeev Arora
Venue: ICLR 2025 Poster (2025)
Keywords: instruction tuning
high quality synthetic data
diverse synthetic data
Primary area: foundation or frontier models, including LLMs
TL;DR: We introduce an automated approach for creating diverse, high quality SFT data for instruction-following.

Abstract: We introduce INSTRUCT-SKILLMIX, an automated approach for creating diverse, high quality SFT data for instruction-following. The pipeline involves two stages, each leveraging an existing powerful LLM: (1) Skill extraction: uses the LLM to extract core “skills” for instruction-following by directly prompting the model. This is inspired by “LLM metacognition” of (Didolkar et al., 2024); (2) Data generation: uses the powerful LLM to generate (instruction, response) data that
exhibit a randomly chosen pair of these skills. Here, the use of random skill combinations promotes diversity and difficulty. The estimated cost of creating the dataset is under $600. 

Vanilla SFT (i.e., no PPO, DPO, or RL methods) on data generated from INSTRUCT-SKILLMIX leads to strong gains on instruction following benchmarks such as AlpacaEval 2.0, MT-Bench, and WildBench. With just 4K examples, LLaMA-3-8B-Base achieves 42.76% length-controlled win rate on AlpacaEval 2.0, a level similar to frontier models like Claude 3 Opus and LLaMA-3.1-405B-Instruct. Ablation studies also suggest plausible reasons for why creating open instruction-tuning datasets via naive crowd-sourcing has proved difficult. In our dataset,adding 20% low quality answers (“shirkers”) causes a noticeable degradation in performance.

The INSTRUCT-SKILLMIX pipeline seems flexible and adaptable to other settings.

Published as a conference paper at ICLR 2025
INSTRUCT-SKILLMIX A POWERFUL PIPELINE FOR LLM INSTRUCTION TUNING
Simran Kaur', Kimon
Anirudh Goyal", Sanjeev Arora' 1 Princeton Language and Intelligence (PLI), Princeton University
Simon Park
2 Meta
ABSTRACT
We introduce INSTRUCT-SKILLMIX an automated approach for creating diverse, high quality SFT data for instraction-follovinn The pipeline involves two stages, each leveraging an existing powerful I.IM:: (1) Skill extraction: uses the I.I.M to extract core "skills" for cnstruction-followiin by directly prompting the model. This is inspired by "LLM metacognition" of Didolkar et al. (2024); (2) Data generation: uses the powerful LLM to generate (instruction, response) data that exhibit a randomly chosen pair of these skills. Here, the use of random skill combinations promotes diversity and difficulty. The estimated cost of creating the dataset is under $600.
Vanilla SFT (i.e., no PPO, DPO, or RL methods) on data generated from INSTRUCT- SKILLMIX leads to strong gains on instruction following benchmarks such as AlpacaEval 2.0, MT-Bench, and WildBench. With just 4K examples, LLaMA-3- 8B-Base achieves 42.76% length-controlled win rate on AlpacaEval 2.0, a level similar to frontier models like Claude 3 Opus and LLaMA-3. 1..3..140000 Instrucc...
Ablation studies also suggest plausible reasons for why creating open instruction- tuning datasets via naive crowd-sourcing has proved difficult. In our dataset, adding 20% low quality answers ("shirkers") causes a noticeable degradation performance.
The INSTRUCT-SKILLLN pipeline seems flexible and adaptable to other settings.
INTRODUCTION
Instruction tuning (sometimes also called imitation learning) is the first step in converting a base LLM trained on next-word prediction into a helpful and interactive agent. Whereas early versions of instruction tuning involved supervised fine-tuning (SFT) traditional NLP question-answer datasets (Wei et al. 2022). nowadays, the SFT data is collected at high cost from skilled human annotators. We will use the term "instruction tuning" refer solely supervised fine-tuning (SFT) on such Q& A pairs - and not to reinforcement-leariin methods such as PPO/DPO/RLHF (Schulman al. 2017 Rafailov al. 2023) etc., which usually follow SFT in the pipeline.
Human- -eneeeeeeeredddeee data is expensive (e.g., even the tiny model Instruct-GPT was estimated to require 20K human hours OpenAl (2022)), (202)), which has motivated the creation of open- open-domain alternatives. ShareGPT (Chiang et al. 2023) contains conversations collected from a model-hosting website, whereas OpenAssistant (Köpf et al. 2023) and Dolly (Conover et al.) 2023) contain crowd-sourced human data. Another intriguing method, popularized by Self-Instruct (Wang et al., (023b) (and its variants, e.g., Alpaca (Taori et al. 2002)) leverages synthetic datasets. Here, strong LLM is prompted using small set of human- examples to generate large number of (query, answer) examples on a variety of topics.
Open evaluations of 2.0 (Dubois et al. 2023 2024) is based upon curated queries from various sources. In such evaluations, model's response provided query is compared against strong reference model's 's response, and the model is ranked based upon its win rate - the percentage of queries for which the model
ability have also sprung up. The popular AlpacaEval
"quual contribution.
'Source code can be found at https:ttp///o.....p. http://hhhhh.ccco//r/aaottoo////
Ski 1ki

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Published as a conference paper at ICLR 2025
produces a better answer than the reference model, as judged by powerful LLM. Rankings AlpacaEval and related benchmarks like WildBench broadly align with the human rankings of model''s performance (Dbbois et al. 2024 Lin et al. 2024).
1.1 SURPRISING DIFFICULTY INSTRUCTION TUNING
A persistent puzzle in this field is that SFT on the above public datasets does not yield good performance on the evaluations. It was initially suspected this is due to lack of diversity in the training data. But, efforts to produce more diverse synthetic data e.g., UltraChat (Ding et al. 2023), a synthetic dataset of 1 1.5M multi-turn conversations created via meticulously tracking lexical and topical diversity well coherence did not significantly improve performance.
Another hypothesis places the blame on the uneven quality of open datasets - which are usually a hodge-podge of collected queries (e.g.. Dolly (Conover al. (0233) whereas proprietary datasets are produced to careful specifications using strict quality-control One finding that supports this hypothesis is that SFT the 1K Q&A pairs in Alpaca-52K with the longest responses, outperforms SFT on all 52K pairs (Zhao et al., (024). In other words, the 51K other data-points are redundant, interfere with the "signal" present in the best 1K examples. This finding has inspired "less including an extreme one based upon just judicious set of in-context examples (Lin et al.] 2023) provide surprisingly reasonable level of instruction tuning and alignment - but they did not significantly improve the performance either.
more" approaches
Some have cautioned against hopes for miracle of instruction tuning. Gudibande et al. (2023) suggest, based upon careful experiments, that basic capabilities of the LLM arise from pre-iininigg and massive training corpus. Most deficiencies left after pre-training will be fixable by, say, million SFT examples. While this perspective feels broadly correct, it does not quite explain why open efforts instruction tune Mistral-7B-Ban -as---aaee.. fail to match the performance of its proprietary Instruct counterpart, which has only undergone SFT.
The above difficulties have lately lowered interest level in instruction tuning, with many researchers now turning to RL-based methods (e.g.. PPO, DPO), which have been used in recent open-source projects to greatly improve proprietary chat models (Meng et al. 2024), which had already trained on expensive human data.
Skill List
Synthetic Data (combine random skills)
What are some instruction
Create one Q&A using the k skills below...
following skills?
Step 1: Use powerful LLM to gather instruction-following skills.
Step 2: Create synthetic (instruction, response) pair that requires applying k provided skills.
Figure 1: Sketch of INSTRUCT-SKILLLME pipeline. See Figures 2a and 2b for more details on two
different implementations of INSTRUCT-SKILLMIN
1.2 OUR CONTRIBUTIONS
We describe more efficient and effective approach for creating synthetic instruction tuning datasets. Past open efforts invested significant human effort in ensuring high coverage of topics and scenarios to sufficiently equip the LLM for scenarios it might encounter at deployment time. We take a subtly different tack. Accepting that pre-training the dominant source of the LLM's "inner knowledge." we focus on merely teaching the LLM to draw upon that inner knowledge and present it nicely during conversations.
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The key idea is to strong LLM teacher. The recent discovery of LLM Metacognition Di dolkar et al. 2024) suggests that frontier models have significant capability to "think about thinking." which in humans is referred to as metacognition (Flavell 1979). Specifically, it was shown that given a task dataset, frontier I L.MSs can help assemble a list of named "skills" needed to solve that task. This requires human involvement apart from an automated interaction with an LIM
The first phase ("Slill Extraction") of our pipeline INSTRUCT-SKILLMMO uses this idea and frontier LLM to identify list of "basic skills" needed for darructionffolooiiggg Unlike Didolkar et al. (2024), which extracts skills from existing SFT datasets, we instead identify skills by directly prompting a strong LLM. (We also tried extracting skills using examples from Alpaca and Ulrachat, and it works quite well, but noticeably worse than our main method.) See Section 2.1
The second phase of our pipeline, Data Generation. uses the list of extracted skills produce synthetic query-response examples. Here, we repeatedly draw a random pair of skills from the list and prompt the powerful LLM to produce a suitable query that tests those two skills, and to also produce good response to the query. This generation is inspired by the SKILLMIX evaluation (Yu et al. 2024) for LLMs' compositional generalization, which also uses a predetermined list of skills. Hence we call our method INSTRUCT-SKILLMI See Section 2.2
Using merely 2K to 4K such Q&A examples, vanilla SFT allows popular small base models (Mistral- 7B-Base- v0.2, LLaMA-3--88 3-BBeBase, and Bermma-2-9B-Basee to match surpass some apex models on AlpacaEval 2.0, such as the original GPT-4, LLaMA-3. (Table I.. The estimated cost of creating this 4K dataset using the GPT-4 API is under 600 US dollars.
and Claude Opus
We stress that although reminiscent of prior efforts using synthetic data such UltraChat, pipeline is fully automated with no human design elements (e.g.. choice of topics, lexicon etc.). The only human involvement involves the short prompts used for skill extraction and question generation, which we adapted from the math setting of Didolkar et al. (2024). While our pipeline currently focuses on simple instruction-followine the method seems extensible future safety/alignment, as well as domain-specific Q&A.
2 INSTRUCT-SKILLMMO
This section describes our methodology for extracting skills from powerful LLMs and how to use these extracted skills to create diverse, high quality dataset. simplified version of our pipeline and prompts are depicted in Figures T and on this dataset.
Section 3 reports the evaluation results when finetuning
2.1 SKILL EXTRACTION PROCEDURE
The method involves automated interaction with frontier LLM (GPT-4-Turbo). We ask the frontier LLM to first generate a list of topics that arise in instruction-following For each topic returned by the LLM,, further prompt it to generate list of skills that needed to answer typical queries on that topic. Additionally, we ask the LLM to create list of query types (c.g.. "Information Seeking") that might arise in that topic. See Appendix L.4 for details about the prompts used, and Appendix K.2 for the list of all extracted skills. Since this method relies solely upon the LLM's inner meta- kntta-nnwwedge, this method should extend easily to other types of instruction-followee
An Earlier Attempt: Our initial attempt to extract skills leveraged existing instruction tuning datasets, which is a more direct analog of the method in Didolkar et al. (2024). However, we suspected this to be sub-optimal due to known limitations of past instruction tuning datasets. We therefore designed the method described above, and found superior. It also has scientific benefit of being independent of existing datasets like Alpaca and Ultrachat. However, the dataset from the initial D:
method, called ESTRUCT-SKKILLIXS
2skill lists generated by different frontier models are related but not isomorphic. Skills generated by one model are comprehensible to other models. See Didolkar al. (2024) for such experiments. 'We use GPT-4-Turte Appendix B for results when using Claude 3.5 Sonnet (2024-06-20).
our main experiments (2024-04-09 checkpoint unless specified otherwise). See
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Table 1: Evaluation results of base models supervised-linetunet on INSTRUCT-SKILLMIN
versus the proprietary instruct versions and other proprietary models. For our models, we report the results for best checkpoint selected using held-out queries. For other models("), we report the published numbers available publicly available leaderboards. "# Data" refers to the number of (instruction, response) pairs the training data. See Table 9 for more detailed view, including comparisons to past open datasets.
AlpacaEval2.00 LC WR(
WildBench WB-RRwardee
Model
# Data
LLaMA-3-8B
Ours
42.76
-36.91
"LLaMA-3-8B-Instne
22.90
-46.30
Mistral-7B-v0.2
Ours
4K
36.70
-29.25
SFT on Alpaca-52K
52K
8.64
-80.47
-Mistral-7B--nssruatt-0222
17.10
-54.70
Gemma-2-9B
Ours
2K
36.18
-37.83
Gemma-2-999 2-1-a---2-------
37.21
-28.78
*Other Proprietary Models
LLaMA-3.1 14055..--005--nstttt
39.30
Mistral Large
32.70
-46.40
Claude Opus
40.50
-21.20
Claude Sonnet
34.90
-30.30
GPT-4-Omni (2024-05-13)
57.50
+1.70
GPT-4 (2023-03-14)
35.30
see Appendix A) is still very useful for ablations that pinpoint ways in which our skill-based pipeline improves on past synthetic datasets for instruction-following (see Tables 2 and 5
2.2 DATA GENERATION
Inspired by the recent SKILLMIX evaluation (Yu et al. 2024). we generate Instraction-folloviin examples by randomly picking l skills as well random query type. The frontier L.LM prompted to create Q&A pairs that illustrate these & skills and the query type. We refer to the resulting dataset as INSTRUCT-SKILE data created from random combinations of & 2 skills. See Appendix L.3 and L.5 for the details about the prompts used for data generation.
For example, SSTRRUCT-SKILMMXXKE
refers to 1,000 examples of
See Appendix A for more details, and an estimate of the low cost of INSTRUCT- INSTRUCT-SKILLMIX pipeline.
Where does diversity come from? The first source of diversity is the skill labels. A skill label represents some part of the frontier LLM's meta-knowledge of human behavior and needs, which observed in its vast training set or during instruction tuning. Replacing concrete Q&A example with skill label converts it into pointer to region in the frontier LLM's meta-knowledge, which the model can then freely draw upon to create new examples. The second source of diversity is the use of random k-tuples of skills when generating new examples. The motivation here that, in most cases, distinct tuples will lead to very distinct flavor of examples.
For instance, the skill pair (critical thinking and communication. literature and language skills) leads to the following instruction
I'm. a high school English tescher aiming to deveicp a curriculun unit fer my 1ith-grede class, focusing Amorican litarsture. understarding the texta. Specifically, T'm looking to enhance ny atudenta' critical thinking and commurication skills through engaging activitles Can you suggest detailed ways to incorperate these skills, ideally with concrece examples and expected learning ooteomes?
this unit beyond just reading and
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whereas the skill pair (critical thinking and iommunication,ski.. skill in virtual and system design) leads to
As an TT manager, am overseeing the deve levelopment of virtual workspace to enhance communicatioon ion and effleiency among remote Leams. This wozkepace must support multimedia content, including video conferencing and live document editing. khat the critical steps should take in its design and implemencation, belancing technical robustness with obstacles?
dould you provide apect fic technologias consider and any potential
Even though the two skill pairs share a common skill, they lead to rather distinct Q& pairs, involving creative and nuanced situations with subtle moving parts. Since the number of k-tuples scales as (*). where is the number of skills, using pairs of skills foster lot of diversity e.g.. 125, 000 possibilities with N 500, & = 2. The pipeline in our experiments mainly uses k = 2, but generating answers to these queries will certainly end up using many other unnamed skills well, and thus serve as a rich source for learning how to follow instructions.
3 EXPERIMENTS
3.1 EXPERIMENTAL SETUP
SFT on INSTRUCT-SKILLMIXX,, We finetune LLaMA-3-BB-Base Mistral-7B-Base-Wll Gemma-2-9B- 2-seeBase, LLaMA-2-7B-Bian Base, and LLaMA-2-1338 1AAMA-2-13B-Bas- Base on varying number of examples We train for multiple epochs and 100 held-out questions. Similar to/uuyng et al. (2022);
from INSTRUCT- SKILLMIX-DDC -(k) and select the best checkpoint by performance Zhou al. ),023), we observe that using cross-entropy loss on validation set does not lead to the best checkpoint. See Appendix B.2 for a more detailed discussion of the checkpoint selection procedure. As baseline, we also finetune on different subsets Alpaca-522 (2K,,-5,, including the IK or 5K examples with the longest completions. For further training details (e.g., hyperparameters), Appendix E
Evaluation. We evaluate models popular instruction following benchmarks: AlpacaEval 2.0 (Dubois et al. 2024), MT-Beech (Zheng et al.) 2023), and WildBench (Lin et al., 2024). For AlpacaEval, we report the length-controlled win rate (LC WR) of the responses of our model against a reference response, which corrects for the length bias of the judge model. For MT-Bench, we report the average score of the responses of our model graded by a judge model. For WildBench, we report the WB-Reward (weighted win-rate) of the response of our model against one reference response when graded by judge model. For further evaluation details, see Appendix D See Table [1 in Appendix for evaluations on additional benchmarks.
3.2
MAIN RESULTS
For the main results the paper, we report the evaluation results when models are finetuned on INSTRUCT- PRRRUCT-SKILMMIX in Table []] and summarize our findings below. For more detailed version of Table 1 see Table 9 For additional ablations, see Appendix F For evaluations on other LLM benchmarks, Table
INSTRUCT-SKILLMIX achieves SOTA performance amongst SFT models. LLaMA-3-8B-Bas finetuned 4K examples from INSTRUCTSSKILLMIXEE
yields LC win rate of 42.76% on AlpacaEval 2.0. This score is higher than Claude 3 Opus. LLaMA-3. 1.1.3.1-400 1..11-405B-Insse
and GPT-4 (2023-03-14) 14). Mistral-7B- 7B-Baee-ve v0.2 finetuned on the same data achieves -2,225 on WildBench, which outperforms Claude 3 Sonnet and Mistral Large. Gemma-2-9B-Ban finetuned 2K examples from DISTRUCT-SKIILMIIIAI
gets score of 8.12 on MT-Bench, which better than GPT-3.5-Tuurh (2023-03-01). To best of our knowledge, these scores are higher than any base model that has only undergone supervised instruction finetuning (i.e., no RLHF, DPO, PPO, or variants).
Early saturation. Performance from method rises rapidly, reaching unprecedented levels with 1K examples. Unfortunately, improvements stop already with 4K examples. This turns out to be consequence of its high efficiency inducing good instruction-followinng Specifically, with 4K
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examples, the win-rate against GPT-4 approaches 50% on heldour queries from our pipeline, and thus overfitting sets in.
Observed limitations. The open benchmarks used in this study have known limitations, related to the insufficient number of under-specified or ambiguous queries, and no testing of long-form generations such as multi-page essays. Our current pipeline shares some of these limitations. Fixing this seems very doable via suitable modification to our INSTRUCT- SIILLMIX pipeline, but this is left for future work. This aligns with the observation in Bai al. (2024) that a model's effective generation length seems to be limited by the typical length of examples seen during SFT, and is exacerbated by the relative scarcity of long-form samples in the SFT data. This underscores the critical influence of training data composition model's post-fine-tuning capabilities, and would be interesting to investigate in future work.
ABLATION STUDY
Whereas pretraining is the source of an LLM's basic capabilities (Gudibande et al., 2023), the sole goal of instruction tuning is to impart skills, such as answer-structuring empathy, helpfulness, etc.
Vanilla SFT on Q& data generated by a teacher LLM akin to imitation learning. Our ablation studies below help understand the contribution of different elements to the effectiveness of imitation learning method using improvement is the skill extraction step.
SERUUCCT-SKLLLMM.. Q&A. The main finding is that the source of largest
4.1 BENEFITS OF SKILI. EXTRACTION (WITH MIXING TURNED OFF)
To highlight the benefits of our skill-based method versus current synthetic approaches, we use the pioneering Alpaca dataset, whose responses are rewritten by GPT-4 (2023-03-14) (Peng et al.)
data, where 2023). The fairest comparison here would be with our Alpaca-52K, and each of our datapoints the underlying skills were derived from random sample of uses one of those extracted skills. All results below involve finetuning Mistral-7B-Base-vii different subsets of the Alpaca-52K dataset: (1) Alpaca- Longest: 1,000 examples with the longest responses (Zhao et al. 2024); (2) Alpaca-5K Longest: 5,000 examples with the longest responses; (3)
on
Alpaca-54 5K Random: 5,200 randomly sampled examples from which we extracted our skills; and (4)
Alpaca-52k. 52K: the full 52,002 examples.
Table 2: Evaluation results of 7 finetuned on INSTRUCT-SKILL vs. on Alpaca-52K. Note that skills extracted from Alpaca-5K Random were used to create the INSTRUCT-SKILLMISS datasets.
AlpacaEval 2.0
WildBench
SFT Dataset
Data
LC WR( %
MT-Bench
WB-Reward 90c4
PETRUCT-SKKILMIXX
4K
29.77
7.17
-39,06
IK
27.04
7.22
-46.83
Alpaca- Longest
IK
10.09
6.88
-63.38
Alpaca-5K Longest
5K
8.92
6.90
-62.55
A Apaaca-5K Random
5K
11.10
6.86
-74.41
Alpaca-52K Full
52K
8.64
6.45
-80.47
As shown in Table 2 finetuning on 1,000 examples with the longest completions from Alpaca-52K
yields 10.09% LC win rate on AlpacaEval 2.0. On the other hand, finetuning on only 1K examples of PITRUCT-SKILLMIX
yields 27.04% LC win rate. Note that since the skills in INSTRUCT- SKILI.MIX-DD are mostly derived from Alpaca-52K, the observed improvements in the win rate are indicative of the improved quality of INSTRUCT-SKIL
queries.
4.2 MIXING SKILLS HELPS, BUT NOT AS MUCH AS SKILL EXTRACTION
In Table models finetuned on ASSTRUCT-KILLMME D(k22) data marginally outperform models SFT DSSTRUCTSSKILMII-AA on AlpacaEvval and WildBench, whereas performance

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Published as a conference paper at ICLR 2025
MT-bench is about the same. The marginal improvements from increasing & are less noticeable for INSTRUCTSKILLMAA
Table 3: Evaluation results of Mistral--B-BBas--e SFT on INSTRUCT-SKILLMIS where k=1
vs. k=2. each entry, we report INSTHCTTSKKILLLNKKDDSSSUTTSSSSSS
AlpacaEval 2.0
WildBench
Model
Data
WR(%)
LC WR( %)
MT--encc
WB-Reward
SFT on INSTRICT-SKILLMIX(E2
IK
33.87/42.48
27.48/38.34
6.92/7.33
41.46//30.65
Mistral-7B Baaal-----Baeeeeee -0.2
2K
37.05/40.83
31.57/36.18
7.04/7.20
-33.4//331.9
4K
35.08/40.74
29.77/36.70
7.17/7.16
-39.06/-29.25
SFT INTRUCT-SKILLMIXII
IK
30.06/41.75
27.04/38.34
7.22/7.49
46.8./330.95
Distral-B-Base--ol)
2K
35.07/-
31.66/-
7.39/-
-46.97/-
4K
33.57/-
28.85/-
7.13/-
-44.43/-
4.3 QUALITY OF QUERIES (AND SKILLS) MATTERS
The effectiveness of this approach depends on the quality of the queries used in the fine-tuning process, where high-quality queries enable the frontier LLM teacher to provide richer instruction the student model undergoing instruction tuning. This relationship between the quality of queries and the skills being imparted is supported by two key observations. First, the frontier LLM proves to be a more effective teacher when the skill list being used was also entirely generated using its help (as opposed Across all model types, dataset size, and the
to giving skills derived from existing datasets) [ evaluation benchmark, we generally see an improvement when finetuning on INSTRUCT-SKILLMMI compared to
(see Table more details). Second, incorporating these sub-optimal skills from existing datasets part of "teaching" (e.g.. with INSTRUCT-SKILLMM still more effective than using an equal number of random (or even the longest) examples from Alpaca-52K when responses quality of the queries (and the skills used to create those queries) drives how well data generated by the frontier LLM is able impart its skills on the model undergoing instruction tuning.
also by the same frontier LLM. These findings suggest that the
4.4 EFFECT OF TEACHER AND GRADER
SFT performance derives from the model used to generate Q&A data, which plays the teacher role in imitation learning. The student's performance is evaluated by the grader model. The main results reported in this paper used GPT-4-Turbo as the teacher, and some checkpoint of GPT-4 or GPT-4-Turbo as the grader.
Effeet of the teacher Many SFT efforts in 2023 used earlier versions of GPT-4 or GPT-3.5, which were weaker than GPT-4-Turbo. To pin-point the effect of this change, we try doing a head-to-head comparison once we fix the teacher. The responses in A KK Longest (2023-03-14), whereas INSTRUCT-SKILLMIN data is generated by GPT-4-Turbo. Thus, we use GPT-4-Turbo regenerate answers to Alpaca-1K 1K Longest (Zhao al. 2024 and we also use GPT-4 (2023-03-14) to regenerate DNSTRUCT-SKILLME
written by GPT-4
Table compares the performance of Mistral-7B Barrall-B-BBae-ee- Base-v0.2 when finetuned on the two datasets using the two versions of GPT-4. For each fixed data generator model, the INSTRUCT-SS SERRUCT-SKILLMIXX dataset leads better performance. Furthermore, replacing GPT-4 with the stronger GPT-4-Turbo in data generation makes INSTRUCT-SKILA highlights that our pipeline better positioned than Alpaca dataset to elicit better supervision from a more powerful LLM teacher.
pull even further ahead of Alpaca- 1K Longest, which
*e also observed improved performance when the teacher model generated data based its own of skills, rather than using skills extracted by different teacher model, further highlighting the advantages of leveraging the teacher model's metacognitive capabilities during dataset creation (see Appendix (B3).
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Table 4: Evaluation results Mistral-7B-Base-ee finetuned INSTRUCT-SKILLXI- vs. Alpaca-1K Longest generated from two different versions of GPT-4. For a fixed data generator model, SFT Mistral-7B- V0.2 on INSTRUCT-SKLLL SKILLMIX outperforms SFT on Alpaca- IK Longest.
AlpacaEval 2.0
Model for Data Generation
Dataset
WR( %)
LC WR(%)
MT-Bencch
Alpaca- IK Longest
12.75
10.09
6.83
GPT-4 (2023-054))))
DSTRUUC-SKKILLINS
13.29
15.01
7.10
Alpaca-1 IK Longest
35.23
19.62
6.99
GPT-4-Turbo (2024-04-09)
DOSTRUCT-SKILLMI-DEE
33.87
27.48
6.92
Effeet of choice of grader We use GPT-4-Turbo to generate data and AlpacaEval 2.0 uses GPT-4 for grading, creating a scenario where both the teacher model and grader model are from the same family. This raises the question of whether model family overlap leads and inflated scores. To quantify this effect, we used Claude 3 Opus as the grader for AlpacaEval 2.0. Table 5 shows that although Claude is more generous grader across the board, generally preserves the relative rankings among the models. Importantly, it exhibits even stronger preference for our student models' generations than does GPT-4.
potential grading bias
Table 5: Evaluation results when using two different graders for AlpacaEval 2.0. Relative ranking of evaluated models generally preserved when using different graders. Here, ISM-D refers to INSTRUCT- SKILLMIX-D.
Grader: GPT-4 (2023-11-06)
Grader: Claude Opus
Model
WR( %)
LC WCRRS
WR %)
LC WR( %)
Mistral-7B Baraal--B-Basssee -B-- SFT on ISM-D-IK
33.87
27.48
50,56
38,50
BB-ael-v-- SFT on ISM-D-2K
37.05
31.57
48.94
38.29
Missral-7B-Basc-eee SFT on ISM-D-4K
35.08
29.77
52.55
44.16
(Reference Model) 1-5MA-3-70B--mme Instruct
33.20
34.40
39.68
42.33
(Reference Model) Mistral-7B: Cestral-7B-Instruue 7B-IInstruct-0..
14.70
17.10
15.16
18.89
(Reference Model) LLaMA-2-70B-CCHS
13.90
14.70
16.67
17.85
5 EFFECT OF Low QUALITY DATA
Our fully synthetic pipeline produces a large number of high-quality questions and answers that look impressive but also (for want of a better word) "robotic." Data sourced from human workers shows greater variation, and one begins to wonder if that additional diversity could be beneficial. We tried interventions such generating 20% using different prompt e.g.. require shorter answer, a poor quality answer. In human pipeline, this variation would be expected. We can think of this "data from shirkers," and one would expect a fair bit of in naive crowdsourcing, (In corporate settings it would be mitigated via quality control measures.) See Appendix ! for an example of a poor quality response.
We replace 20% of the responses in INSTRUCT-SKING SKILECT--SKILLLMX(22--- with short responses ("respond in one paragraph") to create BREV-INSTRUCT- BRAVWNNNTRRUCT-KKKLLIKXX->>>>>> Finetuning Mistral-7B -B---Be--.2 on BREV-INSTRUCT. MT- D was surprising: brevity constraint on just 20% of data almost halved the average response length on AlpacaEval, from 2817 to 1746 characters. LC win rate dropped from 31.57% to 23.93%.
alternatively replace 20% of the responses in the same datasets with responses that are still long but have poor quality (i.e., deliberately sloppy and unhelpful) to create JUNK-INSTRUCT. SKILLMIx(k=2)-25 Mistral-7B-Base -ase--00-- finetuned on the SEEN--SSSTUTTTSILLLNN less than 1% win rate AlpacaEval and 5.01 on MT-Bench.
Lower-quality data harms performance. As shown in Table 6 replacing just 20% of the data with poor quality responses harms performance. For INSTRUCT-SKILLMISS the harm is super- proportionate. These observation may help explain why creating open-domain instruction tuning data has proved difficult via naive crowd-sourcing.
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Table 6: Evaluation results of models finetuned on low quality INSTRUCT-SKILLMIX Replacing
just 20% of the dataset with low quality data has prper-rrppottoonae harm on the model performance. Amount of harm greatly differs between the two versions of the pipeline.
AlpacaEval 2.0
WildBench
Model
Data
LC WR(%) %)
Avg Len
MT-Beech
WB-Reward 2064
SET INSTRUCT-SKILL.MI D(k=2)
2K
31.57
2817
7.04
-43.46
Misss
2K (Brevity 20%)
23.93
1746
6.69
-49.85
2K (Junk 20%)
0.77
1104
5.01
-47.50
SFT on INSTRUCI-SKILLMIIIEE
2K
36.18
2936
7.20
-31.92
Mistral-7B- 2Baal-7B-Base-v0.2
2K (Brevity 20%)
31.61
2336
7.32
-32.27
2K (Junk 20%)
24.60
2435
6.90
-47.50
High-quality data's protective effect. While adding some low-quality data to INSTRUCT- SKILLMIX already causes noticeable performance drop, doing the same to INSTRUCT-SKILLII-- is catastrophic. This suggests that
is more robust to "shirkers," " corroborating our previous observations in Table of the superior performance of INSTRUCT-SKILLMIT over INSTRUCT-SKILLMIX SKILLMIX-. This finding suggests that higher quality data can somewhat protect against negative effects of "shirkers," which needs further study.
RELATED WORK
Prior works observe improvements from instruction finetuning on fewer, but higher quality data generated by humans (Zhou et al. 2023; Touvron et al. (023). However, efforts to curate high quality data from humans are quite expensive, and licensing can become complicated. This has led to an increase in the popularity of semi-automated and less expensive approaches.
Selecting high quality data. Synthetic data creation has become a predominant approach for curating instruction tuning datasets, especially in the academie realm (Wang et al. 2023b Dubois
et al. 2023 Xu et al. 2024 Gunasekar et al., 2023). These synthetic datasets are generally created by providing in-context examples to powerful LLM to produce the synthetic data, followed by some post-filtering (Wang et al., 2023b). Recent efforts have also focused on data selection strategies for high quality subsets of the original dataset, which lead performance gains (Tunstall al.)
2023: Chen et al. 2024: Liu et al.) 2024 Zhao et al.) (024). Notably, Zhao et al. (2024) show that finetuning on just the IK longest completions from Alpaca-52K outperforms finetuning on the entire Alpaca-52K dataser. Whereas the data selection methods just described focus on general-purpe purpppss instruction tuning, Xia et al. (202)) explore an optimizer-aware data selection strategy for targeted instruction tuning.
Encouraging data diversity. Common approaches to elicit diversity in datasets include mixing multiple datasets (Wang al. 2022 Longpre al. 2023; Wang et al. 2023a), well rewriting the data in multiple ways and changing formatting (Allen-Zhu and Li [024]]
2024)/Hoooo Honovich et al. 2023). The
Self-Instruct framework (Wang al. 2023b) and variants such Alpaca-522 52K (Dubois al. 2023
encourage diversity by identifying similar pairs using ROUGE-L similarity. Other approaches hes to
ensure diversity impose constraints on the topic order enhance wide coverage (Ding et al. 2023
Xu et al.) 2024),, require synthetic data to use random subset of words or concepts chosen from some vocabulary (Eldan and Li 2023] Gunasekar al. 2023 Li et al. 2024). The latter approach is also suggested by recent work that provides mathematical model for emergence via LLM scaling (Arora and Goyal 2023) and used
evaluation setting Yu al. (2024).
Alpa AlpacaEval (Li al. 2023 Dubois al. 2024) a popular evaluation for assessing cnatruction-follovvin capabilities instructions, and its answers compared against reference outputs of designated baseline model. For each instruction, another evaluator LLM outputs a preference between the two responses (output
LLMs. The tested model provides answers 805 carefully curated

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Published as a conference paper at ICLR 2025
of the model being evaluated vs. reference output by the baseline mode). The primary evaluation metric is the win rate, which represents the expected probability that the grader model favors the response generated by the evaluated model over the response produced by the baseline model. Given that a raw win rate shows bias towards longer responses, AlpacaEval 2.0 (Dubois et al.] (024) introduces the length-correctdd (I.C) win rate as a proxy for what the raw win rate would be if the evaluated model's 's response lengths and baseline model's 's response lengths matched.
WildBench. WildBench (Lin et al. 2024) is another benchmark for assessing the instruction follow- ing capabilitties of LLMs. Unlike the AlpacaEval instructions, 50% of which are only "information seeking" type questions, the instructions for WildBench cover a more diverse distribution of task categories, including coding and creative writing. Whereas the grading in AlpacaEval is more liberal (since there is no penalty for poor responses), the grading in WildBench is more finegrained: a model answer is compared against reference answer, but is graded on a scale of (1) win by big margin, (2) win by a small margin, (3) tie, (4) lose by a small margin, and (5) lose by a big margin. This ensures that models that output bad answers to certain types of questions are penalized.
RL-inspired approaches. Since we do not use RL, we defer discussion of these approaches to Appendix G
CONCLUSION
While one would have certainly expected the cost factor as well as scaling ability to ultimately favor synthetic data, the surprising finding in this paper is that, when done well, synthetic data can be much more effective than human data for instruction tuning. Our ISTRUCT-SKILLMII pipeline, uses the recent discovery of LLM Metacognition (Didolkar et al. 2024) to extract skills using a powerful LLM and then leverages an LLM to create quality instruction data using random pairs of those skills.
Vanilla SFT of base models just IK to 4K examples from our pipeline outperforms the proprictary instruct versions of the same model, as well as older and larger instruction tuning efforts like Vicuna and Ultrachat that used orders of magnitude more datapoints. The performance also approaches those of frontier models, which trained on expensive human data as well as with RL techniques. Unfortunately, our method saturates at 4K examples, when win-rate heldout queries approaches 50%.
Ablation studies in Section 4.4 rule out potential confounding factors, such the use of strong teacher, or bias due to teacher and grader belonging to the same family. These ablations reinforce that the improvement primarily due the uniformly high quality of examples produced by our skill-based pipeline. Each example contains a query with nontrivial scenarios and lots of moving parts, which improve imitation learning.
Section 5 offers a preliminary exploration of pitfalls of naive collection of instruction tuning data. In particular, the presence of some lower quality data noticeably harms the model's 's performance. This insight should be more rigorously investigated, including via new theory. The experiment also suggests that our less preferred ASTRUCT-SKKILMME skills from an existing dataset) is more susceptible to such bad data than our preferred INSTRUCT- SKILLMIX.
method (which involves extracting
One potential benefit of INSTRUCT-SKILLMIXX may be that it gives some insights into an efficient method for dataset distillation (Wang al. 2020) for text datasets, which has not yet proved possible.
Finally, it should be noted that our results look stronger paper than they actually are. Open evaluations such as AlpacaEval 2.0 have blind spots, especially the fact that win rate of even 50% against a frontier model still allows unacceptably high frequency of unsuitable responses in deployment setting. The new WildBench evaluation does test for more corner cases. We hope that ASSTRUCT-SKILLMII ideas also leverage LLM metacognition to create better evaluation.
Although our SFT data does not address safety and alignment, our skill-based ideas may be useful there. A related next step would be leverage ideas of skill extraction to improve RL-based methods (whether for instruction folllooinnnoooowiingg or alignment). We hope to address these in future work.
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8 REPRODUCIBILITY STATEMENT
We provide the full lists of extracted skills, topics, and query types in Appendix K of prompts used to generate the data from these lists in Appendix L.3 and L.3
We provide the We provide the set of training hyperparameters in Appendix E.I We discuss the details of the checkpoint selection
method in Appendix E.2 We provide the details of evaluation settings in Appendix D
ACKNOWLEDGEMENTS
SK, SP, and SA acknowledge funding from NSF, DARPA, ONR, and OpenAl.

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Lianmin Zheng, Wei-Lin Chiang. Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin. Zhuohan Li, Dacheng Li, Eric P. Xing, Hao Zhang, Joseph E. Gonzalez, and lon Stoica. Judging llm-as am--as-s -a--s----judee with mt-bench and chatbot arena. CoRR, abs/2306 05685, 2023. doi: 10. 2.5S0/ARXIVV2306..0
WwR.httss: // www.w/..r.....00 48550/ rxiv. 2306 23686.56886555 05685
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Published as a conference paper at ICLR 2025
Chunting Zhou, Pengfei Liu, Puxin Xu, Srinivasan Iyer, Jiao Sun, Yuning Mao, Xuezhe Ma, Avia Efrat, Ping Yu, Lili Yu, Susan Zhang, Gargi Ghosh, Mike Lewis, Luke Zettlemoyer, and Omer Levy. LIMA: less is more for alignment. In Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurlPS 2023, New Orleans, LA, USA, December 10 16. 2023, 2023, URL http:: ccpppppppprpp..cpppppp.r.. files files/papalleeee 26622774822944771e422277773333 - Coarracc-coffeeenee.htiti
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List of Appendices
INSTRUCT-SKILLMIX Pipeline (More Details) INSTRUCT-SKILLMIX with Different Teacher Model
18
................................
19
Full Evaluation Results (More Detailed)
20
Evaluation Details
23
E
Training Details
24
Ablations
26
G
INSTRUCT- SKTTT-IIIMMMM. is Competitive with RI RI-Innpired Methods
29
Robustness of MIX Across Random Skill Combinations for SFT
30
1
Examples of BREV.INSTRUCTSKILLMMI and JUNK IN INSTRUCT-SKILLMIX
31
Stats on Different Datasets
32
K List of Skills
33
Skill Extraction Prompts
129
M
Comparison of Responses
137
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Published as a conference paper at ICLR 2025
A INSTRUCT-SKILLMIN PIPELINE (MORE DETAILS)
A.1
AND INSTRUCT-SKILLMER PIPELINES
Method 1: Leveraging existing instruction datasets. Even though existing instruction-folloiing datasets may not induce good chat capability via vanilla SFT, these datasets still exhibit (possibly in an uneven fashion) some "skills" needed by the model. Thus, we adapt the methodology presented in Didolkar et al. (2024) and use GPT-4-Turbo to extract instruction-following skills from random samples of existing instruction and alignment datasets (5,200 samples from Alpaca-52K and 1,000 samples from UltraChat). We then GPT-4-Turbo to cluster similar skills into broader categories. forming our final list of instruction-followwii skills. See Appendix K.] for the list of all extracted skills and Appendix and L.2 for details about the prompts used for skill extraction.
Method 2: Directly prompting a powerful LLM. While Method works surprisingly well, generated unease about possibly relying on existing seed datasets of uneven quality, and thus potentially inheriting their limitations and biases. Therefore we also tried an alternative pipeline that solely relies on the powerful LLM's ideas about list of skills leverages for instruction instruction-tuning.
We will refer to the datasets generated from the seed-dataset dependent and the seed-dataset agnostic versions as INSTRUCT-SKE SKILLMIX-SEEDD DATASET- DEPENDENT and INSTRUCT- ASTRUCT-SKILLMME MIIE-SSIILMM---SSS DATASET-AGNOSTIC respectively. Unless stated otherwise, INSTRUCT-SKILLNN refers to the INSTRUCT-SKILLMIX BOBED-DATASET-GGNON data.
Extract Label Skills
Cluster Skills
Synthetic (combine k skills)
(Instruction Response 1)
Instruction 2, Response 2)
(Instruction, Response)
(Instrcction N, Response M)
Skil A1
Step 1: Use GPT-4 to label each (nstruction, resporse) pair with skill
Step 2: Use GPT-4 to cluster similar fine-grained skill into broader skill
Step Create synthetic (instruction, response) pair that requires applying provided
(a) Top row: INSTRUCT- DERRRUCC-KKILLMX-DD pipeline (short for INSTRUCT- MINERUCCTSSKILLIX---- DEPENDENT).
Topics Query Types
Extrapolate
Create Synthetic Data (combine k skills type)
Topic
Query Types
(Instruction, Response)
Topics
Tapic N
Step 1: Use GPT-4 4 to extract list of query types prompting.
From topics, extrapolate
Step synthetic. (instruction, response) pair of desired type that requires applying provided skills.
relevant skills.
(b) Bottom INSTRUCT-SKILLNN pipeline
Figure 2: Two variants of the INSTRUCT-SKILLMIX pipeline. INSTRUCT-SKILLMIXXE involves two steps: (1) skill extraction using similar ideas as Didolkar et al. 2024); (2) data generation from random k-tuples of skills.
A.2 DATASET CURATION COSTS
Generating synthetic data using the INSTRUCT-SKILLNNG pipeline is more cost effective compared to using human annotators. To extract the skill clusters for INSTRUCT-SKILLMIX- than $120 to extract and cluster skills from 6,2000 examples from various existing datasets. For INSTRUCT- -ECTT--IMMMI,, extracting skills via direct prompting costs under $5. Additionally, producing 4,000 examples of INSTRUCT- SILLUCCTSSKLLLLEE SIIISSKIILLLEII MIX(k=2) data costs under $570.
it costs less
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B INSTRUCT-SKILMIX WITH A DIFFERENT TEACHER MODEL
We apply INSTRUCT-SS -KIUIT- with Claude-3.. 3.3-Sonnet (2024-06-20) as the teacher model and replicate some of the experiments from the main paper. See Tables 7 for the results. We report the results for best checkpoint selected using held-out queries.
B.1 ASSTUCCTSKILLMAR IS APPLICABLE WITH ANY STRONG TEACHER MODEL
We observe that Claude-3.5- is also able to generate a meaningful list query types, topics, and fine-grained skills. See Appendix K.3 for the full list. When compared to the list generated by GPT-4-Turbo (Appendix K.2). we see that Claude-3.5-Sonne generates a very similar list of query types (e.g., "Information-seeele
and THelp-seeking are the first two entries generated from both models), but the description and example queries from each query type are more terse. On the other hand, the topics and skills generated by Claude-3.5-Sonne: more fine-graine tne-ggained and specific than those of GPT-4-Turbo.
-laude-3.5-Somee is also able generate (instruction, response) pairs from randomly selected pair of skills and a random choice of query type. Upon manual inspection, we observe that the data generated by Claude-3.--Sonntt is slightly less illustrative than the INSTRUCT-SKILLMIX data generated by --rrbo.
B.2 BETRRUCTSKKILMME OUTPERFORMS OTHER METHODS
Once we fix the teacher model as Shaude-3.5-Somee the conclusion remains the same from the main paper: INSTRUCT-SKILLMEE outperforms regenerating responses to existing datasets. See Table 7
Table 7: Evaluation results AlpacaEval 2.0 and MT-Bench. "# Data" refers to the number of (instruction, response) pairs in the training data.
AlpacaEval 2.0
Dataset
# Data
WR(% %
LC WR %)
MT--enchh
SFT Mistral-7B-Base--he data generated by
(2024-06-20)
INSTRUCT-SKILLLMIX(k))
IK
25.74
25.54
6.88
Alpaca-52K
Long 1K
22.10
19.12
7.13
ShareGPT
Random IK
21.00
19.77
7.06
B.3 EXTRACTING SKILLS WITH ONE TEACHER AND GENERATING WITH ANOTHER
We ask Claude-3.5-3 3.5-oonett generate INSTRUCT-SKILLMIXX-2)
from the query types and skills generated by GPT-4-Turbo, and vice versa. For any fixed choice of teacher model, performance slightly better when generates INSTRUCT-SKILLMIIXC- data from the query types and skills it extracted. See T
Table 8: Evaluation results AlpacaEval 2.0 and MT-Bench.
AlpacaEval 2.0
Data Generated by
Skills From
WR(%)
LC WR(%)
MT-Bench
SFT
on ISSTRUCTSKILLLIXXI->2...
GPT-4-Turbo
41.97
38.48
7,33
GPT-4-Turbo
Claude 3.5 Sonnett
43.22
31.98
7.20
GPT-4-Turbo
21.32
23.91
6.87
Claude-3.5-Somee
Claude-3.5-Sonnet
25.74
25.54
6.88
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Published as a conference paper at ICLR 2025
C FULL EVALUATION RESULTS (MORE DETAILED)
Tables 9 T0 contain the full evaluation results on instruction following benchmarks, including the ones in Table
Table II contains the full evaluation results on other popular LLM benchmarks.
For our models, we report the results for best checkpoint selected using held-out queries. For other m*)els(*), we report the published numbers available on publicly available leaderboards.
Table Evaluation results on AlpacaEval 2.0, MT-Bench, and WildBench. "# Data" refers to the number of (instruction, response) pairs in the training data. In each relevant entry, we report AISRIITT-KKILLMXXXX///ITTTTTTTTT
Alaac 2.0
WildBench WB-Reward
Model
Data
WR( %
LC WR( %)
MTT-Bench
SFT INSTRUCT-SKIIMMIX(-2))
1K
27.83/27.48 4.8
23.41/27.83
6.85/7.15
-48.58/41.46
L.LMA-3-8B-Base
2K
31.19/35.73
29.16/36.51
6.85/7.18
-45.70/42.55
4K
30.05/44.63
28.59/42.76
7.05/7.09
-51.76/-36.91
IK
33.87/42.48
27.48/38.34
6.92/7.33
-11.66/33..5
Mistral-7B-B Histral-7B-Base-v0.2
2K
37.05/40.83
31.57/36.18
7.04/7.20
43.46/-31.92
4K
35.08/40.74
29.77136.70
7.17/7.16
-39.06/-29.25
IK
31,36/36.80
34.80/39.58
7.81/7.99
-53.171-37.16
Cemma-2-99BBBl
2K
34,28/39.30
42.09/36.18
7.80/8.12
-52.05/37.88
4K
33.64/37.97
35.87/40.05
7,88/7.69
-56.05/-38223
IK
8.94/14.00
10.20/13.81
4.38/4.59
-77.981-72.36
LLaMA-2-7B-Bars
2K
7.24/14.95
10.75/15.76
4.44/4.67
100.71/755.15
4K
6.90/12.50
9.63/13.94
4.50/4.31
-81.121-76.27
IK
17.34/22.54
18.06/22.69
6.40/6.71
-64.421-55.22
LLaMA-2-13B- Basc
2K
16.95/19.67
17.76/22.75
6.29/6.73
-67.581-5844
4K
15.79/20.70
17.08/23.05
6.44/6.29
-69481162.55
SFT on INSTRUCT-SKILLMIX(-]]]
1K
30.06/41.75
27.04/38.34
7,22/1.99
-6.8.3/-30.95
Mistral-7B-Base-vO..
2K
35.07/-
31.66/-
7.39/-
-46.97/
4K
33.57/-
28.85/-
7.13/-
-44.43/-
SFT Mistral-7B- Other Datasets (response generated by GPT-4 (2023-03-14)
Long IK
12.75
10.09
6.88
-63.38
Long 5K
13.01
8.92
6.90
-62.55
Alpaca-52K
Random 5K
8.70
11.10
6.86
-74.41
Full 52K
7.47
8.64
6.45
-80.47
SFT Nistral-78--Bas--O22. Other Datasets (response generated by GPT-4-Turro (2024-04-09)
Long 35.23
19.62
6.99
-43.26
Alpaca-52K
Random IK
20.85
23.48
6.93
-55.42
ShareGPT
Random IK
30.06
26.01
7.19
Ultrachat
Random
37.10
25.64
7.39
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Published as a conference paper at ICLR 2025
Table 10: Evaluation results on AlpacaEval 2.0, MT-BBench, and WildBench (continued). '#
Data" refers to the number of (instruction, response) pairs in the training data.
AlpacaFval
WildBench
Model
# Data
WR(%) LC WR( %
MT-Bench
WB-Reward
*Existing Models (not trained by us)
LaMMA-311400SBmmmiiii
39.10
39.30
Mistral Large
32.70
-6.440
Claude 3 Opus
29.10
40.50
-21.20
Claude 3 Sonnet
25.60
34.90
-30.30
GPT-4-Omai (2024-05-13)
51.30
57.50
+1.70
GPT-4 (2023-03-14)
22.10
35.30
8.96
LLaMA-2- -2-708 Chat
13.90
14.70
6.86
53.40
UltraLM 13B V2.0
1.5M
7.50
9.90
Vicuna 13B v1.5
> 1M
7.00
11.70
6.57
LLaMA-3-8B-nnste
22.60
22.90
46.30
Mistral-77 Bistral-7B-Instie 7B-Instruct-vO.2
17.10
7.60
-54.70
Gemma-2-4 2-9B IB-Instruct
21.99
37.21
-28.78
Zephyr 7B Beta
11.00
13.20
Claude 2.0
17.20
28.20
8.06
Gemini Pro
18.20
24.40
GPT-3.5-Turho (06/13)
14.10
22.70
8.39
GPT-4 (2023-06-13)
15.80
30.20
9.18
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Published as a conference paper at ICLR 2025
Table 11: Evaluation results on MMLU, TruthfulQA, GSM8K, ARC Challenge, Winogrande, PIQA.
Model
MMLU
TYQA
GSM
ARC-C
Winogrande
PIQA
LLaMA-3-BB Models
INSTRICT--KIILLIXX----
62.09
34.88
52.54
53.92
74.51
79.76
INSTRICT-SKILLMIN,
62.09
37.33
52.77
53.75
75.06
79.54
PSTRUCTTSSKILLMI--D--
62.28
32.19
50.42
52.73
73.09
79.22
INSTRUCT-SKILLMIX-IB
62.33
37.09
51.25
52.39
74.19
79.92
INSTRUCT-SKIIN SINCCT-SKILMMI-222
62.18
35.25
52.39
52.39
74.66
79.05
INSTRUCT-SKILLMIXX
61.72
34.15
51.10
52.22
73.72
79.27
1-MMA-3-BB-Insruse
63.84
36.23
76.12
52.99
72.06
78.62
LLaMA-3-8B-BA. Basc
62.06
27.05
49,96
50.43
72.85
79.71
Mistral 7B v0.2 Models
INSTRUCT- SEILLCT-SKILMII--D-IEI
58.97
26.19
36.01
$1.02
73.64
81,18
ESTRUCT-SKILLMING
58.67
25.95
36.32
73.56
81.01
58.38
26.68
36.54
50.00
73.56
81,45
INSTRUCT- SKILLMIX- IK
INSTRUCT-SKILLMIX
59.24
27.05
35.10
52.47
73,48
81.23
PERRUCT-SKILMIXS
58.90
25.83
33.66
52.99
73,88
81.66
PIRBUCCTSSKILMIX--AA
58.49
26.68
31.77
52.13
73.72
81.12
INSTRUCT-SKKILLMMI. MIX(LL.
59.02
26.56
34.27
72.77
81.07
)-2K
58.90
25.83
33.66
52.99
73.88
81.66
ASTRUUCT-SKIIAAMIX-DD---
58.94
26.56
33.97
51.11
73.56
81,45
INSTRUCT-SKILLMIXX(--]>
59.07
26.44
35.86
51.71
74.11
81.45
Alpaca- Longest
58.72
27.29
35.18
72.93
81.01
Baatral-B--Intrutt-vve Mistral-7B: B-Bssc-v00.
58.70
52.51
43.67
54.35
72.38
80.41
58.59
28.27
37.98
48.81
71.67
80.30
Gemma-2 2-9B Models
INSTRUCT- SIIICT-SKIMMIXX MEXLLMIX---III D-IK
69.16
30.60
70.96
62.54
74.74
81.23
INSTRUCT-S MIIIESSSSILLI-I---IIIIIII
69.26
30.72
70.81
63.23
74.59
81.28
MSSTRUCT-SKIILLMIX-DDAA
69.39
30.11
71.72
63.14
74.66
81.66
INSTRUCT-SKILLMIN
69.49
31.21
70.74
62.80
73.95
81.83
INSTRUCT-SKILLMIX SKILLMIX
INSTRUCT-
69.64
32.56
71.04
63.82
74.59
81.66
ESTRUCT-SKKLLMIXY
69.36
31.58
71.27
63.74
74.27
81.72
Gemma-2 2-9trunst
71.61
42,96
79.08
63.40
76.32
81.18
Gemma-22 2-9B-Base
68.58
30.11
67.10
74.11
81.45
LLaMA-2-7B Models
INSTRICT-SKILL MIX-DDIK
41.04
34.39
11.83
46.93
70.01
78.07
INSTRUCT- SKILLLI-IIIIII---I -D2K
41.84
31.21
17.51
47.10
69.53
78.45
D
43.00
30.84
15.24
47.01
69.38
78.24
ISSRUCT-SKKILMIA
41.45
34.39
14.78
48.38
78.35
ESTRUCT-SKILLME
43.17
33.41
15.92
47.78
70.01
78.51
ESTRUCT-SKILLMIXS
42.56
32.80
14.63
47.70
68.67
78.02
LLaMA-2-7B 7h-Catt
46.39
30.35
21.76
43.86
66.69
76.44
LLaMA-2-7B-Base
40.76
25.21
12.36
43.52
69.46
77.97
1LMMA-2-13B Models
INSTRICT-SRILLII- SKILLMIX---IK
51.25
30.72
28.51
$1.02
72.38
79.16
INSTRUCT- SKILLMIX- D-2K
51.03
30.84
28.73
50.85
72,30
79.43
INSTRUCT- INSTRUCT-SKILLMIX D-4K
51.05
29.50
28.58
51.19
71.82
80.03
IETRUCT-SKILLIE IK
50.68
30.11
27.45
50.60
72.61
79.92
INSTRICT-SKIILMMXX-2
51.67
30.35
29.19
50.17
72.06
79.98
INSTRICT-SKILLMIIIC
51.47
30.60
30.86
50.94
71.67
80.41
LLaMA-2-13B-Char
53.25
27.91
34.80
46.42
71.03
77.69
LLaMA-2-13B-BA..
50.48
25.70
22.74
48.81
72.06
79.27
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Published as a conference paper at ICLR 2025
D EVALUATION DETAILS
To evaluate our models on the AlpacaEval 2.0, we followed the instructions in https: / com/tatsu 1ab/ aaspaca_evaa__ (Dubois al. 2024). The reference model and judge model are both GPT-4-Turbo (2023-11-06).
To evaluate models on MT-Bench, we followed the instructions in https://github. com/
Lm-sys FastChat (Zheng et al. 2023). The reference model and judge model are both GPT-4 (2023-06-13)
To evaluate our models on WildBench, we followed the instructions in httpt:t/ggthhutt com/ allemai/WildHene (Lin al. 2024). The reference model and judge model are both GPT-4- Turbo (2024-04-09), and we used length penalty (K oo). This corresponds -e--dward in their notation.
For other LLM benchmarks, we followed the default configuration for the evaluation scripts in https: /ttp:/gittutl cwmccoomeeeeeeeeeeeeee www.Eeeeeeeeeenneeeeeeeeee bat i con-aaresss (ao et al. 2023 We
report the exact-match accuracy for GSM8K and the MC1 score for TruthfulQA.
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Published as a conference paper at ICLR 2025
E TRAINING DETAILS
E.I HYPERPARAMETERS
In Table 12 we include the hyperparameters use in our experiments. We finetune each model using the Adam W optimizer. For every run, we use learning rate schedule with a linear warmup of 0.03 and cosine decay to zero. For all experiments, we finetune for 15 epochs and store the checkpoint after each epoch, with the exception of the full Alpaca-52K dataset on which we only finetune for epochs.
We use the torchtune package (torchtune maintainers and contributors 2024) to train all models, except for the Gemma models, which were trained with the MAmmo TH package (Yue et al. (023). Note that the default hyperparameters not specified here might be different in each of the packages.
Training 7B model on 15 epochs of 1000 examples from SETTRUCT-SKILLIX takes approximately 15 minutes on 4 H100 GPUs via PyTorch FSDP (Zhao et al. 2023).
In total, 120 hours of H100 GPU were used for training models reported in this paper, and an additional 1200 hours were spent preliminary experiments.
Table 12: Hyperparameters used for SFT.
Model
LR
Batch Size
-LaMA-3-8B-Ban
2e-5
64, 128
Mistral-7B Sastral-7B-Baseee
Sa-Base-vO...
2e-6
64
Gemma-2-999
le-6
64
2e-5
64
ILLMA-2-155 2-1B------------- --s-e
2e-5
64
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Published as a conference paper at ICLR 2025
E.2 CHECKPOINT SELECTION
As discussed in prior works (Ouyang et al., 2022 Xia et al. 2024 Zhou et al., 2023). minimizing validation loss does not always correspond improved generation quality. Thus, select check- points based on generation quality on held-out data, as used in some prior work (Zhou et al. (023). In particular, we use length-controlled win rate on held-out the selection metric.
We randomly choose 100 held-out examples from our dataset. After each epoch, we generate responses to the held-out instructions using the model checkpoint. We then calculate the win of these responses against the reference outputs generated by GPT-4-Turbe (using the same grader as AlpacaEval 2.0). We select the checkpoint with the highest ength-controlled win rate (LC WR) on this held-out evaluation.
Since the held-out dataset contains only 100 examples, the costs associated with evaluating win rates on the held-out dataset are relatively low. Across all 15 epochs, the total number of API calls made is just under twice the number needed to evaluate the selected checkpoint on 805 AlpacaEval examples.
In Table 3 we report the LC WR and WR checkpoints when training Mistral-7B-Base-Vee on INSTRUCT-SKILLMIX -D.K.
validation dataset and AlpacaEvvl 2.0 for all 15
We select the checkpoint corresponding to epoch 11, since this has the highest LC WR on the held-out data. Note that (1) the corresponding LC WR on AlpacaEval (29.77%) is fairly close to the best LC WR (30.84%); and, (2) the corresponding WR AlpacaEval (35.08%) the best WR.
additionally report the cross-entropy loss each model checkpoint on our held-out data. Similar to Zhao al. (024). loss on validation task (i.e., epoch 2) leads to suboptimal downstream performance. The LC WR on AlpacaEval 2.0 is only 16.5%, which is significantly lower than 29.77%, when select the checkpoint with our validation task.
notice that selecting the checkpoint that minimizes the cross-entropy
Table 13: Checkpoint selection. We SFT Baatral-B--Bse---e on INSTRUCT-SKILMMI--D and evaluate the performance on held-out data. We select the checkpoint with the hest L.C WR held-out data (in this case, epach 11). Entries in boldface represent the hest performing epoch for that metric.
Epoch
2
3
4
5
6
7
8
9
10
11
12
13
14
15
On Held-Out INSTRUCT-SKILLNN D Data
LC WR(%)
20.7
20.4
27.8
28.2
37.0
35.2
45.5
44.1
45.6
39.5
52.8
42.8
45.6
38.5
44.1
WR(%)
34.1
42.8
63.1
61.8
69.7
69.8
75.3
76.2
76.2
71.7
82.3
74.4
73.1
70.6
74.0
CE Loss
1.21
1.18
1.19
1.23
1.30
1.43
1.61
1.78
1.97
2.11
2.19
2.23
2.24
2.24
2.24
On AlpacaEval
2.0
LC WR(%)
14.8
16.5
22.9
26.2
28.2
28.4
29.7
30.
29.9
28.8
29.8
28.1
29.4
30.4
30.8
WR(%)
17.3
19.2
27.1
30.9
33.2
32.4
34.4
35.6
34.6
33.7
35.1
32.5
34.0
34.6
35.1
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Published as a conference paper at ICLR 2025
F ABLATIONS
F.1 SCALING UP MODEL SIZE INCREASES PERFORMANCE.
In Table 14 observe that the win rate and LC win rate for LLaMA-2-13B-Bas is higher than for LLMM--2-7B B-Base after finetuning the same dataset. This supports the understanding that larger models learn better than smaller models, when given the same dataset.
Table 14: Scaling up model size enhances performance. In each entry, we report INSTRUCT- SKILLMIX-DINSTRUCC-SIIIIIL
AlpacaEval 2.0
Model
# Data
WR(%)
LC WR( %))
IK
8.94/14.00
10.20/13.81
LLaMA-2-7B-B B-BBBas
2K
7.24/14.95
10.75/15.76
4K
6.90/12.50
9.63/13.94
17.34/22.54
18.06/22.69
1oaMA-2-13B-BBB
2K
16.95/19.67
17.76/22.75
4K
15.79/20.70
17.08/23.05
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Published as a conference paper at ICLR 2025
F.2 WIN RATES AND AVERAGE OUTPUT LENGTH ON VARYING AMOUNTS INSTRUCT-SKILE SKILLMIX DATA
In Figures 4 and we plot the win rates and average output length varying amounts of INSTRUCT- SKILLMIX-D D and INSTRUCT-SKILLMI, respectively. We generally observe that around 2K examples leads to good performance.
LC
average langth
146
Ilama3 8b basa
win rate
328
Legend
SFT ISM (DO(k2)))22)
2.6
245
238 26
1ik.
26
num training examples
num training examples
num training examples
AlpacaEval win
win rate
average length
mistral .b, vo base
34-
Sverage Tenoou
2.18
Legend
32
SFT on (5M-SDD(k=1)
win
LC
231
20-
25-
26
26 examples
training examples
training
AlpacaEval 2.0
win rate
LC win rate
average length
1.16
gemmaz 9b, base
LC 35-
371
overage Ion
Win rate
3.16
Legend
(SM-5DD(K=2)
2.76
1/6
100
358
3.00
training examples
training examples
training examples
Figure 3: Win rates and average output length on varying amounts of INSTRUCT-SKILLMIX] data. Here, ISD-SDD refers to INSTRUCT-SKILLMII.I
27

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Published as a conference paper at ICLR 2025
AlpacaEval 2.0
win rate
LC win rate
average length
Iaama3 8b ba base
40.0
400
overage lerrot
426
win rate
57.
373
Legend
ISM SDA(K2)
27.5
examples
examples
AlpacaEvval 2.0
win rate
LC win rate
average length
mistral 7b, vo _asoo
average lerge
Win rate
Legend
ISM SDA(k-1)
SFT on (SM-SDA(K-2)
35-
176
336
2.6
196
num training examples
num training examples
num training exomples
AlpacaEval 2.0
win rate
LC win rabe
average length
gemmaz 9bb, baso
overage lengh
win ratc
Legend
2.00
SFT on (5M-5DA(K-2)
.11 176
16 136 24 num training examples
2.8 416
136
1.8
2
num training examples
num training examples
Figure 4: Win rates and average output length varying amounts of INSTRUCT-SKILLMIX data. Here, ISD-SDA refers to INSTRUCT-SKILLMIX
28

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Published as a conference paper at ICLR 2025
G INSTRUCT-SKILLMIX IS COMPETITIVE WITH RL-INSPIRED METHODS.
-I-inspired approaches. Turning a vanilla LLM into chat model consists of two main stages: (I) supervised finetuning (SFT) obtain supervised policy, followed by (2) alignment (with human preferences and values) via RL methods. Standard approaches for alignment, such as RLHF (Ouyang al. 2022), rely on reinforcement learning. Here, a reward model is trained on preference data to reflect human values, and used to update the policy using proximal policy optimization (PPO) with corresponding preference data, and evaluated on AlpacaEval. Optimization issues with RLHF, had led to RI-free approaches such as direct preference optimization (DPO) (Rafailov et al. 2023), which implicitly optimizes the same objective RLHF, and SimPO ( et al. 2024). seffrrecce-mdel--fee alternative to DPO. Alternaee RL-inspired approaches take on a theorete approach, equating RLHF with finding the Nash equilibrium of two player constant-sum game (Swamy al. 2024 Wu et al., 2024). For example, SPPO (Wu et al., 2024) approximates the Nash equilibrium policy via combination of multiplicative weights and self-play mechanism, where in each iteration, the policy plays against itself in previous iterations by finetuning on synthetic data (which is generated by the policy and then annotated using the preference model).
(Schulman et al. 2017). But the same idea can also improve cnstruction-ollowwiiii
Comparison with RL-inspired approaches Self-Play Preference Optimization (SPPO) (Wu et al. 2024) and SimPO (Meng al. 2024) are two RL-inspired methods that are used as an alternative to PPO. SPPO applied to LL-MA-3--B-Instteeee by training on 60K examples, whereas further training LLaMA-3-8B 3-B-sssruut via SimPO achieves 44.70%. On the other hand, finetuning LLaMA-3-8B
achieves LC win-rate of 38. 78% AlpacaEval
with 4K examples from INSTRUCT- SKILLMIX yields 42.76%, which is better than or competitive to the two approaches. Note that combine process (1) instruction tuning (with unknown amount of data), and (2) RL-based preference optimization into one instruction tuning process with 4K examples.
Table 15: Evaluation results via RL methods.
of models finetuned on INSTRUCT-SKILLMI data vs. finetuned
AlpacaEval 2.0
Model
Method
LC WR %)
MT-Bench
LLaMA-3-8B-BE
SFT on INSTRUCT-SKLLLMIXX22244
42.76
7.09
LLaMA-3 --M-A----BBBB- 8B-Base
SimPO
22.00
7.70
SimPO
44.70
8.00
S-aMMA-3-8B-Insteee
SPPO
38.77
Mistral-7BB-Base-Wl
SFT on ISSTRUCT-SXILLLIX((2E4444
36.70
7.16
SimPO
32.10
7.60
SPPO
30.46
7.59
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Published as a conference paper at ICLR 2025
H ROBUSTNESS OF INSTRUCT-SKILLMIX ACROSS RANDOM SKILL COMBINATIONS FOR SFT
We finetune four disjoint subsets of INSTRUCT-SKILL data, each consisting of 1000 examples, and report the results Table T6 Due to the randomness in choosing skill pairs, only 1 % data in any given two subsets share the same skill pair. Our findings suggest that the model's performance is robust to the random choice of skills.
Table 16: Robustness of INSTRUCT-SKILL.M across random skill combinations for finetuning. We SFT Bestral-BB-Baee-Vie on 4 disjoint subsets of INSTRUCT- INSTRUCT-SKILLMIXCN SKILLLIX(===) data, each consisting of 1,000 examples. The SFT-ed model's performance is robust the random choice of skills.
AlpacaEval 2.0
WildBench
SFT Dataset
WR(%)
LC WR(%)
Avg. Len.
MT-Bench
WB-Reward
Split (1-1000)
33.87
27.48
2835.0
6.92
-41.66
Split (1001-2000)
34.14
28.60
2657.0
7.00
-40.62
Split (2001-3000)
34.31
29.16
2669.0
6.93
-43.36
Split (3001 -0001-4000)
34.17
28.78
2704.0
7.12
-36.28
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Published as a conference paper at ICLR 2025
I EXAMPLES OF BREV.INSTRUCT-SKILLMM AND JUNKINSTRUUCTSSIILMM
Below an example Q&A pair generated from BREX-INSTRICCT-SKILMMNNYES
Instruction:
I'm. crafting adventure nevel where protagonist, city dwellez, is thrus: into dense forests the Jacific Northwest. portray their evolution from survival novice competent woodaman. What are some specific survival tactics could integrate throughout the stery to enhance realism and deepen the character's
survival challenge
realistically
Could you also suggest hew thesa skills could reflact the protagoniat's emotional and payohological journey?
Response:
Begin your protagenist's survival tale with basic skills, like creating shel:er using struggles
emotional turmoil. As confidence introduce intermediare skills like novi sovigating using moss trees, paired with feelings of achievement. Eventually, showcase their expertise with advanced techniques, such making surroundings and emotional resilience. This are not only propeis the plot but deeply embeds protagonist's psychological Lraastormation, enhancing authenticity depth of your narrative.
fallen branches, sparking their
from natural materials, syabolizing complete mastery
their
Below an example Q&A pair generated from JUNK- (IXKINSSRRUCTSKKLLIIX())))
#4 Iastruction:
multinaticnal company to give preseatation the ispact of cultural sensitivity in global business operations. Can you help me dreft compelling introduction and main peints that emphasize the importance of this topic?
preparing
job
F++ Responsa:
cultural senaltivity important for businesses, especially the big ones that wozk in lots of different. countries. Maybe say something something like that. You can talk about respect and being nice each other, which always good, right? Than maybe add some stu"f about making money avoiding isaues, though I'm not too try to speak confidently, fill up the time with some general ta k about understand and luck, keep vague
your presentation, just by talking
helps people get slong
better
affects business, those details. Just
teamwork, ahould probably oxay, sounds kind of important and cosprehensive.
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Published as a conference paper at ICLR 2025
J STATS ON DIFFERENT DATASETS
In Table 17 we report the average number of characters and number tokens in the input (instruction) and the output (response) fields of the datasets. For the number of tokens, we use the LLaMA-2 tokenizer. On average, INSTRUCT-SKILLNG model responses than the Alpaca- Praca--KKLLnggeee K-ongges,, but has roughly the same number of tokens when the input and output fields are combined.
has longer instructions and more concise reference
Table 17: Statistics of various SFT datasets.
Instruction
Response
Dataset
# Data
Avg. # Tokens
Avg. Len ?
Avg. # Tokens
Avg. Len
UltraChat
?
?
?
?
Alpaca-52K
52002
221.09
912.17
159.48
664.58
1000
511.37
2289.16
458.19
2069.64
INSTRUCT-SKILLMII-D
4000
511.58
2199.01
394.15
1644.88
INSTRUCT-SKILLMIX
4000
510.63
2152.77
392.32
1606.33
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Published as a conference paper at ICLR 2025
K LIST OF SKILLS
K.1
INSTRUCT-SKIILMII--D LIST OF SKILLS
Using the skill extraction procedure detailed in Section 2.1 random sample of 5200 instruction-respons. pairs from Alpaca-52K (GPT-4 version); 128 skill clusters from random sample of 1000 instruction-response pairs from UltraChat; and 35 skill clusters for alignment and safety. We remove duplicates, and end up with 484 total skill clusters.
we extract 337 skill clusters from
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Published as a conference paper at ICLR 2025
Table 18: (Part of 6) 337 Train Skills extracted from random sample of 5200 instruction-responne pairs from Alpaca- 52K (GPT-4)
www.acappppitiii.eeeeem.ccaaa.ip on
wwwearchhtecchiicc. ques
wtw.wu.....wwwwde........... wtt.kknowledgg.
probl em solving and decisiinn support
conpetence
communicatiion
knowlede ledge based and ident identificatan ification
wwt.kwwwwweeee eddg
technology ooonooloyyyyyoo and wwwppccpppteecccccocommo is sesceee science
irreative_wwee wrrt setiinngann lnw.innnnnnniiiiiiin wwaanniiteeee iiterature
advanced www.ssiinntii.ee.ee wwweennniiiii......eeeeeeieeiiie fic.iiiiiiee knollloweee
touri sm and cultural www.kkwwwllllooo..... ledge
ledge
machine learn rearnnng and al historiann ical and cultural
ast rstronomy and mytthyyttoooooooo colyyyyy....
seproblemssoiie weiitinggand.co.
ion
ques
research. research.and.inf.e and.andiiin www.iinnoatiinn..mmm
dat database. www.maaaaan.nn...mmmmm.mmmm -atmmmetttaiilllleeee sakilss
digital content creaticin ion
educat iinnttiion ion and game desi gm
research.and and.aanndaaaa.....aa adsaaaskiill I envir invironmenta. wsw.ncceeesssseeee
data www.annnnnnnnnnnaaaaee www..ii....aa...........m.. andaanalysis
envir cevvironmental al k...kwollll knowledge eddge
knowl adge edge
skills for fkills.for.effede wor.lfff.r.fffttttii.ooooo.....e wrffective.com
analyti amalytical.and.pe. .aw.anddddbll........
language and ccul ture
reb.capabilities and
customer service ce ae..and.pp
computer www.rsccenneeartee
analytico iialtiical caal skills
writing www.gnndccooprrro.oo
linguistic. www.iisticckoooooo
scientiff iirriiffi.ie wea.ffi.uumiie
hi historical ceariialll.
in
wot.annccatteooriii...... cetoriizaionn zanion
greative-thi tetthiiniii iigi-ig-- and iediiddaaa.. www-iideaff-oooe wow.formulllmmmm foormulation ion
content curation iontiion on and presentatiion on graphi caa.ccaaneeeeeee desi gn www.knowwleee edge
analysis
costomer rel relat lat insship management
wwwssssstrateyy..aae. knowledgebaseed www.speciiiie soppecificiing fic.iiciii in.tnteessss
wow.ogaaming.skii...... creative and academi c. writing
fashion and i festyty knowl edge
and and data alaaaaaaalya
data handl ing and teanagement
data and innforrmtt infforrationn analysis
informati www.ssification.a.....
cogniti Sognitive.ste wie.vvessee ses.kkii aakillssde s.alaadddd and snowledge
web and software deve Seveelopn op
language gr. and writiit inn skills
cognitive ive. creatiiiveeeeeeeee ive. www.tiii ing
specifice seriificcsujeeeee jecttknottooo wwwgeg writing.and seinndediiiii settingiiillls skilss
digital and and.graphicccccooooooooooooo.p. www.phicc.diiiggg.
internet wwweetteeehhoooooeee logiee
algeri cebarittmic.anni.. and.
travel Travelland..o aan.anddllll www.llessee..ee
www.ppoootiiooo ion
computer www.pprororis.....
writ www.iingaan...ieeeeee...eee...ee
digital www.marriitntt ing
machine learning
cultural and www.aandddd..l......eee
Skill Cluster Name
weltten.commnnnn creativee vve
geographical happhicallase
content weweent-proddee
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Published as a conference paper at ICLR 2025
Table 20: (Part 2 of 6) 337 Train Skills extracted from random sample of 5200 instruction-responne pairs from Alpaca-522 52K (GPT-4)
iiss www.uissis.cooppp.e wwwssionaaaeeeee on ana.andaaaaaayl lysis
language comprehensi in and expression system www.maan..aaawooo..k.m aasillsisss wtw..eeee...i.aaassseooooo.eeeee
probl em solving and cew.iriiiiiooo cicaall thinking mathema wateemtttiallaa. cicalllaee computer
daaaaassiiiill skills
wwwnndsuuaariiiaammoommmmmm.ma ieaiiatiion ion sci eniee and analysis
educational and pedagogicas callllll skills programmi ing and data mana gement
a.t.aandddattttaaaaaaaaaaa dat
economi c. wwa.d.aniiiiiie fonancia all analysis mathematiin hematiaal ical skill computatiion creat ivve and social skills natural.and. aad.eenidiiiiiii. inl.environmentee analyt wtapyiicallaaaan aaddlll www.looiical........ skills commain.specifie kedge
eustomer: r. www.eiiiiiieoe peervice_aate ce www.annexxeeiieee..
critical criticaa.chinnkng..aa. and analyiissiiii
cont ent www.crrttt.i....e. ioniioon.ee ieraatioo.nnd.vvee ang
www.pteeppcccc.ccccmoco.o......e cies
computerr-poone -pr pro.rrrraeeeoooo www.gramminnnnn...oo.
Preative art. and des ign
publi wellic.rellatee www.relattti ialaaiionnn ionsn skills
language I wtw.eannnddd..auuuu..aaa.
mmalytcca..thinkii
hww.hhowwwoo..c.cc.eeeeeeeeeeeee creat ireative.wrtt wwlvveewwiiii ing
content aaa ael--aaalliiiii
adolescent we www.wwwllleeeeeeeee welnwess Iness and activiti ies management
understanding and dewllingggggg www.with.... www.hwatth.uannnmmmmmmmm hhhanumnn_5 factors
wwwlaaauultuzeennnue entertainment
knowledge and.andddnne.. us andinng in. www.technologyy logy
artificial waw.aanneeeeegeeeeeeeeeeeeeeeeee ma ceaachineelee wawcchieeeeariinnnn learning
natural andd aan.aanddddiaaaal socialllll..ooo www.sseeneee ceiencesee.. knowl edge
anngabbiiiiiss
critical thinkin wikinng ng. and propppobbloo em solving
srofessionallie. ponalal,, deeeloopmet rcientific knowledge and ioppiication business and economi cs anal mallysis comput at
strategy and coll coll aborattoonn ion
technica cal. sk skills.rre w...reaated.ttt o.t.oaatttt...oooott.ee.e..ee
.en.ggaaaaggegaaiiiii..........e in language language www.iigggndd......... www.ppporossssiga.n......
pospitality. and le leisure management
www.prorgg........mm......m ing
data.processing ing aary-andallge aonddagggiimmms
creat ive.tvee.ee ve writ t ing and anaaysis
content creation and ana lysis
specific fic knowl knowledge edge. research
literacy and wr wrwwiitt iertiiiing.. skills
comput er and informati con.tttttttooto...o
scient siffico afficcandd. sic.anddttee wew.tteecciicco caicaaakkeee comput at wwwooonnnnooooeeoeee
analyti ica h.w.aaaaaaaaaaa.. haaddainniin
knowledge.in www.iin..oppareeeee
www.scce.ec.cc..cc..o.... ence and p
preative_wrae www.wwwwiinngnnn...m...m
language_skii wwwiangeskkille. lisssillllae wwi.kiissannn....
python pythoonprooraanee www.t-rorrraaee amming
data analysis and
Skill Cluster Name
inturrlland.soce
cul culinante inliryy arts bos business insss
analytical
comput er aciiciie
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Published as a conference paper at ICLR 2025
Table 22: (Part 3 of 6) 337 Train Skills extracted from random sample of 5200 Instruction-responne pairs from Alpaca-522 52K (GPT-4)
iaciicllle skills
language and literature comprehensi
wwc.aliizedddnio zed.zedddlweeee Mednwwledge
Lext processing sessingg and restructuring
www.liitrrrryaaaaa
creat ive writin inngtnn ing and Language use literary compost t on and analysi lysis
software deve oopeent and securitt
waw.yandeeee acology
ttiikiiing
language process ing an-aand.. worggand-linnge computer. and web. www.teechollllo pechnologies
dat anall aal.anallsss lysis and stati ietaiisiiin sst irssiicaall
anima 1 aandd Land.planet laneaaantatiii arr wrr.aaayllleeeeeeeee wty-k/nwwwllle wwwlegee
speci preciallnn al allize zed subject know knowledow ledge content i creatt wot.iiinnnndssi uummary animal .an..anniiiooo...... www.bollooccc.. _kkooledge
diet and envieeiiiinne environment consultins wewelopment..esttieeee software devee opment. wetssttiinn programmin wwmmiinggaaaa wanddddaaaaannaa
security
progr programming amm wng.iiiggnn Land ww..ppteer.scieeee....
digital lan...ddd.... oooooonnee line wnwlneeknowww// weee.knwlleddee
creative.we ive www.iinnnnnbbbnnnneo.ee
wnw.innnnndd...omoeeee.... and cow.unii cation
web.t www.teccholloo. ogglesssss.oooooooooooooooo.oo.o wnw.ann........ www.eesaannscccuceeeeeee
creat ive. and visual. arts
ai and tech unders standing
technica ca L.and. wwweeppllll.. creat creative.ttt ive wr it ing-and. research_ang wataarchhanddoo.
wathhannd.oggic... logic.skill
natural anguage-prrs language uud.uuun
creati ivv and and.annddsrrr..
digital.com
writ writing tettinngaaae
knowledge doma in expert. ise onl online inne researee rch and digital competence creati ve_ vee_and_daa.... sanalytica waicaalane wtw.wi.t.......ingggggg wwitiig
data org.ooinnniiooo.ooooooooo zat ion and machins ine learning
composition
language and gr araamar proficieecc
wen..techhiquue techniques iques economic and bus
programmi antrrammingg... ng annd.algorith Serrrtttmmdeee www.orittmdesiiigg desigg
wwtgorithssaaate thmsshh www.ttms..dd....oamm aad..ddd............. wad.daaaaaaa.mm...am.aaaaad aaaammnnippp catannpplltee at ilniioio ion
text analysis and categorizate inn ion creati ve and technicc iishhiccal cal writing knowledge.in werlge.ii.nniee.
iinguisti..ae ww..ic.ndd...eem semant sic.c.c.. c.annlysss
programm ing wagginnnnaa and. woftware.dewwe www.ddeeellpeee
www.uuusiissee insss analysis
anaalysis
content creat i ion and editing nin creative ve_des aedesigg.aaee
commun inatunication catiiiiiiiiio cratiion.e an.andd www.and.oocca..nnn...........eee
english 1 anguage proficiency
comput er programming..t weg.teechnie
www..n..wwiiiii......... Sewwiiiig iing
task and event management
programmin ing.aniiingg........ ing wow.dsoottareeeeeeooeeooo
english anguage skills
che.areas
ai mm wew.ning.appllll...
language w...aanddnnnnn.oomo.............
www.ppoooonnnno.ooeo aadd
creativy ve peewwiit ting-skiils
knowledge.iing www.inaaaadiiiien..e
pract malalllffe we..f.skklll skilss
mustomer-ii ieweaeilooioo lat wwwiioonnnddddd
language .art wet.geraattsssiiiiiees...... s.skills
scientif iereetiffi.aat wr.....addmatt...
Skill Cluster Name
data-analy ysnnaalysis.eo
language_ann Landllaaanl...e
linguist st
algori
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Published as a conference paper at ICLR 2025
Table 24: (Part 4 of 6) 337 Train Skills extracted from random sample of 5200 Instruction-responns pairs from Alpaca- 52K (GPT-4)
www.oroowm.......ooooooee......e knowlede
wwwppooemssscoooommmmo ion
wwt.::ittmdeeeeeoeeeeee.mee wen.ddvellle lomment
business and c communicccccccccmmmccm...c...... comnicationn on patationskiill skiils
creat ive. and artiatiit iettissiii.. i.t.ist...uudrstttt... www.undesttndd....
wan.anddddpp. space
customer wwwnterratior..mmaaaeeoom creative ive. www.aandariiiieee ietriistiin exexxrrssiion real time data handling ing
andaappiicate
creat ive and critial thinking
literary ana.l ysis and www.ttitcc...... ion on informat ion and data aaa.aaalll www.aaallsii ysis
cosmo lomoooogcal....o and aelaastrro rotomi cal
att.rttuatttit... ion
business and economic analysis
psychology at wwwrratgyymaarrce
programmn ing and cowputopptttt.ccccccccoo. it ion
history www.err.eseercc.aa...... ana.aaannnnaalssii.......
tex. tet. Lanalysis
text aertLanndlanguar wwtggugge.naalysae analysis foodLand cuw.ccisiiiiio woiisine.koon knowledge
progr amm anniig wewiiinggaan www.gnnnnnnnooriiiiiioa
mathemate t ical reason ng
business www.mmaaaannnn...mmmmmmmmmmmm..m historical. wtw.:iiaaaaaaa...uaaoaa. a.l.anddcuul cullduuuuurraa..
literary www.ositttt......ccccmc.. www.wedgeeaate
knowledge. www.iinnnggaappp.
mealth.and_i iindiiii.. iffstyle
technice maanccall 1 www.kkowllllee...
scientific knw.lnwwwwe
advanced wwaanced.iiite....
informational settion-pprre
marketing. ieseiinnnand..m
writing pritting-and...
art art. [icial intellineeennee igence and machine learning text analysia and comprehensi
management
internati onal i ons and poli ey design www.eeeeedddderereeeeeeeeeeeeeee iie. wertiiiewwiii artificial inniilliineellie wwwllligeeee..m
andiing
www.langgggg.... skills
machine.i wwwiineeeeeannnnnne www.eeernnnnnnnnd...m www.riinnnnn..ii.. wet.anndai..nderr.. wii.ddestaadiiinggggg
critical wet.hiiiinng wenking.-eee and catmmnnicaaiioo ion
machine learning and deep learning
website e. www.annd.dccc....o wocoooeeeereeeoomoo ree. devel development lomment
knnwleedge
iffficinccy
artisti irttisticante calluuuuuun wttuuallriiiiiiii g_t ght www.sssceeees..iii..
processing
manngemeett
social and www.ereaeeppp...... skills
dat Mata-analysse www.ssssnndsseee sstiisiiss commun communication.
data management and analysis
winelnssss.. knowledge
language_ t rans lat www.saatnonnrrtree oon.oooffoo.......
strategy www.deeelllol. lopment. and
reach
aeageeand_ssee udy
machine 1 learni ng appl i caat
Land_cuu tturll...............
knowledge ba based kils
web-and. diggittalllll des ig in
writing wat.iingccoooosiiioo.........
literary ana www.aaaalssls...... lysis...... and
tritiiggaan wowmunionnnoo..mcc comunicate ieaiiiiiiinnnn ion
aaatiion.andd and
data-analyse wwresssand...
system and www.nnetwortt
content www.ccccrenn...........
health-and.c anw.weedddeeea...e wew.eelllee
Skill Cluster Name
historica ciaalal lal.calaa...
general-knng
37

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Published as a conference paper at ICLR 2025
Table 26: (Part 5 of 6) 337 Train Skills extracted from random sample of 5200 instruction- instrnction-responnee pairs from Alpaca-522 52K (GPT-4)
advanced www.woowwww..nnn...e...eeee.eeee www.ddleeeattte..aalll...
knowl knowlend edge a www.acuuisiiiii on. and manggement
design
inderstanding.com wwwiinggof..e weoffscceene weleennificccmmeeee creat ive. www.iiiiaaiiii....c......... hinkkng
digital and computat. ietatiionll..mmmmm skkils interpersone perrreesoonll.
language and c communi calcaatiii cation skills
creat i ivve writi ittinng in and storyte weryyeellnn i
weooonalldeeeeaaeeee deeee neveloomeeti opment_andd ltw.ett.adddd.. wtw.taand.wweeeeeeeeeeee..e..eee welnness
wtt.:uuallaaallll...... analysis linguistic.ance wtt.iiiannn.tetuaaa........
aeconal-and.a.. an.andd... al..anssociii. ses.ciillll alllll skills
educati pruuatiional.o ionallalll iatriioonlnnssiiiiiieeeiiiiiiile linsiggitt ght www.strattee.....e........m
ll..andiiiland............ai.iaa saand_l 1 linguistics writing.ske lllllls....
data ana yyss and computation
ereative content generation
health aaalthhand.aam and nutrition eepeeeeettte.eee
language and www..and.ooomm. won.ommmuiiicccm cation
text text_and wtw.aandd.sst..reeee.o gi..epsccccsiiiggggggogooogggggo
environ alw.errnnettalllammmmm.......l wwwwwnowwwweee www.edee..........
wtw.deeeeweeeeeeeeeeee.eee...... expertise
data.scient www.n..an..aao..ee.....
algorithmi hmi aaaadddaaaaanaaaaa
knowl seoowledge.ie wwrrdeebaseeddee ana
textual textual.ana anna ly sis.syind.wwiiii...aa..........i
content knowledge
knowledge.to www.eeege.basd........
personal
interactiv ivee collaborat ion and activiii viiyy Planning
mataetttii i cal 1. commptation..o and probl em. solving
sustai natii 1 lity and envi ronmental awareness
computat at i onal Land www.ttchhhooogiiccooocc cal knowwedge
informat on awalysianaissaaaa and.ini interpreratttt. at on task www.mmaaaggeennmm wwwggemettandddaeee aarganizaaiion
python progr woogrammiiio ing and appl ication in www.anndppeedccc...
wwt.aandddittraaaaee literacy
cultural and conteetuuuttaa knowledge
scient i fic know ledge and analysia
www.knowllsls wwwlege
cultural al and wnw.aanniiiii............aaa.ai. hiwhhittt wwwhoricccccc aaooriaall.......oooo cal. knowledge
Aiterature and language skills
Lddg
www.kkowwllleolo knowledge eddge
www..ggoggaahiii.ooommmmmm ca Saalkkkn
cognitive inntiiive vee. sk kilss. and literacy
personal bette rment. knowledge
wrofesssinnl.... writirii ing. skills
data.processiin ing www.iin....nnn.e.. laa.adaaddlala......aa.. lsiilliissss
skills
natural and soci ieilll all sciences
comput er and web wtwebb.nwwwwdggg.
lingui sttguistion iesiiccaa wea.dddext.aaall...
ieforizatio
media_and_ent wenndeenteee eeentertaiinn peerraiimemt menntmeettteee
aut wwwoomoiieeeeeeee www.ttiieeeeee.o.......ee.. technologyy
literary Lanarr-aaaaaaaa aan www.seeaa..........e.....e.a.a.e
survival and pl anning
financial mwn.gamenn.nn...mmmmmmmmm.......
literary aadd lan.uagggge...... www.oaaa..........oo.....e.eeaaa
creative www.eeeeeeesseeeeeeeeeeeeo
informat www.ooo.nctcoooeeeeoeeeoo
biologica calooiccalste wal.ccala.ddedeeeeeee
market www.arrrsseeece.com
data_handl wei.innnanne
writ www.tiinnnnnnereaaeeeee
Skill Cluster Name
text wwwpositotttttoccoocoo
38

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Published as a conference paper at ICLR 2025
Table (Part of 6) 337 Train Skills extracted from random sample of 5200 instruction-resse -repoinnn-----sseeeeeeeee pairs from Alpaca- -2K (GPT-4)
www.iiinnnnanaal......oo. analysis
technical computer based proficiency
software deve 1 lopment. and engineering
wtw.oaaand.aaaaaaa.......... awwreness
www.iinnnnnnccnii...... ques
language and wx it wiinnggttt.... I achniques iuues
cloud and st streemmnng www.heoocolyyy
literacy aan.aandlaapa www.llaggggggeiiii.....
digital www.ket wng.aaatttttttgtaaaaateeee ing.stratgggss
python programmi ng skills
wr.and.literray...
problem em www.sslllinnn..................s reg-reeasnnn
stati ist i caal cesiicclcloooc www.oct atiitaiinn..
computer www.sceeeesc..eee.ee and
informat won.processingeeoooooooooosossse
social social.commmicatioon www.alccccciiiiccccco.m......cco
lata.basedaana lysii
manguistic.and...
educational.
intell ectual comprehension and generation
written commun caciiicioono on and content creation
language abilia abilities ies and rewziting skills
geograph iaaicall and enlevvii rowmenttetnttttmmmmmmmm al iknowledge
skills
technical cal. and procedura writing
wallaand.hee medical.ame medicallandd.eel heal heal theare knwwledge
iiggussiccskill mrl.knowledge
environment. and life sciences
user user-inter www..iineratiioo..o ondeesiodiiiiiioo wan.ingnnnnnniii
critical and sthical. tttiniiinngggggn
technology
digital mode inggannn and design
digital market et ing and sseo
natural manguaage.rrcessiiie
www.saannncssssiiiii......... ai 3iml ai.ml_noone .nnwledge
digital and waw.laanndaaaaa.ee.aaeeeeaaaaaaa www.daaa... tatttteee
literacy- and_1 www.aannnnnn................
Skill Cluster Name
speciali wwlliied_kken
39

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Published as a conference paper at ICLR 2025
Table 30: (Part of 3) 128 Train Skills extracted from random sample of 1000 instruction- wewpeciiioo--spossseee pairs from UltraChat
envir rnvirrommental science and www.ssuataiiiiiii.......
_skills
health we 1 ll and fitness knowl edge creat ive. and.cnnneeetnen
-andaaddd... ddaa_.mm.. adminii stration on on
economi c wan..busineeee www.ccowww.eeii...oo.oo.o
www.eeeeeeeeeeeeeeeeeeeeeeeeeeee del.deeeeelll dew..eeeeeoooeeeeeeeeee.e. lopment
eritical analysis and evaluation creat ive product ion skills
pal--pyychoogyy
economi wwt.n..aaaaaamnnn www.annfiianiiaaa..... planning project ject www.aamemnn..n..mmmmmemmmm.e.... und.strated
cul multural and social. wnd..usndnsstnnnn
legal experti se and counseling creat weve .wr wrritti wiiiiing ing
economi e. and www.uuuiiissssseeeeee stt.tnsssssttttegy.............e
cuisine anddddd...... www.cikiiiiii.iii...... wini.ng.. knowledge content. creat ion. and analysis
dehavioral Moorl and mental a health ah
data handl felling ing and management
creative and design sptitude
speciall percializen irecialized.technte wed.ttchhical.ski sport www.ssspcccc........ specific.streeneeeee
writiingaane communi cation
dat aalaannlllsiii and mwc.ime..eei...mmmmmmmmmmm
programm: ampirimmiii wnw.iinnnddd.... sysssysteess system writing www.aa.dddttteeeeeeeeeeee wew..enallsiiii.se....a......sse
ills
www.ooottttttttteeeeoeeeeeeeeeee ent
bublic.relaticn cetiionnssss sks
fitness. www.nnnnnuriiii...eeame.a
business .st.ategyy..............
culinary- sskills
Rehaviorall
interprerrtttiiiii ion
digital skills and technol ooillal application
cultural and societal underst Saarsannnnng informat: on extract on and analysis
pogiicalle udiees
comprehensiver ve. www.ustandia....... strategees
programmi ing and syst ems developeee lopment
business aaaaaaaaaaaa lyt irall Land.eeeee cevaluationnss.. cultural.se aww.aanddhhi.. wan..hiisttteeee...ee.a.... his wiistoiiiciiiooccoio cal waw.aaakooooooooeo www.kkowwleeeee
analyti Sytiica iicalll 1. and research. skills skills www.iinteehhiicccc ing wnw.aanddddduuatta.....ee......a www.atducciio.. on
munltural... anallsii
business and product. management
www.einnssssnwweeeeeeeeeee knolloeeeeee
ai.andmachine _1 wwwning..gplicattt............ee
sntioonssilll. wwwaaaemeet.and.eeeee negot www.nnnggiiaiii patiiii intion ion
experiencce
cultural.ande www.aoooill.inssi...........ssss insigghsss
wendiiieenddddddaa and literary ittearr-skills skiils
research writing iniiing...... and analysis
techni ca 1 and di gittal skills
pvograming.and www.coooinnnnnnddiicceooe
reb.designi gww.andd...oommmmmm............a www.ann.deeee...e...mm www.ddeeelppee eepeentent
environmental al and www.ecoololcioo..
data hand ing and insightss
cuisine.and cennrritiinnn wonntriiiiinallste....
Inderstanding and. empathy
environment www.ccocccee...cc reraavaioo........
lysis
web-and wwwmmiiiiiii www.miimeeiiii.m meiimedaa..ee demeedaadeeiiooo des ign ign
teaching nighingg-he reachinggandd.pree sarg-and.pree presentt itnntaaion
financial Aanancial.andiae aus.dddduiisnnn couusinnss... creative ve
data wii...nannee anggandLanalyss
consumer -ooods. insight
personal www.aar.aaa.... war..and.le..... a...nnn........ wwn.ddiiffe
computer programming
Skill Cluster Name
historical wwwrriialaanddda
management
40

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Published as a conference paper at ICLR 2025
Table 32: (Part 2 of 3) 128 Train Skills extracted from random sample of 1000 instruction- petprruccioo-reppons.. response pairs from UltraChat
wee.knowwwlee ledge
ieetingg-eee strttegy communicationn www.onttannaaaaaaaaaaaaaaaaaaaaa www.nnndmarreee manddmarrett..
technology.deen -evey oepeoooooeeeeeemmnn oomeent Laaddannncuu and.seuurity
di digital media.and and marketinsi wtw.tiinnsskklilll skkill
wwt.siio..skii. ssiils
www.iinngnnnmoppuue compott ing-skills creat creative.war ive. wr it ient-inggaae and.composiiiiiiiiimmmm.om.m.iim on
aatooicallaaaaaaa aanallllsss
wwnttiffi.koooo fic.kwowedgee
bi biomedi cal cameedical.kowll. knwwl knowledge.ande red..addddeeecch.
syythesis
international ional, w.w.ilannnn...........a.a und.politican cultural factural.knowii www.wwwwwwww.............o..ee.e aadge-and..om and analysis
wrriting.and.com and cowprehenseeessooeoooeeme sin.rrnssinssllll.. media.and and.anndenns wennddeeeettttteeeee
cultural historical knowledge
policy.analysio satallysssaaddeye. Landeevalll leaeeeeallliiooooo
writing and content. creation
data-anal ys is ys and prooesssing
writing and cowmuni cat ion.
survival.sxills
technology and progrroogeeo axoogrammn ammi ng critical andicalllnndddh anndhisttoo wwwcaall...
wwwddgeescieeee environmen eevironmental.. wew.aannggg....
technology ant.auutmattta.... ion
sub jubject.in www..oooonoowweeee.o.o... edge
creative writing skills
peecialized.sciente iementiffiine
criti i cal analysis and.and....
out -and. www.ssurvvvis.esseesss
el climate_ande andd.nd.en. www.eeeenndeeiiiieeeeeeeee www.deeviirnneeee
programming
communi cattinnn and. social and eemo onall inteeligennee
software development and interactive technologies
writingg
food re lelaeddd.e knowl smowledee ledge and recommendat ions
advanced writi writing in and comprehension skills
cultural hi nistorical and religious studies
and we weellll Ineesssss.... www.erstetttt............. anding
strategic. c. deve 1 lopment. and annalyiis
wol.domaiii ins
policy aediccyanddreee www.nndrrrrllllle regwlattiiiieeeee. ion. wnd.unttaddiinn
public. and tousiness, deminiiiiii iistrationn
werrssy-awareeese
wtw.sssssadd.di.a.ee.eaeee..e.sa ssa.ddddsusssse.s s_anddsummaryy susuaarrrr
pultural.sn stuudeessadd.. www.anndanalllae analysis
critical think making ng and research
literary and cultural analysis
machine learninng ing and model ing
creative ve and cu turrll tural.acumeer aacumen
mental wel aibeing m weninndfulln fulesss
securis ty and saft fet analysis
cultural.ane Land aoociallanalyse research. anddd. data. a.aaandiinn
knowledge.in. in. inmmssii sic. wwc.ianddpiaaa
cultural and soci veiieall... Lanal
digital www.mmaratitt...mmmmmmm..m ing strsstrreggg
Preative-wze www..inn.nssorrr...nn....o.....e www.wewwwwwineeeeeeeeeeeeee
wwn.anndlitera.. communi catounicaiiinn cotaaiinnnnn.liii...
inclusion wellussion.ti and di velviiisittt
health_and_ae ttetthhmeeddiiiiieedeeeeeeeeeeed dlmediiiie e wwwineerelll
desiign
anvironmente www.mmetttaaaaa.iaaeeaeaaaa www.aan..iiiioo
software www.eerrrrrraaaaaee....ee.e
informati on. an..aaalssa..... in..rmmaiionnnallly
web_and web.and.graghicc.
Skill Cluster Name
self self.care_de wwl.fccareaand.eo
understand
41

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Published as a conference paper at ICLR 2025
Table 34: (Part of 3) 128 Train Skills extracted from random sample 1000 cestrruction-rssponns pairs from UltraChat
www.ininngaiii.mmm. on
wwd.aandnttt...... creettettttt eve.expressinn
www.lldwwweeeeeee.c.ee.ee and
market_anae www.eee.aaallsisssa.a.. wtw.aaaasssssaa..sese..a... sttsssanndssttteeesssssss
underst www.rsannning...mm www.aannaaallll..m.amm
di digital www.mmedsssssi....me
techni archniccalo ca wwwrnialllkoooooo
writi writing ing. www.tiignnnnn.ccmm
knowledge www.aaaee...ee. www.d..ppecc..e...c.oop..p.....e www.dpppciaaa.c..ee....m aeciaaaiie www.erstttnninnngggc..n
gardenindg
iizztionnn
researchs carc..... and analysissssa.. sssiiss
aad
academi c eur aataarrhhandaaaall...
science_and.ee anv www.oont..uuu..oooom....
envi environmen wwnmenteeotooooo al wwwseceeesscccccoco
Skill Cluster Name
42

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Published as a conference paper at ICLR 2025
leisure act aerre.aatiiii ieiiiissseei.... wes.suggeesion ons
content moderati on standards
guidance
sensitive. topi naviggationn www.::iiiiii.iiii....oo..o.....m
mi misuee leuuse.preeenee www.eeppeeetttteeee wwtreettiionniio www.iioviiiiooo ce
compliance iance st atance
mental health guidgndd dance
resource optimizatt in
educational.rs wew.-ressurcessse legal compl
legal wawallggiiann...
Table 36: (Part of 1) Alignment Safety Skills
dietary -guidance
romanti Senntic..n we..eeatioon
skill enhancemens
wwreer..dvaanceeeee..
achical.useeeni
legal advice
safety tiissttipss
ethical
guidance
www.oolelliiiioooooeoooeo.. weeegiiimmeee titttimtteee e.taatettot opic opic
fami family-red lelllly-elattt at ionship-advice
self_di wal.fdiscove.ee acivvcerr assisstance
social_sta www.attt..enneeneemee critical think ing promotion
privacy poli icy exp lanat on
ethical secussiin.fosserri..
liginalitty
lawful lawful www.eolooy.cceoeeooooooo www.hhooooouuuogggee.eemmee wusgge
physical_hee hhe wtw..dvaiiiiooo ce
empowerment rment. st sateeiiess equity www.cecciionnno ion
aestheti enhancement
cybersecuzii addvice privacy maanagement
friendship.men hat.hiimmaaaggenettta...aaae
sustainabilis wttaaaiillyy.iiiiiii..
wwamoofforiigioog or wwt.ooriiinnn
Skill Cluster Name
promotion ion.of..noof.....ooooo
redirect_to.
43

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Published as a conference paper at ICLR 2025
K.2 ANSTRUCT-SKILLMAE LIST OF SKILLS AND QUERY TYPES
Using the procedure detailed in Section 2.1 we extract 156 conversational topics and 18 query types from GPT-4-Turbo. From the topics, get fine-grained list of 1,143 skills.
44

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Published as a conference paper at ICLR 2025
Table 38: (Part of 156 topics extracted from interactions with GPT-4-Turhh Turbo
wwt.mmaainntttcnnn... (it)
archi tectura wwt.turrl.ddeiiio....... desiggn
transport. Lechnology
www.apiica...aee. facts
software tutorials
rehaviorall www...all..seeeee....eee..e..lee
training.rout ines
nutritionall finoonnnniiooo inff
industry trends
world wellddeeliiin ligions
pprporate.finane
stock market
civil.rights sights
online_courses
geographic geographical
b biodi vediversity
mental Lhealth
emerg ing tech
Pome_improvent
career advice icee
Lreatments
chemi stry
information
study tips
Aigorithme hmss
strategy
first_aid
statistic
hobbies
cooking
algebra
grammar
film music
chilosophical. aeeaalttherriee Les
programm wewiingnlaaaa Languages
software www.rreddvellpppee loomenn develogmentt
educati educational ioaa..thorreeess
vehicle scli maintenance
disease yymp Loms
lifestyle choices
natural.da lidiiiis sasters
language-1 ea eelrnnnngg
personal aooonnl.finanee
mistoricaLeeven.
climate.com allmate.change .change
instainabill iittyty
social.ther altoiiriisess
sportinng events
gadget reviews parenting ing
wel Iness tips
public moblic-policy
market trenet.trends trends
physics engineering
legislation
visual arts
leadership
team sports s
job hunt ng
literature
pet-care
calculus
travel
Topies
diets
45

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Published as a conference paper at ICLR 2025
Table 40: (Part 2 of 156 topics extracted from interactions with GPT-4-Turhh Turbo
disease.pree on on epidemi wwwoicooooooooooooooooooooooooo wag.icll..eee uduiesssss
envir invironment aadd wnw.deeooll...oeeeeeeeeeo www.eclleee logy
social wwd.meddiiss.... wtt.mmaaaaasaaaaaaaaaaaaaa wtl.assttttegiee
lifestylee.ee leeeesyleeaddleiii..e..e... pidddeeissnn sure surre
sports and wtw.sannddddddddeeeeeeeeeeeeeeee recreati ion on
rducation.ant wowooonnnneee....ee........eeeee wnn.deeeaaiinnn
vet veterinary medicine
resource wwwoorreeeaaaageeeee maaaggemen
fi finance and economi
civil eng angineering
cultural w-ddnamiccs
internati iemrnatiinaa ional law
ethical aehhcal.ddiee d.l.ddelaaass lemmas
law law_and_ss law.and_governee government
news wes-rrporting irporting
social 1. cust oms
space mi ssions
fashi onaiiiotttenee rends
touri i sm. sm.ttmmttte... rends
design resign.theon theohherry
meteorology logy
aichanical
diy ccaffs
fundraising
diplomacy
bicethics is
painting ing
pet care
theatre
poetry
wealliccheatt.in.e initiativess
warttyppaaciin. ices
business and ma manggeeent
arts-and_ente aalanddenteee wentertaan wwteertiineeennnn iimment
digital www.imeeeeeeeeaeeeeeeeeee tends
narrati ivv.. techniques
text ile wwm.maanfcttriinn
history wwlooryaandggeee www.yannnggggggapp....
psychology hochooloyyyoooooo aandd www.aanddccccccccoco
content -ma-kmtiinnngg
planetary science
literary sanalysis
renewable samewable.enee eeeergy
sustainabin www.inaailllyeeeeeeeeeee
hotel mannagement
moral philpshess losophy
healthh 1h and medicine
media_ana a lysis
animal arimal_health hhalth
ngo management
global conflicts
event-ppl pall-lllannnngg
linguisticn iss
anthropol pogyoy ogy
oceanography
electrica ical
ast
sculpture
geology
charity
Topies
dance
seo
46

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Published as a conference paper at ICLR 2025
emergency wws.aeedes....ee.....ao and and.anndrrsse response
wtw.eeettt.anduurbbeee.e. warrraaapppeee paanning g transportati intaaiionnddd..mm. aut omoti inotive ive
heolichhealth...m and eppdemiooogy
Table 42: (Part 3 of 156 topics extracted from interactions with GPT-4-Turbo
visual.arts.d s veterinary sciences
hospitality aan
home and family ly
cultural warraal.stee .studies
performin ieforminggatte ing-arts
energy.and.resour
engineerint engineering.di
mathemat ics
internat onal lalatii ons and global studies srofitssector
career and professional www.deleellneeeeee opment
social social.media wwd.iaaandddddi gital. wwarketins ing
comput er aaciieee eeeeeennnnn progr angganiinn min
ooatiioon on
media_stte stw.iesu.ese...... and j journalist
technol logy and innovation on aoodaannnnttrtiio
languages: www.annd.ii...... minggiissii cs
astron omy www.deppaeeeppl..ooo
htrpby-and.reiggi.
phi philanthropy and
fashion.and wew.annddeeeeeeeeoomeeeeee iles
anc.ssanddmmie and www.:annndd.d..a..eeemmmmme mwwality
literary studies
earth wertthssieee sciiheceseess
philosopby-atte
ethics
Topies
47

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Published as a conference paper at ICLR 2025
www.aaaeaaaammneneeee management
Table 44: (Part of 18) 1143 skills extracted from topics in Tables 338 40 and 42
encee
textile wel.eeeddgnn....eeee and weaving
cattiicaaii ion
cross cal.culltti casssccuuuurall. turall wun.drstandiiin
tental.toughnest trriiiiin
wwteelicttaaallyan global.conflice l.aislysis
textile wiiiiiiggogeeesseeeeeeeeee
transport. data analytics
navigattionn ion public speaki
mi miraal i dent i fication
time www.raaemeeeeeffiiii....
Interpersonil www.ooomuniice
cast i ng di ricritii ion ion debat trratefface www.fiiiiaatiiin character weweerddeveeeeee lopment de
stakeho "takeholder.comiee www.oolder.coooo www.coomunniaai...
Aducationnll aa sessmeent
ruction
investment.anm analysis
whopp cooooggrahyy skills
endurance_t.ee rainingg
Alinicall aalmicallpatholoo pathologgy
pet conttiiiionpplaaae
diving proficienes
expense tracking
network. . wws.urrrityyy
home demee_dccoatinn
garment www.connsttssre
performin performing.arts
regul awwoyaaooooooooo
genre analysis
spacecraft,
wwwp:owwwwww.e...eneeeeeeeeeeeee inteeiinttgraoion
wtt.:pptiiitiii... wwtiiiiiiiiiiii in
data structure integration ion
manage diettrrrrrrrrr.rrrr....ad www.rrssttiiiio.
marketing ing and promotion ion shock prevention inn
effective ive communicco communicaiion ton
writiing review art icles
production on. m
music www.histtooo. ory research
constit tetttttioooooo ionaloonll-. Looallinteer..
mood_safett-practt ces
www.or..ooodinntt.... in
debate and di scourse opr.execution
www.ee.ppiimmin .opt
system www.mttoouuolooooooooooooo
act www.inggtcchiigge iques
project.manee maw.mmaaaaggamm gement
sustainable design
ristorical.war writiing
dance dance.choreogee chorogrraghyy
wood_sculpt pouuppiing ing
donor.research
mmotive.expresiiee
a wamaachineeeear....
ivercrre
iwwentiiee.ee ve care
aut www.ooooooss.eeiii...o.
sporting ing events
impact aawaacttaaalsiisss
beyychology.ndd.sook
userr wwerrexperiennee werre.eneee.
coftwaretestinm
field field sampling
stock market
preventivy
Skills
48

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Published as a conference paper at ICLR 2025
resourge conserves weatt on strategies es
weteevettieeemssurreeeeeeee weeeeesuuees
sustainable iaatanaalle desidiiip gm www.ccpiieeesssse
Table 46 (Part 2 of 18) 1143 skills extracted from topics in Tables 338 40 and 42
green infrast design 9n 3d model ing
comparat ieeereieeellil www.eeewwwiio.neeeeeee ligionsstudy
-Knowledge
study desidiiiinnn gn. and conduct
celestia wellestalnaavioee i...vigaaiiii gantion analyze www.einnttee.i....eeeeeee content
renewable energy systems
businese and and.annamaacd. management
media. media.experr exper immettmettt at on
quantitate connitatti aivaiiiie..eeee www.saissnnnggg
www.ooolllaaaaa..oooooooo..aa..a on
cognit i ve_behavi wwweoorallaae
software, e. devel lopment.
cultural 1 Leenssii itivity
engagement strategiis
social pocial_medis mrediasstratege
educate cn..reventivee in jujyy prevent ion
financial aahanciaa.budgerr l.budgetiin ing
research.te wew.ttechnniuee
quant comput ing
impact wew.eeaaaeeieeeeeeeeeeee ion
impact www.mureeseeeeteeeeeeeeeeeeaeaae
economi c. e.modeling ling
pharmacologicaa cag.llll.ae
trend mon.moriniiinnoooooomomo
morldrreiin religions
st wwwkeeolleeeeeee
filmeeditimg tidiiiing
rivil-ris ri rights
project_coll
wtw.gisssssseeeearraaoo researhh
harrative technique, evaluation
public outreach and educati on adapt ng communica citictif.... on styles
uree eccuure cloud_solut www.onnninteeeeeoomeemo.ooeeeeee
food-prepara, wetttiionncchhn gues
pioarchaeologiie aaall-nnalsiii
training
numeri ca 1 computation on
historical antoricalllie weliiallllnggiiii ingiinguisi inguistican wwwsetitssteeee
www.innooomsstiiiioo sisiiiiin
hydraulic c. engineering
rhythm. identifi ifiiatiiinooooo in
recommend ba anced meals cour course_dest senersiedeigg de gn
ingredient. substitution
peacebui weaiingsstaaggiii
creati aeliveevisuas izatiiin on
evacuation oo.priiiirroo
visual storyLelling
ethics. in. reserrhh
valuations iireiionttt wwwaaiio..eccniiee.. www.teechniques
style.consiste www.onssssencyoooom
a.analllsis
grammar r prof ciency
resili pesilience.buiie wenccebbulldnn
mult pelliimedieee www.ii..coonnteeeoeeeemoomeoo.ee
stage management...n....mmmmeem
behavioral wwt.oorlllaaalaalaa.aa..aaaaa.aa
disput www.eesoluiion
ocean warammoodellln ing motor.control
bnnwriting.come
glin
stress www.maaaaneennnn
printmakinng wii.ing.tt....
Skills
49

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Published as a conference paper at ICLR 2025
facil Dacilitata attiitttiiiiiiii vattiing inclus ive conversati los
data maaammanngmmntt.... and analysis
42
digital advert.ising quality control inspectionn on
management
computat ional. w.w...ingg.istti.ioooo.........l ics
religious iigiiuus impact: assessment:
Table 48: (Part of 18) 1143 skills extracted from topics in Tables 338 40 and
renewable.ent www.eeeeeeg...eecr.....oo
environments www.eettandeeollee.. logy
deddeeigg gn
wes.ppliing ling
computer aww.aieedddd ded.d.deidddddddeeeieeeeeee..
empathetic ic. listening
www..eechnigggg que
on www.paae..pp.... peppimmii zation
grant wr www.iiiinannn.....
ethical reflectiinnn on
wwwing.technice. ques
laborat aaboratory. ory safeety
budget management
learner engagement
seo_strat wetrrratyy-paaanne
water mater.resourees www..resources.oo
seochemicee cal cetamicallsaapp.a...
mi mission. planninn
algorithhn www.riithdeeiiiee
Mydrations io-ioonnngg....
healthy cooking
event ppl anning
writing weiiinncclle aig.clartty
space wareeess
culinary.skii .skilss
woltiiingual.commi
field meld.mmapping
visual-ar's www.laarts.de
radiology
www.oottttttaaataccc.cc. ittbualiiiiinn inn inn
roof woof.repairt r iea.r.annssaaaaaai...iiiiiiiiiaa insiialatalooo
quality ity wy-cootrrol. rol management
using theorems in caleulus home safety procedures identify www.common.symmpr. oms
cross.plas form form.derrrrr dewell 1 oppent
complex weweeeeseeeeeeee ence. forming
approximatings fnntatinnnnnnnntiiieeea iictiions ions
job search techniechcccoccc tecsniques ques
innovation inovvation.oo innovation.creatti iion. on.creativity
geome rii visualizatiio
brush stroke mas Lery
conceptval ana lysis
geographi aephica cal mapppnng
nut mut cotrriiinn.p iiiinnn. plappammmmm laanning
inte ertervieee lew wethhiicuuuee qqeuss
recipe_dever opment
student moti vvtiion
lifestyle mnddleisurs
educati educational eeoaoooollaaooooo www.iooeeaaeerrrreooooeoooeeeeee
ret retimeeent. p planning
job.search.ss wwrcchssrrteggee..
veterinary treatrtteeettenttt..
curriculume pepuuuummee deside
trend deeedennntaatiieeeee
energy way.mmaanggeenn eeeeet
manual www.erittttttte.oomm
orbital 1 mechanics
donor relaioons anisons
circuit dessiig gn
cost unstumeddees deddiiiinn
distorical.co
Skills
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Published as a conference paper at ICLR 2025
www.teehniiqueee ques
www.pppppapiii... catiion
machine .I learning. integration
Table 50: (Part of 18) 1143 skills extracted from topics in Tables 338 40 and 42
energy efficiency audit stoichi oomttry ca culatiio crochet. ---niiiiiinn
www.eee.gggege.......meeem in
healthy Seathhyyetiiie .eat ing.habitss
spark www..replaccmeee...........
waaggggicllddes... gn pedagogi
reflectio ive practice
network confi fi aeiiggratiin on
invironmentai weweenaalliipaat
robot ics. integration
gn
battery ma maintenance
caloric ic management
screen.t inttimmme.. imme managamannee
water_com wow.ereesseate.o.ooee....o... ieratiinntteeee
theoret i cal mode Lappli
language wwwaaagee.raaaneee www.araanaaiion
nut rirtitt tion management
treatment. -p wpp.anndeiiigg...
law and government
Ieeer wwwhooddreeenageeee stakeholde..engee
conflict. www.llcttrresollte -ettrrsollut cooluuiiii
di diagnostic.tes wwt.sti..ctsiiinnn
voetzy-workshogee
brand all allginnnnn gnnent.
legal_wr wwi.wiriinn
persion.contres
data aalavvisie aerissalliin
landscaping.die
public cww..llealleeo
orbit orbit.dynanl dynttdddnmmcss
water www.rreesuuc....
st stakeho
www.:iinnnoooorooooooooo wwwr-perfooe foraaneee
ieeesimmlementtiee Lement at ion
www.riass.eeeedoooo...eeeeeee. deveee lopment
www...rranfoomaati... tons
hea 1th mon innitton torrtoiiii in procedures
www.ccalconniiiiiiiiiiii for
customer ins ight analysis
animal wel fare www.pcccmccccccocmm.coccmm.mmoee iance
climate e. wwwaange.dappatee.... on
global. strateggy planning
inst rust ional wwwiiinaaaaatteeaa .materiallssss.....m.....
performance Lechniques
health po 1 apolicy_aaee cyvaccccyyy
climate waw.aataa....m. anilysis
performance art issrr
wederrcare.knooee enge edge
amotional 1. regulation ion
strategic thinking ing
financial modemoomllin ing
Financial. rew.....eriiinnng
historical rresearch
financial analysis
augmen Sesmentted.iee augmented.
air. ww..fiter.com change
spectral.s aallannalsiii
audience wwtpcceeaaallay analysis
cerami cs spottery
budget_ wudget.creation ion
policy ey ayylllsllssssss...
database design gn
jewelry makiing
visual design gn
moral_com couraae
using.matricen
api.integrate
Skills
51

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Published as a conference paper at ICLR 2025
community stakeholdde der engagement
Table 52: (Part 5 of 18) 1143 skills extracted from topics in Tables 338 40 and 42
aneleefficien canccyoppimizeeiee imi zation
seismi aaniccaaaaa lysis surrveycconsruuet ruuutioon di iifferentiatedd eat.aatiinttseteeeeeieeee on
technology aand. wew.innovaaiin ion www.ooriinn.coopeeee......mm
wat qwwlity asw.esseeentt
financial risk assessment composit it ion desi gn
milex-nuubbers
cemmunicctiion
research.nee wwwtthhoooollaaeooo
cover letter crafting
wildlife conservation
star identificat.io focus enhancement
www..eeeeeaaaliiioo...e.. livaiio in
inaative..e learniing
social www.rrsssiisiiiiio..
Mistoricall llaallaliissss
chronologice woogical.reeeooooo..........o..e
nutriti perriiiinal.. www.iiioaa.cconse....
h identifiiatiiii
criti ieltiialllae eritical.thinking w.i.hiiiiinng
exper imental wwt.naal.deiiigge ddssign
incrurat
waste management
weeelitt..commne
mental aentaa
sisease.ddagnnooe
vi ut_ fi-ux_desiis desiignsgnnn
out come.evva .eweeeeaalll
mol wolddmaking ing
vi risual.azz"
wnw.aandddddeeeeeeeeeee response
behaviora aorallrlli. wtw.iorallnnnn........eeam.ae intervaniion strategy
tnaeeennnna.....eeee nuances
feck-identiffcaae opt imi zat ion
quality www.oooooooo....o in tettexiiles ile
emergency pr www.pppppppsss..e.e.p.p and
developpennt
art art-port wwwppoootoooooooooo wilioooooo management
marketing ing. www.aandd.ooo..... promotioon ions
dynamidynamic cs analysis
salary negot iirtiiatin ion computer s simulation on inwironmentaleenn leducatioon
educati cration. ion. ana.annleeee learning
maintenance and repair
skill.drill executioo
ecosys tem management
fitness passs-routt rout inee ieattiieeeeeeeeeeeeee
digital proficiency
brand sttorytelling
dietary-adage addaytadattiinn
electrical ceallall.. aeeallwwriiie wiring
themat i c c-anallysii lysiss
chemical synthesis
bronze casting ing
lesson.pl Lanning
grocery www.pshhpngggg...
@rop_managemeen
fabric caaanllsiis
seript-wrii wet.wwriii wwriting.
grant wri writing tiniing
market. reseearch
map map- readreeaiing
Skills
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Published as a conference paper at ICLR 2025
constructions technologyy www.innnttati... ion
mergers and acqui iequusstiinn ionss strategy
lopment.insightt intiiinnihhh
susta nnble land use p annin ng
Table 54: (Part of 18) 1143 skills extracted from topics in Tables 338 40 and 42
Lechnica ical 1 skill enhancement
zoning renulattioss ions www..ccmplpnnncc...
space weeahher.forecase ing
calculate caloric intake
dance and movement a gleebra ic mani penituuiooon budget-pa anning personal ..r.adinnnn iaaracter.analyyee
merformant accennee.cc eee.eavaaioonn
coordinati in agility
resource nageegeent
characters err wwwraactereeeeeeeeeeee www..deeelloeer...e
serategic.alloattinn
attention ion. to detail
spect www.:oooccc.caaa.......
digital marrettin architece wewtttteetteeeeeeeeeeeeeeeeeeeee
wrokiiggtecchiiuue
Petail.ateente inn
technical technical.wiith www.icawwwwiinnn
ethical guidance
artistic selistic.exxe aetistic.exprest
media media.analyee analysis
anit.testing
geophysich Sepkysicall
body body
pub
tontetttss
on-innnuuuuuu multmmlllllleeo www.iiiiutuural....omm contexts
wwwpoooo.mmeeeeeeeeememmm implementation
fundraising.e weiiingttattggeeee deve 1 oppeet
development project management
crypto. al prorithm. applacatttttt... on
ww..aaaagemmnt
sustainabil iamailllty ility integraliion
behaviora wwt.vioraattatiii. training methods
wwwoorreeatiionsee at www.ioosssssaaaaeeeeeeeeemmeeeee
carbon. www.ooooonntttalllsss.....e
operational efficiency
mentorshie and coaching
green energy so lutioons
physical expressioon inss
social www.aammedia.... aaaii..uureach
confli ct www.rssllliiooo
telescope. opeerttion
informat ww..onnnteeee
negotiat attiiiiii negotiation.sie itt.n.ssiiiiii skikkl
speech. recognitiionn ieriiiion.cee
argument deveelomeene
wwliic.halthhanaaie close-reading
event www..oooreeaa.... rin
vanitation.proto.n
test- prst-preparation
iigital.liieraet
gadget reviews
choking remedy
resume wwitingiinngg
language peruuageereaccie
problem.solving
anima 1 healt thth
Skills
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Published as a conference paper at ICLR 2025
phi lalllanthhe lanthropp www.hhooo-ddd.oo...o wwnd_oooo-rorriio. irstttttttttttooor os
Lementation
weweeoopmeppe nt. coteeettpplaannn pplaaninnnngg
91 global conflicts real estate and urban p planning dat abaseeae.nin manggemnn Tatabase-manageenn resign_theol
adapt ive www.neaarnnnnteeeeeeee hwwpiinngteehniigine ques
ques
Table 56: (Part of 18) 1143 skills extracted from topics in Tables 338 40 and 42
proficiency jewe! ry mak ing techn quess
versi iission on www.ccootollnnn.cc memane
geotechnical engineering
improvisatt wwwatiiontetee wwwatiionntciiiine iqqes
security security..o www.mmmetaaaa..mmmmmmmmmm in
trading wtt.ssttttggiiee.... es. iimp
phone pronetich iic. tetttarnneiiiii iereriptioo
sociologicas analysis
system admi adminiss ni meliinnsttaan stssstratiin ion on
pronunciat inn.ationaatt lon.accrracy
forei i gn. 1 Language pro
aut automotion ive. wew.ineerinnnnnnneeemom
el classroom management landscape design
navi navigatinn on expertise
putrition.educar ion
analytics es monitoring
Murgical.teclin wetteehhniiuue
bustainable wwwaableeeeeeeeeee wedeeeeeeoooeee
persuasivee vie wev.wwiiiingggggeg
aerical.reportaa
story ppitching
emerging tech
dental.com carre
oil change
socioling inguistic survey conduct in
wetry-nnterreetete in
ast ast ron and space expl explorase ooraiiiion on
script.anae www.sis.and.inerrreeeeeee.
dance techni que improvement
project_fi wanniannnoooooooooaaaaaaaaoaaala mal.mmodeiin ing cost une um meg.mannnngenttt..mmmmeemmmmmm..
data wetaaddrveendeciiioon connmmkinng
sereesssin.eee ng. des desiig ign
historica ooaoicaal preservation
austainabilisy weabiiitty.eee www.biiityypppootttoeeeeeeeeeeee retorrreeorinnngg
resource opt. ii-immiiaat immiizaiiion
user interface design
textual- mexinallintoo win.iieeeee interrreeation
narrative ive constructiog
voice_pro rectionntriimee
campai gn. management
iiterary-devicee weddviieeaapeee
ethics is and aad_mme www.morrltty....
software sebugging
statutory ory analysis
satellite. wee.immaeryybbe
historica cettoricllaoo ciiiaal wec.alllveeeteeecce Levents
parallel www.ppooossiii.........
climate eaanteeanalysii
brand management
change_mane management
content conntet.cceeatte .ww.tticciiioonoo
team caammaaaaggmmtn ement
market.t. t iming
patient_oo werieettccmmmne
goal_setting
fluid checks
basic
Skills
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Published as a conference paper at ICLR 2025
on-strategy
aut omot ive desi gn and wew.ddnamiicc...
envi environie weriirommetaat policyy. cyy analysis
chemi ca waw..raactttee werrecttiiittiiieeeeeeeii tett-y-eee -pr sprediee ction
cesttredd reductducttt. ion
analyzing series and sequences
Table 58: (Part 8 of 18) 1143 skills extracted from topics in Tables 338 40 and 42
ofn.of.symoollooo..o..ooooooooo. w.f..symolii...................a sm
renewable energy research
int international ietattionnllli. wtw.tiiiiallliiaaaia.. litigatioo
epi spidemion dedeimooooiiaa .ww.elnnngggg.......
physical shysical.fitne www.fiisstsuuuuueeeeeeeeeee routine
www.iinterrettttt ion
construction ion est iestimatiii on
wtt.aaaiinneeeeeeene.e.... enventi
ethnographic.reseee rennii.eeeearc.
quant seative.modeee ling
en i ronmentaal wwwpiiptptt............m..mme
strategies
logical reasoning
interpretat iaaaiioooooo Lon.oo.foooooooooooooooooooooooo
networking wing.iigg.... wetiinggstttteeeeeeeeeeeeeteee
capital www.uuuutitinn inn
thermal management
suicide prevention
di digital storytelling
weerrgnccy-reeparedee
platform... powttoor.sspeciiii.....
coetzy-performant
language wwn.geeexchanee
werbii.forminng...
elimate.cne waa.te..chaneee.
trend analysis
wommanitariart
ast aetoomical iial.
job_hunt ing
scriptural
set www.deiiiee gn gn
fiig.under-umee uncertainny
and reach
www.beeeeeegeeeeeeee menngee
educate eataateeooooorteeee- on control
cos.poimllnccee lincce
social percepti.. ieniionn-aee oonlanalysis
design www.eoottaliiiiioo.ccccccc lizatiion
animal www.ooiooonnnoooooo ing. techniguess
educatio educational ional programming
performance.mme won.itoniiiiniee cettorinn
factorin ing po Lnnoiias
real estate financins ing
adject ve adverb usage
compli anieennneaaaiee managementt
cultural interpretation
customer segmentattion style advi sory
radar technolooo cattolloyy-ie _use use communi
cerami w..rraftsmansh...
surgical procedures
invest.ment strategy
upeyelines weniing-a projects
ttyyttyyyyyy ty outreach
argument -anaaanalyy lysis
patient.on www.imtiiinnggggo..mm.......n.nt
decising ceriissonniiii oo mak wriiingunnn
animal di riagnssss
building www.ingn..eccoooooooeeeoe
voice. pro jection
story-struck www.yysstuuuuut....... wrructure.aee
risk -
style adaptadiiiii..c...
paper_craft: ing
geopoliti ielliiicaain
Food_and_bent
public
Skills
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Published as a conference paper at ICLR 2025
integrationn
impl ementatinn
coordinatio
internat ional Leade management
www.iiatiititoooooeooe wwtpppottooo pwotocol.......ooooo.oo..oo.oo.. wotoocooddesii.. w..eediiggn gn
performance wwweeieetisseeoee mnntrics.anaayse analysis
Table 60: (Part of 18) 1143 skills extracted from topics in Tables 338 40 and 42
iance
social media strategies wtt.mmminiiiii.siiii...
di higital Lethics iic wwa.aaaaggenn gement machine ma mwwntninnttmm.mmmmmmmmmmmmmmmmmm etenacce
compllaan
Interfail iafffaitthh itth www.cmmmniccco citicatii ion
di digital media management empathetic www.cmmmmiiccccccccccccmc....eee catuniccaiii aation
teach ing st strategie impl
empathet ic. c.understandinng
1 listening comprehension
law enforcement compl
finance and www.ecooomccoom cs
science. wew.e..noo.ee.oooooo..c.
wwweenetttiittiiiieeeeeeeeeeeeee setiiveeaae www.wiiinn
nut ri. rirrriiii ti ional advising
vi aissul_.coo www.ooominicaiii.mm.omoo
Linguist is ic.analysis erisis selssssmaann s.managemmnn
home decor www.craaatt...... traffingg inng
canvas pww.pratpaaa.... oo
renewable.ert weleeeeeeeereeeeeeeeeeeeeeeeeeee seneerry-irre
fashion.2 cooonillumg wwl.luttaaaii
coucctiionl.teciin
land.use www.pplannnnng
communi caaiicciinnnnn saationnpee
genet ic.. diversiie
event pp aalaannnngg
tourism sm. t rends
Stage.presence
meal ppallplanning
argumentat at
seo reo_audit
www.vatticionoommm on
leadershi ip we.eeeelll lopeenteent
atmospheris c. modeloelli ing
textil textile_dede wew.eeeeeeiiieeee gning directoro or warlaallaa decisioos data analysis chemistrry semant e ana anaayaalsttl lysis
investigation wetatieeeeeeerron
legislan at ve vegotiation equipment .m intenance
emergency wweerenccyrespoone respondingg
humani camanitarian. wtw.ttaarnnrrssieeeeeeee response
paleontolog www.onooooocall.cc.aca...
recovery recovezy-operatte wor--oppaaiion ions
market ing st sartegy
Sealth-and_medd mediciin
policy ey policy-formelat www.fooraaaii.... ion
vision formall formllttin lationn
research des ign music i performance
wwtprroiitggveerrooee silm.critiqe que
medical.red wwwaa.rseearro ch
dietary eettary_anally. analysis
dat aataaman management
roding-profict.
iegotiatiin weiiing-ee
cust customer onttomer-see wwwoomeseereee
charity pearitt-marken
legal Sigallree reseerseecl
Perse.craftin
note Laking
soil.testing
Skills
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Published as a conference paper at ICLR 2025
aut omot ive. safety sst attandaardd aandrdds www.coomoo...omo.ooooo..cco iance
wwt.or.ssttttatt.tt.....oe.m...e comptomateo wwm.atti.aa. ic.. relief
di differentic intrentiatee late similas lar ssy.pttmtt...mm...........m cms
Loo-andddaada aut omoti ve
42
belt-insse www.inssspetteeepppppp. aeplaceeent
public i health surveill llance ance
and
40
wew.ddiiig... gm
improvisati ional skills
so software documentatii semanti ie interpretati on
ethical.rea www.ssnninnninneeiiie.
compliance
cybersecurity. www.atiiaicssss
icalinference
fitness sitness-progr-s wwr-rrrrraan www.ssoorraa.deii...
volunteer. www.steerrcoorrnnnee www.:ooooddiaaaii..ooo...oo
www.curity.ppratti..m
Tables 38
graph ing functic ions codeLexecution
team com wawamncollaborrrroo laboratioon on
pri srintmakinn ing methods
wildl ife monitoring
allergy management
environmental aepnntallcoe weteenall.cmmp. www.occcolloo
pollution. Celuuiioncce contorooorroooooooooooooooo.....
apply fi atsssaiidffoo
brake replacement
di disaster. resporse
drawi hawinggTr techniques
music Leaching
statisti i cwa.llliniii
kitchen.cd safety
solar solar_panel. www.rpaaeeliiii
criticall.o weiiiical.readinn
volicy_advocer
transportat pappoortttii
verb www.b..tnnseccce
technical.ser
Table 62: (Part 10 of 18) 1143 skills extracted from topics
interdisen wwwddiscipinaaarr integration
aerospace engineering opment
citt ion
event www.ning..nn.nnn.nnmm....nnn.... and management
management..naayya. .naalssss
energy energy wwwp:/ffffffe..........ee www.eieeeeyuuggeeee...... upgrdess
game rattrttee wew.eeee-ddeeedeee wew.deeeelppeett 1opment
pes.applicon
iton
photographics wwt.eccooossii.c... iiiin
phpplyychhiin.o managemenn
gn.principles.
astrobiologe bolbloloolyyoooooooooooooo research
work life bal ance tips
audience_ eng.gemeentnnn
energy management.ce
reading www.:cceepeeeiioooooooomo
well fellsess counseling
plat form havigatio on syst emaae em www.aarcchttttttt
punctuat on ooo.ruuleeel www.euueeeee..e.
corpus compi lat i ion
Intercultural aelluurlllaae auurrllcoope
ast ography
pattern making
sports mam.aaa marketiinn
risk as aasesssmess sment
cultural-img wtt.urlllinnerreeee
decision on mak ing
lighting ingting.att dediigg
bill-drafe ing
stone.carving
mata-analysis
urban
Skills
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Published as a conference paper at ICLR 2025
emoti wwwoooaalinnellll.mme welllgeeeeeeeeeeee management
cul fultural competency deve lopment
data col collecti lection in and management
emot i ona 1 expression on mastering
Table 64: (Part 11 of 18) 1143 skills extracted from topics in Tables 38 40 and 42
seeensssforree forms
dip hopplmmati negotiat intion
cyber. Syberrssece www.seeuurree security-essentian
ionaaizzaiinn
werrciseeruutiiiee rouerrrutti www.rooutiieeeeeee..e. ine.designn exercise.te
titratic ionaationttoo on. techniechccc.e ques ques
child care expertise
fundamental maaamentall.. www.eeentaaaaaaaaaaaaaaaa Lanalysis
fashi on and textiles
www.sh.loo...orcss...... ing
emergency-planee paanniiig coomunity.educcee ion
revenue management
algorithm.ve wwwrithmwveee vi celnvvisuall iriisualizzi
structural. laaaraalaaaliiiis
parrative buiiiiii
ltt.aalssuuuuai .st udies
wound www.mmaaaaga.mmmmm management
ement
Docial.connection.
equity f minnniinnn
securit itttiityyppte hypothesis wwwsssssssssinnn content anallsiis
data data.visualizenn www.sssllaaaiii.....o...
moral reasoning
career.coaching
api devel opmeett
practicing peneticiin.polli
news news_writ wer.wwitii ing
cultural allll..
eco Friendly www.mialteaaa....mmmmmmmmm deve lopeent
wwlllettionnaane analyiis
technical cal proficiencyy
dietary-t wew.edadaalllll
interpret. food. 1 labels
home wrepairrbasie cs
debate_and sabateeandddiicussse scuasion
athical reasoning
www.interrretaaii.
raaiinng
moleculare weruular.aee www.mmidiinnng
kit citccheeequipi www.eguuiient..u.....mmmm
art wrt.history-an aaa.aaalllyl lsiis
cerrsalluuural....... tural www.:uurrrla.uuaa.....o...ommmae
woodwork in basics
behavi oral wwt.viorll.ouooo www..oouueeiinnn
disaterss
customer .engagement
fracture. wwt.ssaiaiiittttioo
waral.ttraaii triniing
moral phil osophy
aeshion.forecastee
empathy www.dveellppm..
athletie cchleticc.rrat certiicctraiinni
team leadershig
mineral.analy ysis
event analysia is
Food.preparatii
report writing
media literacy
tire cee.rotttionn
vorrect-sent seent
Nata.collectio
natural, latraaldiiiie a.d.diiaaee
poet ceatticcineeet
behavi or
cross sasss_culte
Skills
58

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Published as a conference paper at ICLR 2025
literature review and meta anal analysis yssss iss conversatic managemens
www.ddeiiss. siciision co.makking
internet www..hhinnsiinnggee....eeee ion
cogni tive behavior oral therapy
Table 66: (Part 12 of 18) 1143 skills extracted from topics in Tables 38 40 and 42
technologyyy habitat restorationn
www.aaappooodeeeeeeeeeeoeee www.eeure.executee........ extrueeie
www.lictreeoolu.iin.. sk illlss
susta nable eat ng pract
hww.nng.mmitttcneceeeeeeeee tcmoccptsss
mathemati.n cemmmticaall. iical modelling
algoritin wtt.thmoooo hom.ppttoooo..moommmooooom.oom.. imization
local. cuisine.explorate
meditati ve ve.ppactticss
data-sci www.ssceenne..aalll.....
commun i catt.sympt sympt mas.to.heeali teaealthaaeeaae hea..patcaeeeeeeeeeeeeee providers weweencyccrreppacc... digital wwwduainnniiii.. wiw.siinnnttiieeeeeee
strategic wnw.eggeeinneeemeerr..
impact assessment
nutritionallinf:
error debugging
epi wtt.emiooogiccal..o
strategics weatteeiipplannii.
applying www.iinn......immmmmmiiii..miiie www.lmmittcooo
electric. www.tri..eeeiilll..............e
music or criticism sm
fabric c.dyeing
legislative iitiieeaa
tax planning
color mimiiiiinng
surgi purgical.prome
study study.tips tips
sesisiiion cs for aut cmoti ve. parts
cemuury-iinrree religgio
ques
event_ wew.ttteechholooo wwwcchoooogy.iee wog..ttilliiooooooooooooo...o..t www.ttiiizaattta lizat ion
proup_dyname cs pssmmanagement
wtw.ccatio..oollloco. ques quess
www.aanniaiitttppccttt.. practiccss
sitiiiiiiioo sition
vaccine admini stratic on
phi poooppiccall.. iniiinnuiii inquiry-ie
corporate. tta.ep...ppp anning
hospitalifg marketing ing
pat. pat. lence cultivationn
resource_utill wwt.uuuiliaaiiin.
supply www.innnn.......... logioogiiiioo wwg.sttiiiiooooooooooooooooooooi
medi cat tiiii won.managemer..
fiber identificateee fifcctiioonnn
statistical model ing ing
crisis crisis-intent www.innneeeeniionoo on
statistical cal analysis
networking skills
mot motivation wet.vaaioll.oocccciii
ethical.1es rrhhiallleaadesa www.eedddersiii.
policy cy drafting fiingg
prectroscopic.terr
digital eeritalldessa. dddeiign en
onlin melline.aa anline.mate mar.etini
sustainabilitn
research chetiiccs
sound desi gn error handling
garment desi i gm
Lheme design in
anesthesia
Skills
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Published as a conference paper at ICLR 2025
apply popplynggtheer www.thh........... ic. formula
envir rnvironmental hew.th.ahhh............. asse
community sewlunittyhhee wtw.yeeettttttbbii....... cebiliiaaiinn on
corporate. www.soocilll...... wtw.:ee.oooaaa...s.s............ responsiiiis
farentiall ial equa ons
internations ionallliionn..noooom wwwpootaaiiontttch.ieo..
42
spiritudl tuatuulll 1alaalccoo www.ooooononnnnnee....... skills
crompliance
and
management
event. pp1 1al-ppaannn arlaannnng conninggfumn www.fuunndmnntaaaama
40
cybersecurity manalysiisss
partner wyw.chonniaaiii.. tion
www.compll.
risk.assessmtn wws.sassseemeett.a.. anallysis
express ive performance
www.selection
debugging algori algorithhm L.hms preci precision sion machining
nutritional Lanalysis
Table 68: (Part 13 of 18) 1143 skills extracted from topics in Tables 38
international wwwooonnaalaa.eeeeeeeeaaeaaae
rechnicall Rechnicaldrawing _.ddwdiainng
epidemi aedeemoooogee ology-ooooooooooooooooooooo wwwoolleeeeeeeeeeeeeeeeeeeeeeeee
protect ive wwoteetive.fiiishieee.eeeee www.ii.admnnistrrr.
art fact analysis
relationn ialationship-buii
versi persion.com www.roloooo.ooooooooo..oooooo...
time matemnenttntntttt.
cyber pnterssecurit..aee
creative wetattieeehiiiinnn
interview.prepars
wind www.turrbiiii..... wineeeeaee
educational wwtaational.oom
solving
internati looalonnorreeeolleeleoe relatii Lons andd global studies
learning env ironment. i..mm zatziaiio Lon
contextual hi mistorical ana analysis
wtw.liteeercyeeeeeeee.e...e Lopment
vehicle maintenn ienanneeee and repair
management
Soaveelopenn
wewrerreporttii ing
mealtheeguita www.nddaccess.ann.
narrative ive pacing control
progression
strategic ic..c....... communi communicaaiiin cation
diagnosis iis. wessss.ieenie ideet wwe.eiiiiiaaaa. ificationn
platform. -opt imi matiiiiin
wewreieeee.... design
prw.paredeeeeeeee
planninng
geospati a ana analysis lysis
seo opt imi zation
nutrition perriiiional ieoiooonlll.. www.iooallllaaiii..
shysical endurance
big dat handling
ment al_ we 1 Iness-guus
food wnw.annnnuuiittia..e.n ion
sculpting wig..ng.... mew.ohohh
spacecraft feeiiiggndggg
pelligious.literaeee
confli ct wwwesssiiee..eeeeeeeeeeeeeeeeeee
payload management
experience
trajectory or-iiiiignnn
werformancceanaalo
reenttreatment
user www.eexpeeenteee
disaster wwwsserrppppppp
route p llanning ng
track syspstpptooo.mm wwmptoomppp
carpentry work
ingredient.ce
religious wwgiouu...i.
teaching wechingssee
customer
Skills
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installationaar: www.ccontttuuu ructi on
42
and
www.liiinniiiiii.. stics
user experience evaluation
40
Lheme Sfentiffiattii ma mathemat iiaal model modeling ing
out butakka.eeepp www..resppssssttraaessee
www.lummmmmmmmmmmmmmmmmmmmmmm..e um. devel opment
troub troubless www.leeshiiinie wew.hootig.eeeeeeeeeeeeee
economi e www.ppolccy..........o.....ooc.p icy.anallsis
biodiver veddiverttyyy.o moitiinnnii
critical www.aaawwwwiiia.aiiaiiicccc
data awwaaiisllssii..is.lll.il.llsilm wtt.lllssssssssoooooosoessesss ronomy
38
genre ident if fication
dat abase managemen
envir wwvirromental. -ootiallppoiyy
software proficiency
Table 70: (Part of 18) 1143 skills extracted from topics in Tables
ethics.come comp iince
content sesnt-diist dissriibutii
graphin aaphhngg.a functionn ions
trend forecasting inggitnng
simpl www.lyyiiinnneeeeeeeeeee
machine _lleeeleee learning
creat intatiive ive writing
iifanccce
Languages.and.ii
health.p pr promotion
personal aesoona..fiannc.
energency.care
paint wnn.wwwlllllal.mmm
meaymmddeiing
ecologi cal
peer sser_support
fact_checking
curricul curiccllune
mapping
clay
geographic information ion sys Lems for resource
social diciallmedee www.eaeedaaa.... aai.aaanddiigee...... gitillttllllll. www.kaaaiiiinn ing
machine leaaning impl lementation
aeo.ciaiiteeeacciiiccc.... ins
cultural.et www.aaleetiiueetteeeee.eee seeeteelleanings
wttpecoonssttccccocoocccccot ruction
sociol i nggui stii ana ysis
det www.ddeeeriinnne nants
differentiation wlatinggfunctiioo
musical. camsical.comossiiiii www.pposiiiiioooooooooooooooooot severe sewere.weather www.tew...eeeeeeeeeeeeeeeeeeeeee resporss eegulatory.compl. ance ance
deve leweeoopng.teo weweinggtrannii straaninnggere
personal www.aaaaaarraaa...........
surveillance laance
unvestment aatttaadw aavii sorr
echniques ques
stress management eegeeeennt
palette management
oral-pe www.pppseettttie ion
cash flow analysis
feedback assessment
pet pet.care_ane www.ccare.abile. abilities ies
resour wal.orottallccaiii.oo.o
wwwrassepreventii..
ritual.and aniiysiss
enc-progr amming ing
paintings igg t eeeeecciiio
sports coaching Leachingga
disease surveill
sustainable.conee
managing-socle
marine-og marine-geology
yarn spinnininn
calculating.d
disease.pren
it.support
Skills
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deveelepeeeee lopmentt
sety-compliaate Liance
instr iestrumentat on and measurement
42
wwwrerr.ndd.roffese.. ceeiiionlli www.ionnnaiieeeeeeeeeeeeeeeeeeee wel.eddeelllllee
vet weterinary. www.carreccoooiiaaiioooo
intercul percull tural. communicattinn
global mobility management
ethical content. practices di digital des design ign 3d model 1 ing
Table 72: (Part 15 of 18) 1143 skills extracted from topics in Tables 38 40 and
efficiency opt ietimiztiio
t-metthdds
credit t. www.e. improvement
portfolio management source eva eval baat ion
floor flooring ing. installatiti illliiion ion digital.com wow.omiccaaiiin.oo.
appry.nnllsii
digital prototyping
transport. technol ogy
chromat toorphiricccooo wwwrppicc.methhheo logistics sesticssplaane
comparat viv hempprat..ve.aaallaaa.. ae..anlllssi...
health. www.ccoommnniiiiiic.cc
poi poissonmann gemeeennnt
Internat: international- iitonalllaa 1allaw
user wtt.entaeeeeeeseiiie........e.e.
logistics.planiing cs.planniing
video.product: ioo
dolabase.integraae
trave -pl anniing
ammergency-prepareee
gardening.basice
risk ww..manageeennt
voice ce control
music. music.thert www.eehhorr....o
innovat i oonnmmaa
emergency
financia aeneiallln
computat at i onal 1 linguist ics revelopment
wechnology.inteeeeeeeooe interraiion
health. eercheeduatiii www.ooooraammeeeelooooooooo
stress management techn ques
on
literary-t -iteraryytheory weary-thorry.ape. www.liipiiiioo ication
mariti ieiiiimmen paaviiaation... on-systems
policy www.ddeelllee opment www.eo.oaneaaaeneeeeeee
inddication.ed diaammiii in ieiiiissttttn strtiion
exercise se www.onttttttttaa.omeee he.ninnning
robot: cs and aut intomatioon in
monitor_ihe www.ttrrhyraaiiie intaatiooeeee...
fundrais s sing management
ietie care management.
vehicle el desieeiin gn analysis
inventon ory management
roboticn cs engg ineering
financial management
ameory.applice
waaappaappiccaa...m on
safety www.pppecctiiioo ions
recurs vee te.tthinih Miikking
inst rument p ellaying ing
demand for forecastinn ing
budget managgeenn
plumbil i ng bas ics
keyword_re werdddreeeerc..
a.g.biiitty
flavor cae--paarii wwwiig.iiiiggg ring
met welding
whalthheducatee
singing. wwrggigg-ablin
smart_hoo home_
eme omergency-re
Skills
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wnw.oonnnniio an imma health
sisk.assessmenn antttanddne man.ggmennee
42
and
ethical decision making material strength test ing appsehold.organii oorganizztionn reflect iefflectieejuuggeeee iv- jee-juddmeettt
alapppccaaiion ication
hospita cespitalitin l yltttalnttntttttttaaaaaaaaatttta Landtt ouritourisn im
environmental aaaeenntalassssseenn.
40
symbo combbolic.iae ic. ww..ineerretatiinn
nousekeeping.nme www.elnggmaagggmmmm...
wwwiimiitiiii. toon
communi cetttitiiii.c. ion skills
Table 74: (Part 16 of 18) 1143 skills extracted from topics Tables 38
clini Mlinical.dn cal wwwiiall.iagaaaen diagnosis
theoret ceaiiccllppee www.lacplll.pcecccccoo
literary, sttuiess
marketing ing promotion
publllllicc mublic.educatt ion.
hoth management
st stakeho 1 de aaalllaaiii
competit commpetitiveeaaee ivve iivvaaanlssii
lifestyle.che cel.cchccssss
property.va va luation
memozy-re www.wyereeifo.emem......
recovery support
physical fi Eitness
first. www.aaidaaaiinnn
analytica icallall thinking
confli conflict_re weliicttuo wwffficctresooooooo wtw.resoluttooo. Peript.analysee
debt mataggment
calorie. weteetracce racking
sleep www.luualataaaoeo..eeeeeeeiieatt
off.page.or wet.e.pt..imm.
problem. em.
Eor-noonpoofis
cust relas relationnal i management
www.ecrrica..wieee. wiiiig
film pelm.production ion www.aaaeeet....ooonnno.e..
www.eeatteeee.... aarrestuuu udy
iintegration
financia www.aaaaggee...mm wewanngeeentto.m www.for_noooe
career trans wrannitioonnnnooomiiiiiiiiiioiit tioceadvice
management
peanspprtttioonp... lnnning
sustainal aetainnae seiiinalee le agriculture
blockchaiin ww..dveellppooo lopment
portfolio optimization on
phi phi losophiccciallc writing
integrat integrating.fien ing functions
fundraist inn streeg
eart.huuke analysis
sary-expansion
protocol management
cultural competency
geaving.technii ques
cultural dynamic is
comparat ve wtw.eattieee.eeeeeeeeeeeeeeeeeee
training-t waaagrroouttt proutiness
comparat ive.study
cond_computing wew.iingiiiie
vocabu werabulary.een wwrppaaryyeppppp
brush.technis que
tour tour-manane wwa.r-annggemen ement.
legal Ladvi aaaaviiiiinn sing
quality.an wwllltyyconttct soy.ooorolll
home and ff family
debugging ing skills
installimg installingeeletteo
Lextual analysis
chemical.we wwwtwwatteeeeee
remote-se semote-sensiin se--ssssnng
cloud cloud
Skills
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conservation tioii.... wtw.tesrry...... pllannnnnnn
practiccssssss
sustainable pr.raatcisss
energy energy-effi wew.efffiioe ciiicii www.fiiieny...ccceeeee wechniniiiii ques s
grammar error identi fi cat on
eeedeeegyeeeeeeeee lopment
quant itatti attttiitiiiiiiiiii.iiiiiiiiittit vetive.eee www.deeiiiii. sionsioooon makinng
42
aant-anally Leaenttanalssii lysis
lopment
and
suppl ement advising a glorri tthm opt imiiiiii zation understandings www.iingeectorsspaceo
energy -st www..rrr.......iiii.o...o.o.o... lonnssttinnsso
design research methods
www.syystsseeeeee..eee desiing
health.educh www.hhhhhhuttiiiioo ieduuaatioondeet www.aaiioonneedeooooe...oe
phioospphy aadd sellreligtt linigion gioo
40
technology adaptation
Table 76: (Part 17 of 18) 1143 skills extracted from topics in Tables 38
resource scheduling ing
pattern recognition cloud. computino
weather Seaaher--reddhe predeerreiiii......... ptreiictoon on
interior designing
sports j-j Journalisss lism
waaaasscieenn seyysscien cieecee
Rechnicall technicalanall Lacalaanalysii
qualitatinn ive sestiieeddaee
data cowlectiionooooooooooo in
wwwage.preeence.dee
wlient.educat.o
art cew.crition aerriiiiiiiiii..........i..i.iii icism
editing editing.and.prre aad.dddddeer........
geothermal wtt.rmalllsssssse.
advocacy waryysstrtteeee
debt foot.financinn
Planetary.soo
waste reductrd inntiionse consstratee rategies pedagogi www.oggiall....o wot.eteettttt.coeeeeooooommoeo.. contentt knowledge
jaljjurnaaiin ssm
peographic.ino ininnforraaaal rmat. www.iooonsssnnne
benchmark Lng formance regressi on on ana lysis
disaster recovery, p planning
observing ing et iiquettee te rules
strati graphic correl attionn on
active wwt..ppasiveevvvee.ooooo..
media www.stsuueseeeeeee and aanddjjddduaaan
wwwttrumenttpooffciii proficienn
ions
int wttp:ret.symmtt..seeee.e.
integrityy itt..ty... gultivation
api design gn curatorial practices
packing.sfficl. ciency
welllxttyy.... aeiity-nnaal analysis
team. commumcoomniicccmmmm caticitioon ion
scient in wff.fwiiiiiii ing
aeograpicca.facts.
aut omated wew.teeddtettttii ing
sound Lengi inginnd neerinn
locati on ana analasaiiiii lysis
setting www.gddsecriptt.
climate siimate_moder aem.modelll. mrodeling ling
wtw.ssafety-precauue
speaki megakiin...e ng fluency
rextile.dest gn
mental mental.health hhalth
instrument
first.aid
Skills
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autonomous. www.wee.hiie...eeeeeeeeeeeeee..e mariggtiioonn on
42
human resources, mana mana gement.
public transport: pl plannin ing home budget management
siicatioon
www..crraiioo.. on
solar solar-panel.ie www.-ppaeeiisstttee.. instalttion
stakeholdes dee negotiation
risk-factor pecttorrion -iretddettiii iieetiffin ifi cointiiiiatio
color theory applicate ication
40
social www.mediiiaa.. markktiing
38
species.ff wwi.iniiiiiaaiiio
energy aand www.aand.rsssu.. resources
healthy www.eettinne paaiingadiice
negotiat ion. tactics
vaccinatinn ion.s www.sschhelleeecmm
elient.come cennniaaiion ion
sleep www.immmreeeet....mmmm
Tables
planetary selly-gellgyyy.oo
code opt imiiiiii zation
literary- www.cctiiioo iuque
whoreograahy.dessi...
market.are aat.aanalyiissss.
fashi fashion.be on con.bbraninggg
literary wetterryycriiie .eritiiiiii..
social www.a.ddddaaaa......
team bui building lding
reshionttrence
link buil lding
history wan.aandge...................
trip-planning
syntax.analysn
Table 78: (Part 18 of 18) 1143 skills extracted from topics
wwwaabeeaariccuttrreeee. ccss
wtw.innstttttiiiccm.c.. es
wwy.insssction. ion
epidemi C. www.brra..iivesiiggat.....
ems
_syst ems
user. eeppreeeeeee.eeeeeee lence. www.eeealaaiio....o ion
techni cal. www.ppooblee.m..........oo.....e solving
aewaarreuuiionn ions
security tt www.kisssssee assessment
creative ve rreative.problen solving
amotionall inteinnneeenneii gence
www.integrationn. on
i-productioo dialogue cra ft ing
susta i nab llllit lity pl alanning
commonicaation ciccatii ion applicati i Lonoseeee security
mindfulness, www.ttechniqqueee
data driven strategy
reservat ioiiion.saee won.hannlli.. ing
coding wallinganddddde and debugging Legislat,
ive ivve drafting
traffic.mand www.eaaaaageeett...mmme
pat in application
agile deiile.nei www.mmolooieee.......oooo
digital art deddeeiign
act wertiveelisteee wisseeiinnngggg
theme_expl onationioonnnnn
crisis communi
poet ic analysis
food.presentate
solving-lime www.-iinearreeee
cultural wettuurlladap..a.
technology.io perrinnterre
career-p wwreer-plant Maannnnn inn
electrical.saee
digital aiggiaalmuusei www.iuii......
Skills
65

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Queries where the user needs assistance in solving problem or herforming an action. This could be technical achnical support, troubleshooting ing, or guidance on personal issues.
ancce
This includes any query where the user is looking to find out facts, data, explanations, or learn about a topic..
Queries that specifically request detailed, step-by-steg instructions or procedural guidance. This category is designed to assist users in inderstanding and executing tasks by breaking them down into o sequential. manageable steps. Whether it's pract ical day-to- day task, a complex techni inmiical leal procedure, or creative ive process, the AI provides a clear, methodical approach to accomplishing specific objectives.
These are queries where the user is possibly looking for engagement. more than specific information in or tasks, This can include small talk generative ipteractions aimed at entertainmen or companionship.
Table 80: (Part of 18 Query/Task Types extracted from interactions with GPI-4- Turbo.
Description
Informati on- -eekinng ing
Conversations Linal
Instructio ionaa oonal
Help-Seeking
Query Type
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Queries where the user is interested in hearing stories, experiences, or detailed accounts of events. These can be aistorical, personal, or fictional.
Queries related to specific situations or scenarios that the user is facing, asking for tailored advice solutions.
Queries that assist the user in making decisions by evaluating options, risks, and benefits.
Queries that ask for interpretating of texts, s, artworks, or other cultural artifacts.
Queries where the user delegates the completion of a specific task or action Lo the AI. This involves the AT taking on roles that mi ght. require decision sinoonnkinng. processing, or interacting with other systems to achieve the desired outcome.
Queries that assist in planning or organi brganizing zing activities, events, or projects.
Table 82: (Part 2 5) 18 Query/Task Types extracted from interactions with MPT-4-TTurb.
Description
Decisi on- Makinn ng
interpretative
Task Execut on
Situational
Query Type
Narrative
Planning
67

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Published as a conference paper at ICLR 2025
Queries where the user is looking for specific, factual information ion or dat points. These queries are often strai forwwwardd and can be answered with concise response. The focus is on providing accurate, reliable information ion from trustworthy sources.
Queries focused on designing and strategizimg content for optimal communication across digital platforms This includes creating engaging design inss and coherent strategies for websites, blogs, podcasts, emails, and digital essays.
These queries involve comparing different items, ideas, or scenarios. Users often seek assistance in making decisions or underst andstanndig differences.
Queries where users directly ask for guidance on amplementing specific functions, features, or algorithms in a particular programmis language. These queries explict pplicitly request code snippets, examp les, or step by step instructions on how to implement the desired functionality.
Table 84: (Part 3 of 18 Query/Task Types extracted from interactions with GPT-4-Thine Turbo.
Description
Digital Communi cat on Design and Strategy
Code Generati on
Fact: Fact--Seek Seek ing
Comparate ive ve
Query Type
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Published as a conference paper at ICLR 2025
Queries where the user seeks assistance in finding and di diagnosing errors or buug in provided mat mater Aaterials. This could include syntactical errors in code, grammat.i cal mistakes in written text, or inconsi wriistnncie in data sets. The AI acts as di agnosticc ic tool to help pinpoint int and suggest oerrections for these issues.
Queries in which the user seeks a detailed expland iallaatiion or interpretatin of a provided Lext snippet. This could include literary analysis, code explande iollaaation, or any form of textual lissection. The AI acts as an analytical tool to help users understand complex pexx passages, technical descriptions, or conceptual writings.
Queries where users are looking for feedback on their ideas, creation ions, or opinions. This can be particul iuuulllyyyy relevant in educations ional, artistic, or professional contexts.
Table 86: (Part 4 5) 18 Query/Task Types extracted from interactions with GPT-4-Turbo
Description
Interpretive Explanation
Feedback -kk-eeeiinn ing
Error Detection on
Query Type
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These queries aim to clarify confusion or get more detailed information in about previouse oivilusly mentioned or understood topic. Users might need further explanation ion or a more refined underst anding of a comple complex lex issue.
Table 88: (Part 5 5) 18 Query/Task Types extracted from interactions with SPT--4Tuurbo
Description
Clarification See- Seeking ing
Query Type
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K.3 INSTRUCT-SKILLM LIST OF SKILLS AND QUERY TYPES (CLAUDE-3.) 3.5-SONNET)
Using the procedure detailed in Section 2.1 we extract 138 conversational topics and 13 query types from Claude-3.m Clande-..--oonnet. --nnne.. From the topics, get fine-grained list of 3,266 skills.
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iestainaailli practices devel nt
management
digital www.a..eeeaaaa..e.eeeeeeea.aaaee psychologi lollicalllllo cal behavior analysis
Lware deve lopment. practices
hospitalit tallttllllla.aallleala wetlaalttyssrrre wwweeiiiieeeee ce ma management
marketing ing. c.c.aaaaaii gn management
Table 90: (Part 3) 1.38 topics extracted from interactions with Maude-3.5-Somin
emerging.techin woy.togytttoo -ryddesds
emergency www.imallel.e....cmmm.mm..m.llla .response
financial 1 managemenaeenne...mmmmmm.mmm werent.spp seseeetttsssten
wwwsical.scieee reeeerrch
angineering.den gn. gw..etoodssssss....
www.teccnollogg
educational ionllllnll.. www.chhcinngg methods
sub ject. spe.ppecci........ fie.ttttt fic.tuttorrrr ing
wew.eeehhiqueeee quees
religious iousioous..ooo studies anal mallysss
natural resource man management
www.ppanninnnnooommnmnnnnann.nnn ng hods
travel-pl aollinnnnngssee st wwt.sttttiieesss
www.eaaaaggeeee emet emmet
net wetwork securit ity systems
economi c ma mkrt. ana analysis lysis
government wysstmmmnaalyaa.............
climate.cha saaaggeannalsii
literary art.saaasss.a.i............
ppace.explorat www.ttioontteeeeeoooeeo
vi visual.s. disuullarrt arts we..craation
mental_her wetaaahtteeaate
legal. legal.systemt wystemmamalyyee
academi c www.wiitii iitiingg.
business wwt.ssoooccllccoooooo..o...olla.
International.
personal. cornnal..final
nutrition.p
wnwraattuuttueeeeeee...... managemett
envi ronmenta conserves consevvatii i on methods
computer scie ccience and Lechnology data anal maalyssis methods
oos iess operat ions management
learning stt.strttt www.stttttyy.............. www.ddeeevoooee opment
ghilosophic iall theory analysis
theatrica ietricallpee ireatrica.perforn werforrmanennerrr a
www.reeeeeeeeeteeeeeeeemmmm anninn
1l lanning
alological.son weceenceeeeeerree
academi e. reseerreeeee www.mmethottte thods
ceooicyyddee cy lommet
-ol sllannnnn
civil rivil-right trigghts legiiiii reegsslatiin ion
energy system management
publi ic public.heall wwwalhh..aagggmeee.
fitness. satas.raaa wwaaiiinnmmethhee
bus business-Ha and.nndee wwo.omic...oooo
busi inees strategy-Pe 119y--lll
earth ence_reseerre
education iaaation oon and academia
society and humanities
lifestyle. anw.annddeee..e lensyyeonaanneeeoonnll personal
family-life wir-lifeemnn www.llffeeaaaaganeeeeeeeaae gem
career career.devel dewerddeeelooe opment -pl
professional -services
law_and govverneent
health-and and wnw.anndddiinn..
science_and_eee aerce_and_eedee eeeeeniin
arts arts-anddae and culture
unvironment www.eenttnnddddeeeeeeee
musical.arte www.caa..ss..a.......e.eeeeeeeea
mistorical wwl.caleeveee
compute ceneter-iniie
public ctliiicpollliee
urban.develen
Topies
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ceentpplane -planning
nonprofit, organization Zon management
manufacturing process opt imi mmization
mathemat tatttmmaaiial.......a. iiaal.ll... opt ceptimiii. imi zat i on methods
pequussiiioss iiononn www.oontttccng.e.e ques
wew.ssttecchiiii quees
materi verial lals engi neer ng ana lysis
www.deeeeeeennnne opment me thods
Table 92: (Part 2 3) 1.38 topics extracted from interactions with Claude-3.5 3.5-Snnnet
weggineeriiin ing ng
marit it imme transport opeeat ins
eeeemmopptie iemi zati on
Lopment waarryyanalssiii
ques www.eereiiioo...... on wwt.-poooooioooo on
building const ruct. ruct. ictiionn on me methods
industriall wal.llauuutto omat itaaiion ion.systent
agri piricuuturra tural. farming met
hhooyy -ana lysis
publi i c. www.ssppakigg... technichi ques
infrast www.urstccttrreeeeeeee www.ooootnn..o..m
supply y-chain.hopttt imi zation
wwcheetcc.cccchiiee methods
industr trsttrr rialppppe wal.product....mmmmmooooo selluctddeiin
fashion on accessory design
pure pure-matheeee hemamtttthtttttmmmmmmmmmmmmaaa iessiictttie theryy
team www.ssports.ecccieo gues
fashion on. den.ddiiigge wedeiignnnne wwwsignmmethaen gnnethodds
food_safers managgeent
statist ist itatistical ieaalcallaan ltt.iiialaaaaaaaaaaaaaaaaaaaaaaa www.laanniisss..aaaa
aut onton ive. www.syytttssee...
logisti wtw.sssscssssseeee.e..e
mi ailitaryy www.llinelllli
digital www..nnnttttcccccccccccooo
multimer wemeedda.cotteee
leadership www.deeelleeomoeeee
language acquisquuiss...
Film.producti
mi millitaryhhee
mi wallitary-aee
recreat
lingui snguiste
technica cal document ietimmnttaiiin at iionnnn..e writing ing
agri cul curerre ture and food production on food_process iesssinggtte weiiinggeccii
Logooooyyssssoooeoo syysssttemm ems
constructin and infrast. ruructt ure constructs i on project ma management
ical cal wwwemooeeee.ooooo www....deeeeeeeeeemmmmmmm.m do.eeeeooeennt
journalisme ss content production ion industry and manufacturing ing
safety_standee rds hethemati cs and_statt istics
transportation cionrattiio....o and wollogisttii st ics
language. tt ranssaal ranslatioon oon www.mmethoe thods
appliedd wellleedme welddmattaateeeeeeeeeeeeeeea ma www.maaaeeeee hematttt iimaticcs.. cs modt matae
trrininnig
aviati on system management
language and communi catmmniiaattitn cat ion
rext.ile le product ion methods interi
quality.cont rol.strollysten industria www.aaa.oouccc...ooooooooooooo.e .deiin sports ahorts_aadd.. www.annd..see.......
wwttechhniquee
sports sci ence. anaaysiss
public trattraan ans ettptppllaa planningg
securit security zy system, planning
agricult www.cuuutraattccoc. wec.oooeetooooo.o Logy
entert ainment aad media
video-game. deve Lopment
social aediimmedia www.aannnage.....
enterior_ate or www.sppaceeeee happpceedeiise.. desiign
professional coaasiionl... skills
indivi dual 1.aal. www.spprt.s................
military- -anddand... defense
aashionandddess- gn
lat
constructin ion.iin.... son.sonnnaaaeee
defense tetesseetee eeteens_ttchhnoe
Topies
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negotiations Clatiion.a rategy seveeeyddvveeeeeeeee _eve lopment
business communication me-thdds
Table 94: (Part 3 3) 3) 138 topics extracted from interactions with Claude-3.5 S-Sonnet
jot_devicen cee.ccemmaeee wtt.ppputiigngsstee.....
ems
artificial www.iiioiallineelleeeeeeee aalaalnnneneieeeeeeee
cloud www.uud..oo.tiingggg..eee..e.
iot
pro project ject, ww..maanaaeeennn wtw.aeeeetttttennggaa... teu
www.eeeeoomeeeeeee oppent
extended_red retanded_realltt-at.. wew.errraaattyeeeeeeeeeeeeeeeeee det.ttyyddeelllpmee... lo.meve
imme management.e...m wew.hniuueeeee quess
blockcha in. yst ww..eteeeeeeeeee.... em www.ldeeeeeee
digital-: echnologies
Topics
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wwr.modularitt larity eeetehhhhlogyy
lementation
scientific me method appl Leatiiioioon
quali www.cooorooooooo and assurance
Table 96: (Part of 52) 3266 skills extracted from topics in Tables 90 92, and 94
design sritiiqee facilitation
rendering and vi sualizatiin
des i gn for mass production ion
scienti iffiiiccc fic. www.cccmmniatt..mmmmmm caammniiaaiiiii ion
industrial product design
human, factors. www.eennnnneenneeeeeeeeeeeeeeeee ing
sthical.decis/ dee.idecc.... sdecissoon innn mankmaiinggg king
design for manufacturing
design www.innforrrrcclll..oo www.reecyclaaiiit...
network. ad ini rastrition
mathemat ical. mmodeling
computer rrraiaa www.aiddeddee dedde. dedddeiggn gn
user interfacce desi gn
research me .ww.hholloogy
Interdiscipl wwt.rriiisippiiiiy.ccoooooo
software. www.deeelloeee.. lopment
exper experiment. imental design
peer_revient ew.process
technica aachhnicalwwie l.writing
techni i ca drawing
www.iinformmodllalee.... design_ cern-for_mmn computer www.ssseeeeceee..eee
probl problem.son em. perplemmsllllll solving
systems thinking
algorit wwvorithmmae dannddiign
product_lifecy..
sesterial.seeeee
3d 3d_modelimg inn
ethical.
science and eng Lnneriinn data iallysia and interpretation ion perothesis_fooee aallaation ion laboratory.t
design wtw.gnnooosrrrreoerrreeeee servi ceaabiiiiiiiii biiiiiyy
anylysis
designe weriiigtthiiig nk ig.prcesss
design_ for for-cusrreeee ccustmoil zation
www.rryyeeechiigg.......c echniques ques
progranming rg and coding
deppeeeeeeloeme opmeent
sustainabill hwwaaaiiiilyyy. htt.:iaaaiiityyaalllllll...
computer aided design
design paa ent.eeerreeee. reseeress
desi gn for di sassembl ly
programming wea.iiinn..e ing Languages
statistical cal analysis
conceptual.ske l.s.tthhitt
design. for wwa.mmnnnaannnmmmmmmmmmmmmm.
dat abase management
pro projecttnnng project-managent me-.annnnngnnntt
inst www.ruunttcaaaibaaeee..
ergonomic c analysis
Pritical.thi -tttiiiinng
iiterature.rewt revveee
a.r.assseblll
rapid protooyppng
prototype.com wew.ddeelllee
data ructures
error aww.aaalllae yyss
risk.assessmen
grant_wril wetnnttwriiig
design for www.foooaasseeee
dat Data.visure wewviisuue
Skills
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www.ccstoome and et qquette
wempeementtioon
carrier ier parrier.select www.sseeeetiie..eeee iesstiionnnnoo and.andd.in io..an..eenoooiioo...i.....ooo.e www.onntiiiii iecggoiiattio i
points..ps.........ooooo... mnd mils
les
www.beeshottiii ing
seldeeseeectionn in
www.assseeeeeeetn..ne.e. iiltiiation ion
internat International. ionaaiionnll..mmmm wet.tiionntttttte..e trade. www.coollll.... liancee
analysis
research dest instiiiiinn options
Table 98: (Part 2 of 52) 3266 skills extracted from topics in Tables 90 92, and 94
y Loppee
resimpllent
gn.languages
natural, auuurrlll anguage-processee
cross docking operatio ions
hardware configuration.. iin
blockcha in devel opment
debugging. wwinnnaan..tte wtw.gin..and..uuu....... wwr.ttruulllee
mn-ppannnnng
warehouse wwtpsee.ppeaiiiiiooo.eieoe ions
cransportati www.iooooodeeeeeeeeeooeeoeeeeeee
cold chain management
create travel itinerar
eestemmarhhiie ure
agile_me me hhodllogies
route opt imi optimizat. iimiaation on
supply wwa.ccchnn.pppp. wat.yyhhhinppaaninn...
sustainable 1 wwwlooistttio stristii
wook_accomoodate inns oons
web devel Lopment e ouu computing winn
machine, paachine.learite lerrningn
marallel.cod www..ccoputiinn
demand forecastingg
shintent. tracking
transportation ioneiionccoommmm
navigate. www.aateeforr.....i......
optimize www.rrewarrd.d..
api api ieniinntttiion
organi eegganize.trane
vi cerruullrreaa
research
urement
last le del delivery ivery coordinat on samodal.transeee aarttaiin wettionnaaannnn cargo-in www.ooiinurncceeeee management wet.ork.desiise design
methhods
www.innnssu......mmm .needs
implementatiion
systsmmtmmmmmmmm.. em management
andoonnndddll.............d..d.. and logistiiss
logistics. iis.ccsp. wes.pprrfoo..e www.cceeeeee... watemmassueeee
reverse 1 Logistics handl Lng ing
travel pl planning ing st st.attegi weattegii les
i Meentify-tiit rave wtw.::aaaalnnaraaaaaaaa
Sybersecuriin itturity imp mobil le app deve opment
packagi oop.optiinn poptimiaat matizaaiin ion on
manage. www.ttaaeellll... wtw.ee..ogisiiiee.......... lol.looiisiiics.
arti ierifictt aefiiiiaiinnt www.inneellgeeee...
user- www.-innerffcce.. deniign
big d processing ing
invent ory management
compare transpooraae atttattii...
budget_tr www.trrppeeeee tripeeepeeeesss
plan-activii www.eiiiiiseeeeeaaeeeeeeee
devops practicite titiices
iot immp comentation
coantumccompur ing
freight.for forwarding
fleet mww.agemnnnttttt
cust ons_clmlcleeee carancce
logisti ics. www.snewtrr.......
pack.effi ly
comput eeteerrviie viiisiinn
version.contro
transportat parpoortattt taattatiion
load anningg
data-analysii
operating, ing.
Skills
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compare currency exchange opt ittiiion ons
wwc...cciiiiee.... optiios
conduct pwsppsyee psychotherapy .eessionss
performance metriettt...oo tracking
Table 100: (Part 3 52) 3266 skills extracted from topics in Tables 90 92 and 94
di diannose .eentta a diisorders
coordinate group travel
fashi on show www.pprduuciooo ion
mental contal_heall hheehlltt www.hheehhthttt wallthhtrtaaate thetrattment
risk.assessment -attaaannnnn midmmmms
serressiinal... onnll www..seevicee
strategic c. c.cossuiiiingg
process opt imi zation
regul regulatoriy at www.-ccmllannce
fashi on illustration in
research- www.archhhocal..iiii....
anahionn_aae on and design
garment constructionn
draping www.nngtthiiuu.eeeee queq
fashi on. photography costume wed.ddsign ign
quality.aaee aassuance
writing
fashi ontiiinssyllllll snn.stylinng llinggg fashi hi on. marketing
proposal writing
upcyclinee wel.ingggecnniii
time mww.aemenggeen.......ee..m......
team leadershig
fashi i on writi rriting
project hwwpjjectplaannii
patte en. m..maiing
fashi iishon on buyinu
market.researt
professi
rechnicall
fashi
wtd.laafettyyeeceeec setty--reaaution.. ions
client- relat iantiiisiippiio mat.aaooooiiiiaaaaganeeeeeeeee management
practiicss
research visa requi rementss
w...aaggeeeetttttt fashion www.onnussinnennineeee
assess www.mmeetla..ememm www.helltttt status
fashion hist sisryrry. analysis brand ddntti i ty creatian on visual visual.merchenr www.merchnniiiii.
stakehol der communication
client- www.neede....m aes.asssssseen.. smentt
wolor.theozy.eo wolorr.heory..pppiccat...... www.aappppiaiiooo
collection.deer devel ftereloppn lopment
develop-t rea tment.pl plans
indust ry sspeepiiiee. www.ssscc.c.eeppeee.e..
plan for emer imerggnncees
create_backe www.bbacckppppppp Panns
plan_for www.hhaatttt and
contract www.t iation
confli ct resolution
ollocation
sustainable welleedesiggn seesiig-ppp
fashion.cad www.mooeiiiii ling
www.fforheaatthhcm
business analysii
financiaa ..mdddddinng
agement
www.annalyis..an...
presentatio wat.taatiissiiiiii
problem solving
resource www.rrree.laccaaaaa.oooooooo...a
fabric sel eeleciion
trend Forecastiin ing
digital.desi gn.
accessory.dent desiiiing
pextile.dest gn
change_men saageemmaaacee
Skills
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imp implement eemet cognitive behavioral therapy apply -mi wolyymindee www.llnsss.techheeee
impl implement eeneeteet.... t.rreappseeeee.. lapaseeepp www.ppeeeeee..eee.. inreetiion ion s rategies
Lassessment
design behav vehhavier vor modi cin.iiinnnni..ico..ooooo plans
wwwlyye.decisiie max wiiiinn.... propeessssssss
asialysis
assess. aasess.develon dewedeeele.. www..ddeeeeeeeeeeeeeeeeeeee www.llpppeaaa...mmmmmmm.m.m.. milestones
bus businese iness consulting liin pract ices
Table 102: (Part 4 52) 3266 skills extracted from topics in Tables 90 92 and 94
ques perform.pp perform.psy psy www.ohooool.l.sseeeeeeoeeoooeeee
wwl.inteeviie...
interpret psychomet.r data assess_ment wwt.sssmeenalll.mmmmmm.m hesataallhelhhhsstttttae. atuus
monit tor pat. ient progress
apply-trauma inf.rmed.ccarr
business case development
ident ify eemot cemmalttriggen
manage-patien ienattient.ee sscodr
.records
organizat zzaz aaaaional..... cooiooall... _design
cost benefit www.eeittaaallssee..e analysis
negotiat ioooiiattonnt... monttechnii echni ques
psychologis ogi ca www.beeeiioo..
interpret wpw.chooogicccca.cocooooooo.
analyze.group stayzeeggrouup.dymmi...... www.mddmmmi...mm cs
intiiiaiioon
financial innd
conduct www.wieraaa.a.............aaa...
pro project ject. management
process.ope .optt.tpptt...... wwt.eeass..piimiiit.......
client_red www.et.eeeatinnnii.o.
scenario plaaning
probl b em sollooiing
risk aelaasssssme sment
aewaalttechii... ques interpret body wan.uagggggeee
preserine tbe psychiatric medicati ciciiiinnnn provide crisis intervention inn
utilize wwwlizzemotiine sereemotivatt iiat ionall 1. w...nneevieenn ewing
infnvences
ident identify fy behavioral reinforcers
int.saaiionttttoo constrategy
implement www.nionniii.ooo....o.....ooooee cognitive iive www.beeeaaiaaaeee
www.heatthhe whealtharee.a
educate pateeppaiiie pat.enttentttte.iee ient s-and. families
practi ce cul cuwtururrr tural. competence
www.reennnnneiiin
analyze yze envi ronmental inf
conduct conduct.functle www.ffunnntnaabee.ee.eeooo.maaae ionallonaaaae www.iooo....ooooo...e.eee.
avaluate social Interactions
pralyzs.cognniiire. ve.pptteess
cattuniccaiin cat ion
wer.vieeiiise. lislistening
apply www.:bbhaaiiaaatta..... Ltheorre
performance measurement
recogni iigniz eelizeepeeeee peersonality.tee
collaborate wi with thth www.whhatttteeeeeee
conduct ri 1ak assessments
observe wwweeveenonverbaa.... cues
stakeholde lerrddrrr.. www.onimmcnc....mmmmm..m........
business wwwcess.....oo..oo....ooo....... wwoceess.reeneeeeeeeee
industry bbenchharkingg
facilitate www.ate.grr...tteeeeeo.o
Iresentation.sie skills
strategic.pp anning
change man eeent
emnt
trend forecasting ing
market_rese res.archhhchhhh
swot analysis
celacice_actioo
data.analysis
compet irreeitiven
Skills
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www.mmiieesaaeeeeiiiii.e protocols
www.erreglaiiiio.... Illaaiions ions
phi mmilosophical cal. jargon. Lranslar connslatiinn
Table 104: (Part 5 52) 3266 skills extracted from topics in Tables 90 92 and 94
ceenommmooogicca calllal... descciiption
film productiion techn ques 1 lighting ing setup veript.writin set_desiga
philosophic werophiccll.. cal. ahheory-anllye
marit Sirittin iime transport operat i
hi historical. cal wow.ll.cttttttaizaeetttt..
inn ing
green-screen scr serren.ffe wereeefffliminn filming
aerial Serial.drone.fii frroon_.fiimiin fiffiiingg
foley sound creattiitiooo ion
conceptual analysis
iil.inuuiiyy
Lextual interpretation.
phi phi losophi cal writing
premi wemi.seeiiee dee.ideetii contiifiaate ification
sy syllogi wmllooiimmccommmmmmmmmmmmm.....o sm, www.cconssuutiiioo
axiologi cal cww.iiialllalaaaaaaaaaaaaaa iion
manage.cres www.crew.... www.oppettion.... lons
compl comply.wil y.w.... th. maritt iime
logical logical.reasonie reasoning
plan wlanvvoyage.ee routes
waikeeppppppicttii. vi rideo editing
prop management sound folundmixx.e m1 wilmmiiinnng....
compossiiin
impl wwweemeent.iom www.leeeeetttittteeeeeeee
m metaphys s wesssicalliii
phi mmllospphical wwwehiicall...
shot wwwposciocccoo.cc.coo
evaluate cul tural influences on behavior camera operation
cross cultural philosophy con
wwf.fffffsst...... www.ctss.mmllemmm impl wemimmmmemeet.. aeieettetiiioo on
www.-oaain.nnnoaddiii... unloading
phi phil losophishhho caal debate mode maataai
thought exper iment creation on
comparat ve aee.analysis ethical. www.reasonn... epistemo 1 ogical assessment
manage m www.wweetaeteteteeeeeeeeeeeeeeee her-beereeeee teennss
mot.onn notion_captun paature-techhii quess
conduct. www.eelllll..... maintenance
cinematogn tographh pl planingg
post www.-prouucciiic ion. workflow
dialecti wnreetical.reee pariialleeessso reasoninn
avallanalysis
digital www.coomos....cc.c. prrosiiiin iting
argument eva luation
handle mmrritii Tmme eee
Nlorybaardccrattion
paradi www.siiftreeeee.oooo
location on scouting
fallacy falllacy.ettect welyyddeeeciiin ion
budger managgaennnntn
critical.tht w.t.hhhiiinngg
navigate_vent es
sound recording
act co-orriieect wwr.ddireciine ing
interpret htwpppeettttttteeeeeeeeeeeeeeeee www.wwttttttt
hermeneut wwtaaicalllaae
conduct.vessee.
prstume.desii
special special.effece
color.grading
ont ontological calllicll..o
Skills
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implement verviettic ical. farmi ng- wew.heehnncem ques
sogricuuuure
develop predi predicti itive ve. www.pcopp...o......o......oe.. yield models
www.cc.oooooooiooooooooooooooo ing systems manage li www.
www.iiuuttrraattccnooo.. tec.nologgg...y.. sssttems agricultural
emte
ware
ietation.iie on wwwiiionnnnsstt.. insiinttttentt
analyze.soil www....ccossiiccomooooooom.....a t ciooonddat dat
loppeetet
Table 106: (Part 6 52) 3266 skills extracted from topics in Tables 90 92 and 94
manage mamage-irrion iew.irriiie wriiiriiiionnnnn gat ion aut.auttoo www.iionnuttattiiiioooooo aottmtiion
data collectiin eectionn and analysis
manage fuel consumpti couuutiiionnnnno.o iion coordinate port operationn ions www.ttorreessslleeeeeeeeeee
eeuuuutnniaaat.. ion
design wtt.ddevieeeiinaaee.e
fitness.f ra tet.trainnn ieaiinnnngnnn me hods
iamiiaaiion....... aechnigques
assumpt on making parameter.eae r.est iimaiiiionnn dimensi Aemmensional.... ional Lanalysis simul atmulationn iiin iei.oonddiiii design model. vavlviii lidationn
computer programmt rrogramming
mathematical modee.
literature review
technice ciacaaal loall.we lwriting
mnceetaintty yewaiiitttuuanttiiim
scenario.anee anaallsiis
operate marate.navigat..
program. f www.farrrmaanaae
perform.ss .
wtw.iiroonenttal.a...... ect i ieauueeress
interpret satellite lilee. imm immimaagea.. gery for www.rrrppp..ooo health
applicatiin
maintain aut ooaomdaaeeeoo harvest ing ing machinery
farmirrrrr ng lequipment
impl implen eement crop mnoontorine systems
operata commun wwm.iiatiiii iitiionn on equi poeen.
www.uscurrtty... schecks
larallddooee .drone .data
numeri ca waw.aaalmttheeeeeeeeee www.hhds..dttt.eoommmmmmm imp.eeetttmmminionnnn in
interdiscipt wtwp:sscppiaaac.lllllaiooo labborration
halibrate. aer.eagrrico aeggiculttnn cull tettttalllsit... www.senssss wensors ors
wtw.:noorrruupuppp supp
configure gps guided.e.. taactorr
perriliierr
troubleshoot. sww.tmsaaa...................... aeaartffamme www.faaminngi..
implement www.kkinggprrcddzreeee
impl implement eeeeementtmmmm bbl www.hain..oorr.oo..a.a.oo
perforn ri iksk asses assessments.
variable i dent fi cation
design wo www.tut.kkooo.ooo proogram
problem fo formulation smwatdepest.cee
opt iirimiize ze preci werision.ferre
itatistican tiall.ll... inference
model_s l_s wws.smmpiiii.. campliifiaato williiiio ciiiiini
sensitivins iesssiiiittyy.i analysis
data vi suallslii silssuuaaaaai sizztiion
model_call iel.lllaaaaaaaaaaaaaaaaaaaaaalla iibiaiiiionn ion
conduct wwwittiiii............a www.iiimm.......mmmmmmmmmmmm timiiime...
ppt.imizen ww-.ieeeeegreeeeeeeeeeeeeeee
desi intttnutut coaooaaeeeioommooo
mode weaeel.douuentttttoem
model model.comppl www.compariiiic ison
wirritii ime.
execute wew.dockiii
aeror-anallsse
manage_mt
operate
analyze
Skills
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Published as a conference paper at ICLR 2025
ques
reconnaissane seanceenneeaaaeeeeeeeeee and.eeadaaaseeeeeeeeeeeeee survei surreilllars inceece
weageencennallyae analysis
mi military wwwrr.eeuiipii pment wwwppptaientteeeeeeeemeeememeeee wtt.mmaintetaaa enance
cross cultural. www.unoonnn.n..mmm..m catiion
Table 108: (Part 7 52) 3266 skills extracted from topics in Tables 90 92 and 94
comparative ive rellreligg weliiiin..... gi..sssuuyyy ssudy
wneesstanceeeeieeeeeee bands
lead lead.gred group fitness.clll caassess
analyze movement patterns
social. wtw.llthherr..pppiicoo. ion
sociological www.ll.bseeatt... ion
psychologica woggicallaeee waw..asssssseeeeee sment
pntappiccaiinn
bal commun crmmuniicatio catioon
Gemonstrate proper form monitor heart rate
literary www.inneeeeeee... in
mi military www.-nnnellllin intelyiiennnnnneeeeeeeeee wewtteeligenneeeeee
leadershi, ip and command
apply ric.ttettttttaiiiii.......
human. ri. rev...iigtt werightt.... seadtssddvee w.w.ighttttt.accc ssadvocacy
ethnographic writing
incozporate wodywelwwwweeee.
linguist isttissss linguistic..aalyyle itticaallysis
milit military.de aalitary.diplomm diplomacc
practicc ice. www.innneraal...
archi val research
Pactical.p planning
ethical.read rra.caiiieesonnn.
naval havigatin ion
combat .strategy
Sender_studies.ee
writical.thinke
atilize.r wet.eerreiiie.
Implement-pee
execure Parrdovasculle routines
provide mot vat ional l cww.hiccccggg.
www.ssssssssssee sments
explain in. www.uttiiiiiii it iontionnoon principless
philosople wwrossphicco iealllll.. rgwuentatiionn in
art wit.hhistrry.iteee erpret aetioniion
adjust training intensity
qualitatin ive ve. data analysis
anthropol anthropological logical.lll.....oo www.feelleeee www.ewwwwrrrrrr dwork
Political. system analysis
Nemographic.ts wwr.bic.trend... wnn.anallssii
physical fitness training ng
military-I 1 aw_ www.interrretaaiin
emergency www.ymeeicaaa. aaiicllrresse cal.resoonss
www.erteeerrrro serroiia morrism.teee smm tactics ics
social policy evaluation
aerial. wwwbattaatt............m........ wwn.uverinnii...mm.mmmm
waaleey-and_humaan tiies
oral hi otooyy collection
military -adarr-andd.ce and defense
modi Ey- fy.eeyyeeeee www.eeerreeseee www.ises..oor.
log iisisiiss ics management
conduct www.ffitttsssseeee
cultural.ana -aaralllaaaii lysis
bistorical. waaaarrrsearce
medialliteracy wtw.fiiccteesottii...o
threat-de assessment
weapon handl ing
crisis sesissmanageemenn
operate gww.gymmm.....m
counter.terss
Skills
81

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Published as a conference paper at ICLR 2025
financial.repo wwwppporinng..... ing. and compliance
audit pwwptrppeaaiaiicoc ion won.anddde coordination
mi lillarryy recruitment and training
wwwannnnnnnnnannmm Forecast ing
waw.mmaaaaeemeee. agement
financia emett.eeet...m wwt.t--pprrraiiio.
wwp.ral.srructuee.o iww.ptiiiiiii.......mmm..m.i imi zation
argument. wwnstructiio
Table 10: (Part 8 52) 3266 skills extracted from topics in Tables 90 92 and 94
cost.cont www.contoooll.ooooooo.. rol.andd.ddd.. opt oet.opiiiiim iimiiiiii zatiion
engeraatioo
military budget. allocaLion
mi miltaryyhhi www.sssttttyana.. aasiillsss
financial decisiccinn on suppport
financial aahancialsyssenii system.mmmm.m......mm...m....... te int westtmmiinnggaatii..
hi mistorical cal event. analysis
son
sisk.assessment wwt.eettttnnnnnnneeeeeeee www.ttaannaaaaaeaaaaaaaaaaaaaaaa
performance evaluation ion
chronologi ca ordering
cont exateet www..iirreeett.....
cross. cultural www.pcorirc........m
emot www.onnnnnnnnnnneeeeeeeeeeeeelle
bias ieetiddeiiff inrrrfiiaaiinnn ion
probl 1 em em.solving change menene
critical.tt toilihiinn public.speaki.n
feedback deeee..dllll.. deliveezy
active.li wel.eelisseee Listeiing
Eerrategc.thhinki
vision.cast cast ing
Ponflict.ren
historical cearricllleoo
budget p
accounts payable and. receivab vabivall le management
www.oormaeeemmm.m www.erettssttt..m.m sesiicstrrre racking
nomanngeeenn financial wofttartte impl ementati on
psycho loololooiino ical warfare Lechni Lechniques ques defense policy formul lat ioon
opment
impl Lementation
leadership weshpp... dedeeeell lopment methods
wettarr-teccoooooe -eveel financial www..maaggeeeet.sggggg ems financial. data.analysis
wwwooeessaase omat ion ion
cross_cul fural.ccomuu wewnnicaiii ion
historica iariilcalll aat.iaiaaaaaaaaaaaaaaaaaaaaaaa.a data..aaaa.....aa..aa....aaa.a.. a.viiiiii.. susllsultttttiaass iatiiin
tax.planning and comp 1 iance
primary primary.source www.eeeeeminiiiiii.ioooii natiiaaiinnn ion
historiographi cal. analysis
www.eeffetttaaalsiii. analysis
goal setting coachi ng and mentoring
organizationn iioal Liional skills source eva luat in Sruueeand_effede
comparat ive i llyissss
eash fash-flows flow wwm.iooiinnnnnooomooomoommoomiii
treasury management
ethical ethical.decisi www.diiiiin.aa....c...
internal www..cooo....o....oo.oooo.o.....
mot motiva vitiiiaiiii intatiigretee
stress manageeenn
financia aallall-errrf wwl.perfforee
deci decision, siosnnioonne solmmmiinn ing
time meeeeaanngeentttt
financial laciaalpproce
team wuibuuiii... lding
investment
effect ve.
Skills
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Published as a conference paper at ICLR 2025
prescri ibe appropria aaee medicati onns ons www.:/oudddd........mmmmm www.tareeeeeeeeeeeeeeeeeeeeeeeee
ad jajust.ff .tst.rree rra tment. ppl plans.......mmmm as needed
apply wwrr-infeetiii wwfffeeciinn... on cww.connttt ro meassurs engineerinn ing .eededii gn monds requi rement ana lysis design eereeertiiin innn y-wallittttssteeeeeeee
historical narrative creatic oo long term impact assessment ceppiicatiii on
wwt.-ppprsppttiieeeeeeeeeeeeeeee ive.tiiveeteee aew.aatttteetttiin....... aking
interpret t.medii caallcaleeeeeeeee -et-tttttteeee resultss provi
de de eme rgency medi cal care
Table 112: (Part 9 52) 3266 skills extracted from topics in Tables 90 92 and 94
wereeeechhi.. quess
desi revi revier eww Dacilitation on
diagnose medimmiicaaa wow..iconiiioonn
perform non.omomooooooooooooooooooo. non www.ssieeprooeeeeeeoeeoeo verform.non.invas
ssw.saawww...m rendering
administer medicati ions www.eeiai................e......
sketching, wan..aann.... iddaiiiainnn on
computer aided design gn failure mode ana analysis systems.thi wwsmmssthiiiing desi wwiiinnccumentt..m
criti itiic www..aaiinninnggaa..cc.ccm
hi historicali wertorical-perpppeteeeeee
design for assembly
ion
vi wesuullaarte www.sreeaiii.......e..
prorrinnteeminn iti
cost sst imation
perspectivv ive iereetiieeraaaa..
wwrush.techniquee
light and www.tanndssaaddd
design.validee
apply.wound.
interdiscr setrdiscipiinaa paary connect itttionn in making
met met hedoloogyy www.immpeen imppemmntation
implement ppa.aaaia ww...manngmmmettt.......mmmm stt.aaaeeeeetttttaaaeeeeooooo stteeetttssttteeeesssee
reatment..ett.....ee..m..ee...te opt.ions
utilize medi i cal cal-aalllaaaa... imag ing technologies
www.oooaar...aooooooooo..ooo.a.e resuscitation
ristoricalo wew.:teenniii ieediideniiii iddetiffiinn iefiicatiinn ion ion
assess treatment. e effect ct veness
ltty control impl emeettatioon on
conduct conducctphyyste wet--phsiicee Law.maaaiaanatttt i ions
vitiailllll.......... gns
stakeholder communicatioon.. ion
medical. aetaal.trattmentt .ee nethods
design for manufacturabilln
evidence based reasoning
.plans
www.ettetette....e lans
manage chronic di seases
polorttheory: www.rcppppaiiioo.
composit composition.arpe soo..onani www.arraggeeentt
prototype.coe dew.eeeeeeeeee
educat e-pati ent swon_t.ontt.mmm
monitor.a pati i ient.. weeeeeetttttteeeeeeeeeeeeeeeeeee
design ott imi imizatiinn on
problem refinitiion
concept.gge wee.-geeeeiiooon ion
techni cal drawing ing
project www.maaaageenn www.maaanageettt.mmmmeemmmm
material selection
digital wwwpppanipnnnaieiepp
perform werooossuurgro... sur.isurggge. www.ssurrraaaa..............
ergonomic.co dddesign
texture.cat www.rrrerreiiooo.o
develop wew.llopppeettttee
perform.com www.carriiiooo
riak.assessmen
reverse_enginece
pistoricall
Skills
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Published as a conference paper at ICLR 2025
wistriouued.comm rol.troooo wol-systttttooooooooooooe.osssol em www.oonfiguueer on
trans it. wew.eedddeeeeeeeeeeeeeeee opment...n...mmmmmmmmmmmoom planninn
energy management.aeeemmmmmmmmmmm .sy.tetteeees........... em design
cowput wew.iingggeee setuup
envi ronmental impact et eva luat inn on
industrial wll.aattmmss automatio intoooon.. on systems hmi gn
wwwtiion.sss.... sy......t....... w.w.tttm..... design dat www.aiiiiiiiiiioiooooiisiiiiiiii
iimpl Lement ation on
servi ce frequency-our -op.ccyopptooe imi imi zation
Table 114: (Part of 52) 3266 skills extracted from topics Tables 90 92 and 94
safety sysssttee em. Implementate ion
stylization ion and ra in
mi mixed media integrat.ion vi visual storytelling ing proportion wwt.aandscaaeemmaeeee.
art cew.criiii i que and analysis
digital twin development
volor-palest molor.palstte.see wtw.leeeteeeleciionn..
symboli cmpoolssmmmm. smm www.iinoooorriiioo
opc_ua-ing ietpiaiiintggatttt regration
route. opt ieliimiizzi int.iiiaiiiiioo
mizaation
budge: t. management:
dat data.visualizen wiivualllii celsuallzaaiinn izztiiiinn ion
Tare_systent em. design
landuse anyisiss
robot proot-prograamiine
industrial Ledgeeeeeeeee.............
industrial. aellllott__ee
traffie.fle
industrial.ddtaaee for www.process.....ooooooooooosssre .opt imi zationn ion
implementatt ion
automated ma Materiall handl ing system design
www.iinngiiipp 1eg-impl.. lementattion
emot ional 1 expression. ion. thr.gghooghh.oo art
planning
imm.emeetattett aatiionn ion
abstract wtw.teconccptiiii....e. wolliiationn on negative vel.eeespp.... wwwpppp.ttiiiaaaaa.............e
wwl.ltyyottttttt rol-sstuup
www.maaaaaannnm. management
mot. www.ioonnnnooolooomoo 1mp lementation
process coontrll.oo oottmiizatttt..mommm.o..mmmmtmtt ion sensor www.gnnnetaiii... aiiiiioon
anaLomy omy underst Landing g art hi iistoryy Interpretatin ion
predicti inrtivve ve oon.ionnnntttreeeeeeeeeeeeeeeeee integrat ein ion machine vii sisevvisiio si sion..
infrastruch ruuctuuuttt ure asses sment sment demand forecast ing
populatio iertiiondde wwwaaaiionnnnnntnn density mamppnng ridership analysis
art www.mmatriiino al .nowledge
visual ball ance cee.ccrriiion
creati ve problem solving
industrial sybersecuriig
public.tran anaaannnsiii iitsitt.p. wonnsst.ppaane plpplannnnn
stakeholde aepphllee der engagement.
accessib ieoiiityyyie woiilitypplannnie planning transit t.schheuullng lin
mult wemoodallinnggeee ion
scada_syst emL con.conniiiii.iii
fieldbus www.-cooooooo.oooooooooooooooo..
machine. wew.biinneeerrinnn...
gesture addawwing
plc-progr awwroorrmmmmiin amm tommingg ng
aut ooootdeatooooooo ed quallllliiio
Skills
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Published as a conference paper at ICLR 2025
di disease surveill surveillann milillannn llance. implementatinn ion
equipment. selenntsseeeeetee lon. warparratttti
gublic outreach coordination recreat ional activity planning
risk_assessment -_nddmmii ielammittgaatii tigation
accessibility needs assessment
pract lee mi wnn.nllnss..aadd..... focus
am design
team bui ding exerci se design
90.92 92 and 94
epidemion ological. data-analysi
sereetttechhn www.eechhiii. ques ques
aangettallocate wwllooatioonaaaee www.iiionnnnnneeeeee
delegate.rese resrroossiiiiliiees
manage email efficiently ently
ffffeciivvly
weather forecast ing.iingn...... wwwssiinnninttteo
heal health 1th program p planning ing
health.equi ty-promo rion
health.bus budget. management
health. educat i soonnoopooo con-progran
health wnllthhinffrraam informati oo.ooonsss........
minimi iimii ze mm mullitaskknn
conduct :: time audits
health health.com commmniicaiiinn
Tables
time www.mmanageen.tteee
distsisss
create_to.de dodo lists
use.t iime blocking
pract practice www.ctccc.effett...
bat ca.chhsin wwsssmmma.tttteee
health.educattoo
Table 116: (Part 11 of 52) 3266 skills extracted from topics
post. post_acti wwr..activitt. iiittiiityyie www.ttyeevalaataaaaaaaa eea.laaaattt.......e.e ion. and feedback collection
www.ddssaaeypppeeeeeeeeoo ot cotto.llingg 1.m.mmmmpppp. Lement at ion
wownnoorrdiieeeeeeoeeeerrrreeeee wedddiiiersee seerrseggroo re-ggroups
transportat ion. logisoiiiss.. poggisiiinn sti www.coorrinnaaccooooooo...o..nie nation www.tnnonniniiooooeoooooooeieiie
dietary requi rementrrreeess. www.mmcdattttac........oo....... ion
permit and reservatto on icquisition in anvironmental lalalinpppaee
wwt.liipttttaeeee .mi mominimiie aatizatii. on on
locati on scout wng..nngnnn..... and eew.eaaaiiinn. ion
and mediation in
-iiiy-hours hourrs
immitttiiiii imi zat on
act wew.ityyrreeerchho. scoverrrrr scovery time www.emanngementcccmm
ing
set deadd ines and wwt.sticctttt. t..tt.... tt.tttttee them
www.seecoooinaaato.oo...... on
Lransit technology evaluat.io iin ion transit policy devel oopennt
in
www.ddvellpmmee lopment www.fooce.deeeloooe..
implimmlmee implement.a poomooroo.techiiqr..
sntivityycussee www.oitttiii.......m..mi..aaal.a on for-
utilize productivity ivity tools
commun ity heh assessment
health pol policy icy deve lopment
group.dynamic cs management
apply eisenhower matrix
abblicchealte ma..angggeenn.
eliminate wid.staaatiionn...
health.r www.riskk.ii ..k. ask.assessmnn
confli confficttio ct. www.reeessoooeoeeeoeeeooe.eieoot
break warge.projeceee
www.hhaltth.rseeeeoom
prioritizen siriiizettasss
health.re mealth.workff www.worreeee.oooooo
set smart.arr........m.............. goals
first and.aiddddddddo
ident iitfffy fy -wt--peakpppe
Searn_to_say.nn
outbreak
Skills
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Published as a conference paper at ICLR 2025
stakeholdes deeeoolder..oo lder wwwmun catt i on and report ing
competitor, ooor analysis and bencharkking convers
influencer wwt.-ppatttessiiiiseeee post_campai gn. an..anllsiissss and.ndd.rr. report ing statistion itsiiillll ical process cont.ro. rol data coll lecticiii on and analysis process mapppini
continuous improvement facilitat ion product i inspect on techn ques
www.ccooddnntiioo.
management
eeeeeeeetiioon ion
health emergency prepa redness health impact evaluation on
Table 118: (Part 12 of 52) 3266 skills extracted from topics Tables 90.92 92 and 94
www.hooooy..sssssmmetttt global hhw.hhhlao....plmacc..
trackiiking
marketing ientiingautt omat mmaation.e ion.tiontoo oo..oooo.ooooteooooooooooo
inn. on. rwt.raaee... contttttopptiiiii .opt imi zat ion
measurement comment_sysseee ..s.sssssessss ewrstem.adt analysis
pupplieerguall wel.qualittt. lillttyyln -aa.ageeennttnn
campaign www.rattggydeeeego...........
creati ivaaivee ive content. www.attttiooonn.
campa imppaign gn performaaee performancce www.cettrraeee
A.b.testings wininng--e arriimplae Saapleemettttt conntation
Pross-chanin www.eelcampaaiicm gn_
customer wtwpreeffdccaa..aaarree. strat strategic.p perategican weaggiccppaaninnnn volunteer maw.geeenn board.governee governance
pocial-medi mel.meeiia. www.amppaa.cmm gn.......................
waakke..research.aat
quality sauditiiin
design.off wwt.gnooff.xeeerreeee
board
nonconfo rmi it i iedendddii intentffion ficat www.taii..nnnna......... int. reportttnn ing
fon.faciliiaat in
www.llegall.e.... review
financia 1. ww..aaanneeettt...mmmmmm for. nor.oonnrofis
iwwiiiatiiii..o on ma.aaaaneemnt
wablic.health.ethe w.a.pppccaaiiio ion
won.tennnniiiiino ques
gn.kgnkka... kpi. settintt rngginggaa and www.mmooooooooeeeee campai gn.
www.effctttaaaaaalaa.... aniississ
quality document.e aa on management
email ill i ing www.aimgaanncc.. .ngginn
env environmen ronment.l health moniLoring hea tt data visuallian iatalizztiiio oo visualization
ietratiionffee
target-audi wuw.enceed..e..eeee..ee identifieen conniificati fication marketing ing. ing.
www.saaaapppaaee in management
budget pl annannn moning ing and aaalllooeees callocationn ion
se wew.eeeettt.. lecetti on and optimiiaei inta-driven.dee serreisionn. channel
oonmakinn sesiiinmaaaee
onsr.seeeeeeeeen intaaiion.e ien.ettaiionnttnnii
campai gn t timellli imeeinnee.. nn management brand messsmsssss..... ing cons
monnissten stency
www.iian.paii..mmmmmmmmmmmmmm.mm gns
fean.six.5t wx..siigt sissiggaaii aamiimmm impl ement atmentatiin at ition on
ron.rorttcht..... wwwiineerrreaate ion correctivee
risk assessment. and mil tiiittion
fundraisines siniiiiinn.aaaa.. and grant. wwitiing
ietpecivveeciiiiio aee.ecicii.............cce...ete ion. lann wing
roi.cal cul aataaaiiinnnll aam.aanddaaallle
cross_sect or. www.orrooooloaaaatooooooooaoaeaa www.oooettt...eee........
campai www.coomplii. quality control systems
Wailure_mooee mode. anw.aandeeeef....
qww.ity.cott.aaeee analy
nonprofit oo..orraan www.nnizaaatiiiio
root cause analysis
prisis_and man.aeementt....mmmmmm.mmmemee.e
quality.mane wattymaanaggen
Skills
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Published as a conference paper at ICLR 2025
gement
so-nooproffi..
el Limatee change adapt. ddaptation ion strategies
legal compl iance and risk management
stakeholder.relee reterreelatiosshtt cethpppmanaaa p...aaaaee p.w.pageaannnnnttt
honprofit.humee www.anarresuurrecooooe cesureessccoo wasssmanaggeennn
sports sports.broader bro.dcastt..t atst wing and comment ary
awtaatnngaannn andannd.. aee.aaneddeeeeeeeeeeeeeeeeeeeeee www.reeeeeeenee ing
wwwaaainaleeaaareeeeeeeeeeeeeeee wei.gggtechhiii................e ques
donoz relation ions and sLewa rdship
wwwnddddiiiii rrecision nonprofit_lese wtw.fftt.lladdea....a........
lonnallactiiie aact ierriiityyyy sy-pl aanningg
Table 120: (Part 13 of 52) 3266 skills extracted from topics Tables 90.92 92 and 94
www.onaaannnii....m............a www.iinn.iii in. www.innoooo
advocacy and padppoliiee policy icy. wrr.innluee lulnnee
milaiictioon on
resource inventory management
sustaina custainable.to our ceuurism.pe sm. pl anning
natural resource manag ameene.
forest www.emaaaaaaaaaaaaaeeeeeaea -p1 anninn
sports www.event.dc wognnnnnnii. iataniizaionn ion
physi pyysica ca fi fitness training
sports w.ffaciiitty www.maaaggeeee..
water tww.lttaaataaaaaaaeeeaeeaaaattae management
gis pis-mappinn ing. aaddanalsssi ecologicali wwlogicllee mmodeling
ethical wec.iiiin.nnoocccc www.maaiin
moolicy.inie
habitat.rest www.resttratiii ion
watershed management
resource a olloooaionns ion.
sports sports.psychee psychologyyyyyyyy.....y...yo
land use pl anning
strategic.gan cag.mee..aappppppp injurryprewentt wew-yppeeeett...m
recreat recreatiomal. innal.
m
assssssseentt
www.ineseouurs...ommm management
nonprofit ma markiiin ng and communi cations
www.srssuuiii........... inwlluiiiinnnnn on-and negot iatiio conflict itt wwwffficctresoooooo
program deve Lopment. and evaluation ion
nomprofit. www.ooft..eehhlllloo.ooooooooooe ogy impp iemiimmmmme emmettatiin
pro ject www.maanaaggme.. gement. for poo.oooooooooooooooooooooooooooo fitsss
for.athletes
devee lopment
sports marketing and promotion
wildlife fe wwlddlffepppppl. wwwpppppplltitooom on monitoring
community wuutreach.andeent performance mew.urememmeuuemmeeeeeeea.aaa .andaanniii..n
iance
soil wew.s..oeerratt... intiion inn wwl.practioonn ices
remote. www.ttessesnnnnii.ee wel-ineerpee int.iiittttttttii....
interpretaation
wemeentsseeettiieee.e ion
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nutrition ion. management........mmmmmmmmm for
sports medic medicine cine. first. aid
ceompllane
biodivers iity moni moni tozing fisheries wet.riee.sstt
invas ve specie eiee control
equipment .ma enaenance
outdoor aroorrssue www.vvssvaall aallssslll
environme anvmenntental wow.oooieeeo stakeholder. mikeholder.eee wnw.eeeedeeeeegeeeeeeeeeee engeeeengeement
confli ct ct..ccootttttt inlutiionnii certionniin..
sports and recreat ion
coachi ng and www.mmmiinnnnn
sports sadataaaaaaa a alw.aaataaaaalaaeeeaaaaaaaaaaaaa aa.anallsee lysis
ecosystem assessment
conservatio www.atiiooppaannn..
sports_rul rul www.rruuess.itttteeee
envi anvironmenton al llw.
resource_all www.uree..llocaa.i.....o........
team_ www.cooorriniiiooooo
environmental.
Skills
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scenario pl aaningg and simelation on
pettennaaaeeaalaaaa ion ion
data pollectiion and management. economet model wideling ing
www.orreeettaiioooooooooo in
92 and 94
www.tteyyydeeeeeooo www.ddeelleee opment
industry sswcciicc.......... wwc.knowweddeeeeeooooooooooo
consumer r. behavios vorvior.o anaalysi
music. music.prond pwo.ucttotttttoo wow.softwreeeeeeeeoooooooo.o...e
financial wwt.niiiaaaaii.aannaaaaaaaa ratio.analysis
cost reduct ion strategies
swot ant.aallyy ys issssse wetssseeeuuiioo iinn
Table 122: (Part of 52) 3266 skills extracted from topics Tables 90 90.92
weweesiimaiiiin market.sizee wtw.ssiiieeeseiiiee.... ion
market shwre.hrraheee ana.lsiiisss
process opt imi zation
operational iiatllllallo budgetiinnnn ing
rrndoorrelationn pattinnssip.mee
productivil ivity analysis
market_se seeet_sseeeettt..e. ittenattion atiion
demand demand forecastirr ing
investment wtteesstttttttttttttteeee -ptt
wwwentory.controo ronttro
resource allocation
pro project managgeent rean-manufaet urinn demand forecast: inng
strategi egi c.planning
audie i eng neering
data.dri www.drivve.deciii www.e.eeei...
time mamemmmentemmttt
vocal recording
arrangement and
pri www.inggstrtteeeee
sound sound_des sedddeiggn iin
mixing.and..m
inventory
aattoon-operaiiin ion
market ent ry strategy formulatin iolulaaiin on
wew.dieniiiiaatt... on
macroeconomic wnw.iiiooooooooo.mo www.iaatorinnterreee....
data vi suali i zat ion and reportiiii ing
Landcapeesssesseenn
alak.assessment wtw..annniiiiiiiimm........ mittiiiiiion
bosiiesse www.pppraaa.......... veteati ions management
art.assessane sment
economic e market. analysis statisticn cal ana lysis
waarke--ppoortie inpportuuiiion peppprrtuittiinent.....
performanceemmed suruementtt
logist isgisiiss ics coordinat Lon ion mus i caa arts production on
music music.theory-ae www.thherry.... application
live.sound.res www.rennffee reiorrrmeeet
www.mpact..aa.. ct www.acaaasseeeeee
supply.ch www.chhnnnc.cm wwm.aaangmmennn
improvement
Stakeholden deer mww.ggmenat
inst mosertunntpppr performmaceeeeee
trend iendiddentee contiffin fication compet it weeieeellee
growth. rate www.callee callculatiin
supply supply.chair.are aamaan..nnallae
quality Saity_assssie assurance
capacity planning
workfl ow ana lysis
cont continuous intiiunnuussic wwnous-imrrovesse
change www.mmaangeee ag
music_compos iinn
regulatory wwwattrr-mpppppaaaaeeeeaaa
economic www.cccppoccccooo.............ce
risk-as sesasssessss sment
team team-leaders leadership
digital ligitallaudion.. www.aauddoooooo.
problemssolvin
Skills
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Published as a conference paper at ICLR 2025
wtw.abiiity.ditttee ribbtt i www.iieennfiiaiiiio...
wwt.essimmeeeeeeeaeeeemmmmemmeee wel.mmeettiioon ion
aut automated ed m mikking sysstem...mm.............m...... www.ttteeaa......... ion
processes
implement lellenent.. quality col..rolooooooooocoooooooooooo.. rol measures
sample creation and manipulation music bus business iness management
envir meniiormmetta impactt assessment
confi confidence dence interval. ca..caaaaa calculation
lllocate resources effect ively
perform weriiiiaa.paahiie Lysii
lysis
Table 124: (Part 15 of 52) 3266 skills extracted from topics Tables 90/92 92 and 94
.ott..optiiin imi zation
mechhiqques
music. copyrrght, sutttuunddrstandl audio_si sosoossi giggan wtw.sssiinaapppppeeeeeeeeeeeeeee
animal 1 www.bbeewwwiessseeeeeoeeeeeeeeee www.es...sseesmmntt
perform.en www.eerrredlaaleeee www.dvaaleaaaaalaa
pasture rotat ionn p1 anning
i-processingg
herd www.dedoouiiiiiii werrdutttiiiytooo.i wwwettiviitaaalyyy....
apply aggi agile weethodooogiie
identify project risks
genetic. sewiiiiiiim imp www.oooeeeetttt
biosecurit, riturrityyyyyy www.mmes..........
wwt.pprreecccstttcco est wertimttepppoooo
manage e projjectttttee budget
market. www.eeetreeddaee werreennnalllsa..
regression moneoooeomeennn ling
project management. www.ieeeproecttscooo
out detecctoon
feed www.dmannggmeet.coomm
typothesis.testii
data cleaninne iig
probability
impl Ampleemer compemeent.cle
weteiiieseeeeeeeeeeeeeeeeeeeeeee integration
acoust c t reatment impl amentati on lal iiviiieeeeee jreepprojeeceee.ee weeeetcoordiineeoeeeee.e......ee
mation
wtw.ttatioooooiiiieee.ee logistiss
qualityaay. litty.y.e control
ctt.iatiiccaaleeeeeeeeeee srciccsccass www.alclllatiiiiiooccccccccllaca lculation
breedi ng program. imp lementation
manage saakhhllder expectation ons
mat.mmmenn...tn....m...mmmmm software
statisti cal ana lysis techn q descriptive. ieeevvttissstttste correlat ioo.oon...n on.analysis
sustainable warminriigggg ng ppw.tttttc intticesite
pendioeequpmentt maiitteeee enaceennnneeeee enance music masiccmmae mamaaket ing a-andd. pr cerromoiion ion animal www.hhllleee......eee hheahhhhaae www.mmonnooooooomoommooommommmot siriiiiinnggg....
disease preventis ion protocols
conduct. pro ject, post. mortems
on
intriiiion pl an. dwwedeveelll..oo. opment
livestock ock record keeping
liance
livestock. www.trrrnnpptattiii....
syy-desigg gn
create pro project ject schedule
conitor-prroject. wttpjeccpprorreseeee
conduct conduct.project pwwjectt.e.....................p ings
resolve.pros cotf.oonfiiiss ccwrtttasssc....................
livest oock facilit weccciittydssse
create.pro ceatee-projeee www.ooeec_cchii www.hhhateeereeee er
create-gantt gantt.gttt.......at. gatttttttttttttsstt......s
data-vi data-visuali suallllliiee ion
livestock occk product.
develop.work.bbe ww..kkbbeeae berreakee
midi prog rammin ng
variance lee.analliiii
use wwte-projcctmnnee
sampling iigi
Skills
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Published as a conference paper at ICLR 2025
sustainable welleedddsigiee deeeiggn... gn. imp ssmentation
ptt.ppparrccttt..ppllaaaooooo apt.aaappcciioon
safety-protocol. inmpl Lement atmentation inion
ialgganndfiiisshp finiisinnn
int.iinggttaatiiiiini.mmm on
Table 126: (Part 16 of 52) 3266 skills extracted from topics Tables 90/92 92 and 94
ices
quali littyyy www.roooooooooooooooooooo controlinspeen www.ecnspptiioo
hvac eoe-systen ems.ttmm..... installate alliiion
color theory application ga rarent. const ruct iontion fashi on Illustraiion fabri wwammnnnplaaaaa. sion
plumbing ing installati on
fashi on. www.deiinnn insiinnnn -en.eehoods
load.caccu cal.ccccclaiionn traffic flow analysis
exper imental design mime seri les analysis
boards
collect conlection-pl -pl aolpplanne planning
fashi on www.maaaiiiite... ing.eting iereetnngiiin
sustain iasstinnabee e desiig.nn....
survival anaalssis
structural, deesign
blueprint reading
foundat inttion. lonllaying
tunnel construction
creat ing mood wtt..mood.bobar..
data..nteeprettat
Tluster.analysee
urban. pp anning environments
site_surveyinn
aetaaanalysse
sraping-fabee
construction and wnw.oooonnnnnaaaaaaaeeeeeeeeee infrastructure
risk.a srfaaseessmen sment and wew.mmitttttinnn ion
ing. andaateeattin lon
principal component .analysis
statistica iillllllll software usage moltivariat. analys ysis
wew.wwiinnn ing
waste weatteemaaggmmett..... paanninn
sket ga rment. .designs
www..coolllli..oc...cc.cc..cccce ance
fashion.hi hishhittt..... sttoryyy anaalysis
heavy-equi wemeet.ppeertt.....mmmmm.
welding and m anetalwors lwork
drainage system design
techni cal.ccll.ii flatflaatff.. fwa..drawiaddainnog
aat.drawing
ailhouettedeete le.development
www.oolding.ssee sasseebly
bayesiinn inference
statistical setssiicalla.. www.ssicaal.rrrr....oo.o.o
cost eestettis ieatiiaatii ion project management
material.selec eeleecionn
electri eeall wiringg
bridge engi siineriing
seismi, c w..reerofittiin
Lextils ile selection ion
trend forecastin ing
digital wel.ddeiign www..ddessnnneoiooeeeeee
scaffolding.ase
garment wittiii fintiig.t...............ei.glntt
aictoraanllssi
oowerannllssi.
building www.ccccc......
soil yiis
pattern wwwteerrmakkknn mwmimaiingg....
road_paving ving
Skills
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Published as a conference paper at ICLR 2025
machi iihinne, ne lea learnin ng al goritthh deve lopment
probabi Mabbabiltyy" lity theory imp lementation
framing and roofing wwt.eexecution. ion ement attion
paint ing wta.aandnnaannnnnnonnnoo.a. ing.pppicaappiitiiii.... on
st suctural design analysis material.se. Leseelleeionn in. opt iiii zation preety-protoooliig pooollimpll wwwiiimpeeemeees Wememetttt at ion ion
insul at wwwiiionnnntlllleeeeeeeeeeeeeeee wawstallaaiiommeet.. iwtaatiio.mmeet ww..et hods
90.92 92 and 94
carpent ry skills atiilin zaaizziiio ion
differential weeeeeuuiitt..... on solving
algoritiie itrttttmmm.. design. and.and.... aniiiisss
appli ed www.mmttttttiiitcmm inmmtticcscoom meethhod
ninqque
#Eoject.schedduli. wwwplling.cooordiii
concrete pouring finishing
quality control inspection
waste www.maannage.....mmmmmmmmmm wew-rreccliinng
aalaicall.... call wwtmmicalssssse.... syst ems ems anaalysis
www.r..cossuup... in
fashion show production ion
computational ional geometry
compl complex.ame ex ana lysis.techniii..
finite el ementt analysis
manage_devi wetevvceellffeoe liffcccceee
electrical caal wiwwir wwiringgiiie sigiimmp..
stati itatiste st statistical cal modeling
iot jot_devices ce memtmmmmmnnt monitor wewtorrddvicce sireicel hheiihh
cryptography -impplm www.iimpemmee dynami
analyze. deviieeeeeeeeee dataaaaaaaaaaa data
Tables
stochast www.pccceeseeeeeceeeceecccooesee
optimize www.pp.wwroooo
Table 128: (Part 17 of 52) 3266 skills extracted from topics
wemplemmnta...e ion
i siccccsssee inm.impllon
building .code. wwwpccocco...oooooo..cm..ccoo... 11 ancc assessment wea.eequupmmeee pment. www.oere.......................e eeeeratl.. inon on
building i informatii ionn oon mode 1 ing bim
innterpretation on
wws.ittiiizattee.. on
wenneetiiity.isssssiiie -issues
building const ructi on methods
application criiiiiionn on
welding-and wwnddiinnndddeeee wenddmetaaaaemmmeeeeem wwork..xxee www.w..eeeet.... ion
drywall.ing www.alllisstalle allaatlatttatttia.llllllllalllla allatiiaan ion. fon- fini shing
aemeeet
Foundations iin...n.. loy.oonlliiioooooomommmo www.llyynngiii.... ng. techniquess
Flumbing system................m..me...em installat lat on hvac system em i integratiion ion
www.eerraaaaliss............ ysis wwwpssssssspppaaa......o.ppm inn ion
ooprooooooos
mathemati ical wwf.ware.usagg operations.c wevearcchtechin
signal processing iisssnng.ao met chods
graph theory appli auplicate catioon
masonry techniiccicc ques appl
won.technique. ques
security-proteee
#llueprintro www.iintteraiinniiiii peggiinteeee
opt optimi imi zat zetionitttittteeeeecce Lon.techiigee
linear-al al webbra.appicca.
mathematisn ical modeling
game_ camee_theoryaaee wteeoryaappliie w.appppp.... www.pppppaatii. ieplicatioo
troubleshoot www.ennnnnniiiicccmm
time seri es ana ysss
construct ceuuuiiionsiiii www.ssieerrie
cost_esting iiat won.ioo.uuiii.
numeri ca analysis
matrix computation
update. fi wwware.remee
implement www.useerrttr.eeeeee
accessort sory desig inn
configurt petrreiotddevee.
mustainables waaaallebbiii
constructin we..ion.eeee
Fourier_anali
Skills
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Published as a conference paper at ICLR 2025
www.mmmaaeeeenee ement
wtw.eaa...eettto..aaaaa..... sment
ecosystem vul merabiriiiiiie ity assessment
politicn leaaaaaa 1 i deo deology loy ident i fi cation on public public.admi wwolliccamminni ni wetedmmiiisttiii staat on Lea luaallaiin ion
wm..s.ccvvv.............e.e www.vv..c... www.eeteeettaaaaeeee.... sm. anaysssssss wwwrraaslm........
adapt i on st wwn.strategg.. rateegyyy.......... fo.foorno www.ulttttiiiioo.amm..om.aaaaaat ion
waw..raaaaaaaaaiioooaaa..a.aaa.a inn
environmental impact. assessment
integrate iot platforms imp Implement over the air updates
evaluation
Table 130: (Part of 52) 3266 skills extracted from topics Tables |0/92 92 and 94
impl eemleteent... deviv iiceecee di agnostics
carbon www.fooottpinnooo prrittnnnet www.callllla...cc. ion
wopment.goa
s.mapping
power iistributt on analysis
volitical.structt ysia
aeencrypt ion
cemmissson
interdi scipl iiniinnnrn....m hwtpliiaa.coolllaeooeoeooooo
cl climate change www.aaallll... ysis
government www.syttttaaayyyssemmeem
civic.end wwwaaageeet-ssseeeeeeeeeee
greenhouse aaaalyiss
uncertain terrttiitty... y.analysis
analyze www.vieeeeeleeeeeeeee logs
Lime_serics ceberries.ee www.aisssaaalii........
sci ecientific iffiffic.c rew.report.t...
ronstitutional ietuttiinnlliiii lotooonnnteeeoooooooooeooeoieeee
government. www.uuudutttt......
impl implement taatttaateee www.eentddatteeeeeeeeeeeee
aut omate de.eviice seevice_onee
opt iemimia immiee................mm...eeeee ze. deddata wet.daaaattaaaaeeeee
statist sarissiical.. cerricallssn westicallaaalllyy
sustainable.dee wewblldddeeeeee
government wwweerrmeettttaannseeeeee
legislative wwwesstttveeii.
federal i ssm.ssssss.........
sss.sssssseet
renewable e energy potentiai ial analysis
bureaucratic efficincyy measur eeeeet
www.eocnnnncccccccccccmcco..ccoe cat aow.innnnddooooooooooooo oudtoctrecc
wet.nttllrrlaiiieeeeeeeeieeeeltt ons wwwaatioosssssseeeeeeeeessesssse
evaluation
ce..attennticaeee cortication ion
revernaacce model classi iasiffff fi ficttiii on
comparat at aveeeatti...... iv www.geeeeeett....mmmmmmmmmmm analysia.
ensure regul aiggulaton atory compl iance
www.annddmaaagmmentttt
miti ieatiigaiinn seatiionmmeet measure, evaluation
democrat ccindex_call culat at ion
polit political call party-syyy. wst.stssmmmmmmm..........mm....e www.systtteealla.ee.......
www.eoomaaiooo.. ison
ppeerrreiiee www.liitetttttrrseeeeeeoe
checks.and and www.annse.eeaminnaaaaaaaaaae.aae
wil.iccyooooooo for.uuutttatii. on www.llccpooccc......
scale lot. inf infrastein tructure
manage devi ce inventory
creat e. deviviiie ce da dashboards
permissioon
imp eemen. wedee.ge......... www.coomuut....m ing
geospatian 1. data analysis
pol policyy-o ey impactt.p.....m........p. wwract-asse aeaaaseesss ssment
assessment
stakeholdez der engagement
data_col weccollllillooooooooooooooooelll ect www.ooonnnnndnnnn
climate eufateffin lrance.analyse
manage-dev www.dviiceee.. device.groupe
ma-groupss
Deviceed ce a alerts
eeenario_deve
confi gure. dwvidviieeee. wew.dvivceeaaeee
data.vi suali palalize zatiion
manage. www.deeiiieeeeeeeee deevice.pp ce wwc.cpppeeeeee
climate e modeeedelll ling
climate www.eeseeerii...oo
electoral. wecttorllsssstt.. wws.sstteem...
Entergovernmenta
remote_sene www.eenninnnnin.
policy impact..............
climate comatt.coometn
manage devi
public.poll
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government reform strategy deve lopment. fashi www.aaccessory.de.. si
contract wnw.ratttttttttiieeeee iat wow.aaddaaoo adddidd aimmnnn irsistatiion
aviation technologg logy integratiii regration
technicall 1 specificatettc.. oin priting product i ion timel Line mn nagement avi aviation attitii ion syst em mana mana gement fli righttt -s www.schheuling.... wttpllinnnnptiiiioeieeeeii
project ject. plann ng and schedu dithheuuling ing
werlopttiiiee immi zatiiation aatioon
riskaassessmet wtt.eeeemtettttmmmmm...ee and_mii iigat ion
Table 132: (Part 19 of 52) 3266 skills extracted from topics Tables |0/92 92 and 94
deleeddesiige pract ies
assessment
ai riroort operati ions oversi
www.innninttttccceooooeooooeeeee ionn
emergency_respons. www.rrepponee.coordiiioo
aircraft. fleet management
airspace. des.ddiiiinne wwssign.aae gn anw.aanddddda....
runway-capa papppccttyycco wwt.yyymaaagmmmmemmmm
fuel efficienc; cilnccccyy analysis
golitical risk analysis
baggage handl seaaandiigssssseeeee..e..
change.or wwweeoodderr.. management
sketching sxetching.techni. www.nngtthhniiio ques ques materiallsse wwrriialsseeettio
quali Mallity ty control wwwnntollssesseeeee
ground_handlingg wwwandiingooperre
prototype.cm proototype.creatioo
wwwppecrraaiioo.o on
presentation.es aatatatiinsiilll
digital rendering
stakehol T.aaehhlder.commee
subcont wactor..ccttrccoo.
market-research
fashion.hic weahion-historyy
sustainabi waatlniaall...m
aviation on env environneen Loonennaa impact assessment
aviatio on safety protocol Lmplement.n at inn ion
measu rement.
internation caationalggeee www.aaoaeeeeeeeccopeaeeeeeeeeeee son
manufacturing ng process indgraaaddng
www.mmeeeeetttiii.mmmmmm lon
progress www.mmnnioooooommmmmmmmmmmmmmmme Lng. and reporting
constructi on project mangement
resource_alloce winoonnnnnndd and www.iioa.aaaaaaagemeeeeee....eee
aircraft waiittenncee.pllaee lapiinnn
www.ocooooooooooooooooooooooo..c impl eemettttinn
3d momoodeiing-ee ing sor.oor. accessories
ergonomi www.cidiinn...mm gnppriccppes
design.skills skills
www.folwwwootooooooooooo opt imiiiiizi zation
reg ime type classi f cactticiiiiio ion
brand dentittyyyy witttyy. devel oopeent
crew resource ma manageeent
Lovernment. (ccountabllin
air.traffic.cmm c.c.otrrl.coooo..
aviati irriationwwet on www.ww................... www.iineerpppe
budget imat won.onnnddoooo
computer er awddedddccom design
collaborativee ve design.
www.rregulatooryccoo
aviati on dat a.w.ddaaaaaaaaaa....aaaaaaa analysis
client.commen ctt.ercoonnniicc. cation
fashion f forecast........ preccsstnn
sourci mat materials
aviati on www.ssecurttyycee
cost_estion ietitiaaiii inn ion
passenger www.oo..oo
trend ansilsiss
welor.theory.aet
pattern.nt making
safety safety
Skills
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imp.ementateieeeemmmmmmmm ion
interpretation
adapt p1 ans for differeet.. life stages manage weenage.welle we www.weigtttleeo www.loss-ooo
mnstainability-peee ces.cticeeeeeeeeeeeeeeeeeeeeeeee less-deeelppmenne op
waste reduct inncctionn oo ww..ttategyyyyy.ommmmommmmmm...t develeeeeeeee opment
optimize wwweeeeealll. wtw.ieeemllttiiiimm wetttiinnn--. wtw.aei...iia.....oo..........oe www..ooforrromannn.o.mom
concate.cliemt wtt.sssoooouttttnn.............e itt.iooooiiiitnoooooo.ioii sics
devel I op. sports snnutrittiii on wn..strategie.. digital media www.craatioo... on
evaluate food qualiuuggnnn tityyyy and sources track daily lylly www.mmtiiiii....... wwrrientiinnee intaee
const wwtpuutiiintteeemm weiionnteeee www.ioonntoooggg....ccoom per--mpleenn
analyze mww.-oaarooouuoeo nut.nnuurii.................. wonttriee wient.tttt.t....m wennt ratioss
Table 1.4: (Part 20 of 52) 3266 skills extracted from topics Tables 90.92 92 and 94
resource.effiieende analllsi
nutrition.p putzition.planmingg www.ppaannnnnggn..mmm..m.. lanniingmmehhoss mngggeeehhdds
coss.or-gainn
live stream ing manamannneett genent
interact ive. media desi gm
wtl-imaattaaase
construction Jocument
invironmental.in wwtpmmentallimmcttteeem
cal calculate. www.callooo ori www.orrrrrrrrrrr
animation :ion techni ques photo man cranipulatiio on
virtual Pirtual-realll .eerraallll htt.uualerllllyyleaa.al stt--contentt
con ict resol resolution cuiintionn
mot mot wwwnnngaapicsscreeeeee..
digital.in periiallllllsse wil.ussstaiiin
digital photography
st wwr.yyoarr.creaaa...
digital-paiiee caattallppanniiiii in ing
video www.osicoooiiioooomommooomoo
graphicn praphic_dest ic_design
www.rrestrccttt ions ons
incorporate_suugee wwwporattssuuplllaeeeee ement w.inttssiitooo w.wisiinniiiiiiii diet-planss
Rogistinn st cs masamaagtt
lifecycle.assee aes.assssssemnn sment- implimmee ementation
araluurallaee. food preferencee
ion. changes
create wwwget.du....................ue. fri rrieedlly meal. pllans
content.ontttt.ommmmmmmmmo _opt imization
team. www.eeaaddrsiipg.e. wrrippand.moe wwnnddmoiiiinn www.doottiiiiio. viatiion
design gr personalisn perronaaizze...m ized wwl.ddeppppll. plans
ciencies
augmented reality development
wtt.assssssee..
site-select wnw.ionnnnnddnnn and.anaale andd.analssis lysis
create bal anced meal pplan
interpret wetrittiiiii on labels
building code comoliance
assess www.nnutiiiti. ri ritional _needs
www.ddnnsnnn...e.... gnn weessin-prinncee
-lan-bydrati wom.strateggne
procurement Lon.
monitor body wwwposimooc..ccccccmmmmcc
aocial.medie meciaalmmdia.cooteen.ocoo
user user-inters www.riinerfac..ccc ce desiggn
digital storytelll littlllnng iin
enviro anwienttettlll al weteeentaaliiaaaae www.iiiipati...
data.vi swaiiiualattti ion
vel eng engi neer ingering in
integrate wew.e.uuuurraae
typogr aphography.design
audio-productios
color grading
address_nne nut
sound gm
adjust_ aww.
video-editing
3d modeling
web.desigg iig
sustainable
Skills
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sustai iatainabilii report ing and disclosure
order conlfillmenti process. improvement
sustainable www.bb....aassooooo www.strrastttaiioo...o..... on pl planning
immplementtiinn
lementation
agile me wtw.leemmhhoollooooooooooooooooe weeehhoollggss hodology.impl iimpplpppp. eemett anion
techn ca 1 document. t writing
develooeen
api spie opment. and integration
www.iiittttinnn
lean Sean_lop lo i seristice ics. ses_impp impl Lemeeteett atmentaiion ation on
90.92 92 and 94
carbon footprint. calculation
water.conservat on--oon-pnnneee on-lanning
www.-innegratiiioo iwtraaiionss. .on.setup
sprint.po wwwaannnnnnaaanmnmmm aaggaandeee www.iiiganee.eeeuttiii.....
execctiinonnn
warruular.econne modelo peoodel_implee
data-driven dew.eeiiiss... decision sion conmmkiing
last mi lele de.deiiverry seeliverypplannte tryy-planning
susta wrtttannabili:. integration in
management
design pattert Lern appl ca catioon
logistics.nett co..sttttttteoe work gn performance www.mmtttteee.eeeeeeemmeemeeeeee
performance.ov paceepptiii wew.ppiiizaatai...o memiiiiaaa miiiaaiin ion
code. www.ooooeettccmc.cmm wertiinnaaaee and sese f.
sadee-ualll tetuallttyyy sty_asse ssment
biodiversity wwt.-immactttttt mi
multi mroda...ransporttt...
Table 136: (Part 21 of 52) 3266 skills extracted from topics Tables
wwmplyycchin.analyae.
route_opt imization
capacity planning
versi persion..o on www.oooooooooooooooooooooooooooe
cont wwwiinoousiinte
real_time.ten
wollitypprogrrai amm devel opment
aepllmmenationn tioo
eco. eco label. nn and cert certifii fi caiicaiion in management
green buil 1 ding des dew.gd..innn... ign. and wweteeiieiii..ee fication on
ces. immplmmpl
ddereengaeeeent faciiitatiionn
debugging and wtw.broouessooooooooooooooo eesoootinn ing implementatiee
wnw.riingannd.aan and.and.aaa analysis
logistics ics es software impl implement Leeeeeeeteem at on vendor rel at ioishiipp management
wwwpppiiiieeaaaaam.eee aatemmaaageee www.maanageemnen gement cust rustomsaanee and.ccmppocoppppoococmmo
renewable energy integrat ion green supply chain management
wtw.aasssssetiiiieoooo ion
wwwnnssiiiitte. corporate. www.soocllll...... responssssssiii
logisti sestics..te cs wyw.ssstttmoooooo cntemopptime imi zation ion invent ory management
transportaticn at ianiin ion mode sel eeleiiiiion
reverse pogisti 1 cs wwm.aaaaggeentt
risk-as sessssssnee sment www.anndiiigaaeeea.e.am..a...mie ion
software www.ddeeellplee wemeettteent...mmmmmm prwcttiiie ices logisticn iistiiss.ii
wrwcess.ouuuueuee omat ion
softward re architectur p planninm ng
wes. ces wemiimmleen
pair seiirprogrraee anrramminne ing www.litatiiiiiooo ion
iettimiiaan zatizatinn ion
sustainable naweeaablla.. www.riiiutereee.........
inteiiiiaatiin. zaziiiiinnn ion
requirement reeeeteettt w.g.tttssgttteeeeee...eaaaaae www.gaheaiiiggg.. wwttheringgaaae
energy aadit........ www.unninnnnnngo
cost www.beeeeeffeee fit... analysis
code. code-revi rewi ew www.executti. ion ret.e..rtt......cuttt..e........
cross www.kookkiggg..o.o opt.mittimmmmmmmm....mmmmm
test www.eeeddveloooeeee
www.sign.ndd..m and
demand demand_foreed wow.fooreetttinooo ing
securit ty beestppracte
sustainable waainaaleemattin
fleet man.gamenteeette
code ze refattriing
dat database_dee des ign
stakeholdeps weolldereeee
Skills
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Published as a conference paper at ICLR 2025
code vers versioning sesoinnng and release management
email .aa ket ieetinggee www.caaaaaic.mmmm gn www.creatiiio. ion
interdia weipl... poplinaae ina ry collaboratii in
Table 133:: (Part 22 of 52) 3266 skills extracted from topics Tables |0/92 92 and 94
werieeeppppcaatteeeeeeepee iiiaaiiiioo on
famil Hamily-oo family.act aactiii sactiiiiiee wwwivvtcoooriiattt ion
in home -supportt
energy conservat ion pract i ices content. www.sttteeeeee..e
werrategyeeeeeee oppenn search.enging opt imii zatiiatiii zation vi video_pe video ceroopproucct.. i ioniinnnnnnii aww.anideiiiin podcast. creat inttion ion. won.andd... hostinn
opment
in
family lilllllliii 1rrliffer mmm.maaaneeentt
health.a wawaahhhan............. and welweellll www.lnnsss.ppooo
keting.campal
research werhhmmethoe mwt.ooloohhoyy data anyaanisssi
classroom.manne wwwrooommannagmmenni
statisti aassiicallaan Lanalsis
instructi. ional design
wwwssehollluuggeee ing
Lechnolog ogy management
cross-plat wtl.plaaffrrddeee. wwwattionanddcccceee...
student. asses assessment sment
thesi s. development
learninn ing. www.teeeeee.......
st ress management
home_orgam irtanizatio zation
educational ioniaaiionll... www.ionllnpppppee.oooee
teaching_mme methods
academi r.advising
time management
grant wriiiii diting
aedularity arity and reusabi lity improvement
www.iinggnnniiiin anw.annddlleee......m. complmmentatiin iin
educat educational www.iionnneenooooooooo inteaaatiioonn
devops pract ices imp implimmmmmnnee lementation
graphi c. desi gn gg..go..ooo.......oooo.......o.. for digiialllll....................e media
academic e in inneegiityytte wwr.teeeetoerreeeeeeee.. fernereent
work. lark..lfff wei.feebaae www.lif.bballaceee ww..allance.... waw.tenance
family ly tradit iiiiiionn cultivat. ion fami ly meet ng facilitation on
paggtlllllllaaaaaoeleeelpaaaaaaa plat Forms
wwlllccyanalaaee. arr.aaalyee analysis ysis
wwwoorraaeeaaaaaaaaa.aaaa.a.aaaa www.eeallaaataa valuutiion
falt-testing ing iemiimplee wnw.iiilemeetttttmmmmmeeeeeeeeme wewllemenaiionn
chi alddrearing.f www.inggnteniiiggn suu ques
Surriculume um develedeeeeeee lopment
effect ve communi cation
emergency preparedness
relati relationship inshiiphip nurturing
digital aaggiaal.conteeeo cooottttteeo ent wet..creaiiooo writing-fi waaggfforriigggo wigiiillllll.........i......iiti
riciallmeeae ieeeimiaa a management social
web.cont ent mwn.mgment
acalability.a wallabbiityppsanirng
wwwollall-publli shing
citati i management
educati lonal policy.e..
srealpplaae wanningannd.peee..............ee
confli ct. resolution on
financial financial.pang pl aataalppaannnii anniing
literature.F reeere revieww
experimes ment design gn
academi c ce--progres aproogrmmeee
academice e c.wriiigg
public ww..spaakin ing
critical. wwttical.thinnkee
peer review ew
code modul
Skills
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Published as a conference paper at ICLR 2025
green bu bui lidinnn dest gn and const ructi on
audience. se gment i won.and.tt. tandtarggting live divesstreamiin ing weiiinnggann andd wabinnz hhost ing virtual. .ee.rrrllll www.rrelalleepppeeeeeeeeeeeeeele
ence de..deii. i ig infographic deescddeeiiii gn. and creation
ittiinterrretaaion art www.appppccatt.. at ion
artistic_nent www.eeeeiiiemmm um expprrientate on art_educati wetpaatiinnuurrro..o.om
cen.euuriic werriuulls um s sign
artistic.collat abt.aaaion.aaailiiii.... waste_ www.eeeeeucciiiie iereducttinninnee ion.imp ement atin ion
energy efffciency a.sasssmenttt www.:tehhnquuu... ques
env environment ironmentai impact. assessment carbon carbon-footpprin: penttrrinneccee Lnn.enllllnoooeee cecaccclaiion habitat. wewt.rrrsttr..ooo..o itw.ttrrrrii.ttteeniiiiiaeaa.aae
www.maaaageeetttt
lopment
wildl if. www.sronnteat.....o on techniques
artistic composition creaticn on art bithibition desi gn
wawsprrttttttttaoaaaaooooooa at inn.taniipiiiio
Table 140: (Part 23 of 52) 3266 skills extracted from topics Tables 90.92 92 and 94
user experience des in di digital storytel ling wibileecconeett prontenttoptiiii .opt www.ititttttiiii.c.e.m..mii on
art wwt.restoration www.teehhiiic ques
mustainable.fon htrplelffrrsttttree ry envir wwwiirrmmentaa.. walaapplllioo welicyddveeeldeeeoeeeeeeeeeeeeee
artistic www.ntteeeeeeemmmmmmmmmmmm aattttanalliiss
digital digitalasset..ie assesset maanagement
art Prt.collect ion www.manageeeee.e
maaaggemmmmngmmnnn social www.mmeddmmmma...
hi cistoricale. wtt.:/aa..ooottt.iioiooooe
art www.market..cmm eva luati oo
museum mureeuccccatiion
instainable.treaee
envir rovironmentall. www.eatdiii.ooo soo prrogamm design
data analysis for content verformance
iiiiaiiinnaaniii ioniionnnnnn a renttranssstn ranslation
biodiversit wtweerssstyypii ity.ereeevvatii ion.tiiio..........i ioon st strategess
cultural diplomacy impl Lmeet at in
cultural symbol fimbolism........ ism interpretats attpretttii.. on
catuurr-raciicee ice
on.planning
ion content curation and aggregat.ion
ecosysten monito toriiiiin and analysis
pollut ior.preveeettt www.eeetiin.naieeeeeee..eeeeeeee iintioonnnnnn and control
content mww.mmootiiiiiie zat ioniiin.... wtt.etizttioo.ssttteeee..... ratteies
mult tellimmedie a. con.eettttttnmmmcmmmmmmm..m.... www.pppooooiiooo ion
www.ideenniiaaaa..... ffictiion
wet.e.ereeeeeaiiio on
renewable energy systttttts L em design
interactive ieratiivee inteeractiee.coneee.deeeee. seeee_ddeellppeee
on
wwwppppoll.eeeleepppp.o...... www.ddeeelccco opment
cultural.ante wwthhhooooogyy-eeeeeee
cultural cultural.trend.forrr rend...treed.foooo wtw.:rrrr...o..tttt..... forddfforecatinngg ing
environment al wwt.nnalllooosseeeeeeeeeee www.oooneeatiinnnoo..mo
water water.conservatt oon lanning
cross casssccllsee cul tural. www.coooomiicc..mmmmmm
pneveloppeen
cultural event pllannnng
instainable www.lll..aiiliiioo. cerrrcuutuuree
chatbot www.srrrttti.. writing
artistic stt.e.yyye...t.. dedendeiiii
sustainables www.urceee......
augmented. www.rrrlllyycoooe
ietalizatiin zat
cultural.n aaltuurallaaalyeee lysis
art.critici www.iiisswwiiiti..m............e
cultural her www.tetr.eepeeeeeeeeeeeeeeeeeeee
recyclinggpe www.inngggggaaamnmm
climate e.change mit
arts and culture
cultural-por pww.lpollllcy.ylo..l.oooo
content.l wewenntloaaaiiieo
Skills
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Published as a conference paper at ICLR 2025
cultivation
user generate rerraeddd.. content curatictoonn
int integrityyy..o...o managemntt
decluttering and orggaizzati on
Tables 90 92 and 94
www.aaall..iiiii........... ies
management
wwtioonanddmedee mendmmeddiaa ieddiation ini
analytion is interpretation ion crisi s s.management
pry-prrceddure
public. wwwlicccooiiieo wwoolcyy..o. ey imw.ementaeeeteee.mmmmmmmmeee..e
pwweeeroblemm..mm... lem sol.isooingg
networking wtt.
vi visual. wawl..conteee ent .desigg sn
performance reporting
ef effecti iveective... ve. www.onoccttiiiiiicm.....m......c
mi ndnfunesss pprccticcce ice
fitnes wws.sspprrrrr.r wwwpprrrrmmdee..mm www.ameeiiiin
emot Lonal 1 iatelligence
regulatose orrryyrrr.....oo.......o..yooo compliance
government .bwdgeeudnnnnn seggeiingg
audience engagement.
maid_adver's wwwiiniiiaaiiiie
conflicts www.eesssiiooon.. ion
competit competitor_ant weriitoraaee watttorraallliiii
self.care ret.routiin ines
parli aaelimmntaar ament ary www.ennttryyroooooeoec
hasht ag ag-strateeyy
cross pross-pplate pllpppattttt calatformiiiii
time mannagmentetttt
negotiath wwrgotiation.ann..e
personal. aeroonal.fiiami
publi c speaking
creat creative.rt ieraatvee.roblleee
legal res.ereeerr..
legal 1 wri tiiiing
ethics andd...an....... and wandiinted
Table 142: (Part 24 of 52) 3266 skills extracted from topics
meditation.ande iertiion.are relaxxxt on it www.techniiie ques
lopmeee
social wewiaapollcy_deee lopment
apppicatiio on
interpretationn
goal. set. wetting igg and wwwaaccheeeeeee eeeeent
work.liffelee bal ance maintenanc
habit formati inttiinn ion. and breaking
Anfluencer. www.loollaaaaiiooooooooooo.o..aa on
schedulinee peteduliiggaad celuulinggaani waggaanddaa aut aut omat on
cust customer omer servic ce www.hindiii
versonal.styl wew.dddeeelpeeenn.
product i vvity enhancement
brand ce www.deeeeeeeooeeee dwveloopeeeeeee loppent
senooaaaal
preparatiii
trendLident dediedeeiii i fication communityy bu ppildinng
Pelationshan htt.ppuuliiiin...
case law appl applicate iellicaiionn io
courtroom adwacacvcccy public. speakpiinn ing ing diplomati i rereaaiionss
electi on. managemente
sleep op:.oiiii iawiiatiiiiiiiiii ion
campai gn pl lanninng
aafestyle.and.peee
healthy meal. prepa
versonal.bannda brannding
law-and www.and..overmmen.
content creation
social listening
decision molmmaiing kig
e.rests.reduttio
administe www.ttateei.....e.eeeeeeetee
constitutiones
colicy-analyee
personal
Skills
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Published as a conference paper at ICLR 2025
devel og custom Aranalation on a aggorthmms
Intergoverrmenlal coordinationn human r githts protect ion weeialldecisione.e
anallsis
hospitality.servve www.seereve....maeeeeeee.. man.ggeennnn conflict.resee restleesso..ooo
Table 144: (Part 25 of 52) 3266 skills extracted from topics Tables 90.92 92 and 94
multi media content www.tttattt..... ion
ma intain translan aanssatti on memory
evaluate taanatt.traslllse.. wotisaaaiion.o www.reetttrimnoogrr... ddatabases manage.termine
conduct ppr.llall.l...l.. wex.tettetttt..eeeeeeeeeeellllel text wtw.leleet..tttlaeeeeeeaaa.aa
internat internatiomal.tree wtw.lltttttttteeeeeeeee research www.ch..anaac....ccc....... checkingg
sptimiai imi zation
cross wat.formmmdapp.a.. ion
ident ify wwwguistic.pate................e
use t. translation software
food food wnw.and..eeevrrg.. -ergessseiiceee
social www.i..eerorr........e.. ing
front www.dee..k.pp.. opp.aattoiii
source verificat ion
search.eng wwrchhengiinee iine .opt
feature writing
news news-juddye j-s-juuddentttt
content.curat..
news wwt.llieeaeee
news newsswriting
copy-editing
implement stat intiiiiial ical machine ine Lranslatiion
impp wwpplmeent.oo ma www.ine.eaarinnge. aogriihts
storyte 1 Lng-and.nartn narrat.. Live wer.sstruuee suctuue
www.inoiintttttttoooooooo sm. wts.ssstehhsiee www.teehhiiiee ques
opt imi ze neural machine ne trans lat iation ion
wet.uuaggeprocsssingg
wwwoossssnvviieeeeeeeeeeeea sas.saaviiaaaaa on
mewiinssttttinn on
journali smL cont content ent wwwpppooooiiooo ion
facili paciltaaiion
ence lence
research cul tural context
www.oooooonnaiiioooooocoo. ion
cust www.sseeiiiie. www.rseereeeeeeeeeeeee. www.rviceeexeelleeee
law.enforcement www.aaiiiis............aa...immm
investigatt paativejouurnalle
break i ng www.newws... covervgeeeeeeee
fact ba rased. Storytelliin
integrate www.eaattttraslattte.e...... www.rnnssaiionaagn
reservati on management
crisis management
civic engagement
regislatin intivve itessaaatve.eeeeeeee wwe.ereeeessseeeeeeeeee
interview wwrvie.w... technii www.tennniiues iuees
headling fne mh craft eraffing ing
data journal sm
athical-reee wew.rrrrrtiit ing
deadline management
audience_edt enggagemen
visual-stoon str.llllltttttliing
analyz www.yyeeerurreeooooooooooeo pe.urceeteett
apply grammaz r rules
adapt shapt-idi ii oomaiiiiioo wmatic.expreeeeeoeeeccmmmoooeeee
utilize. www.ccooottrrsseo
perform. www.looclllaaaiiiooooo
perforn pwternnposeeeeiiii.
housekeep ing www.iinnccooooo
news analysis
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itt.aaatiaialllllaaat....... .rw.eteaattt iontioos.aom management
staff tra training and deve lopment crisis gemme apselling,teen
livestock wwtoocckiiingggge integrat i on.planning
www.innerrretattee.
susta nable fa Farming ng pract ices lyiis greenhouse management
Table 146: (Part 26 of 52) 3266 skills extracted from topics Tables 90 92 and 94
health anw.hannddamm waa.sttttyy.............oo .wwpplcccc.cccccmcc compliance lance
cat ion ion
forei in 1 Language proficience
pest rooorroo... 1. 1ep.lmmmpplllm wel.o-immemmalll lementation
harvest. L. iming coptimi iat.iiizaiiii ion
crop.disease_f ier.iidetiffi inttfiiiiinn
water, www.erveneeeii.cc.m..... iontioonnnn methods
publi c wiw.p..ppppl.oo................. omacy execution
marketing.and.e waanddprroott... iion
iustainabilin practiccaaas
multilated laterall wwwperattioo..ooo...............
global Sarallcrrii wwr.ciiiii........ iisisssi..ii ss.annggmeett
Internations iiiaal....... Lem_mmedii mediation
www.ianceebuileee. wui.buill lding
agricultural. wtw.oouttraa..aamiimeoeeee www.faarmnnnene..
global risk assessment
crop cepprrotation..o pnrroopppll. aalaannngg sanning
www.inngntennigg.. eecsiiuess ques
internations ceattionalttte www.tiinnaaaad.................e
weather wwrrrppatteee www.tptttttttaaaaaaaeeem
revenue management.
internati international Mentiioall... www.ioonnllllwiiie wwwlaw-ine
diplomatin wwllomaiic.etiio www.te.ieueeeeeeeee..c..e.ee
conflict.resolunt
global.alll wwlllalllancee..
time aere-manggeemnnn
multi multi.tasteee tasking
www.dinatiiiooooooooooo.o ion
amentat ion
internatii onal project management protocol management
quality. assurance imp implimemeennee weweettttiinnnn
fert ilizer. appl applicaal iiaat on me me Lhods
pabblic.speale maaking ing
internati intaationn oonal ional_event Levent wwllannnnnggg internat annaall oonal media. weeeeraat at i ions soil www.:ppppraaiiiio.mmm wwteepprrtiont...om ion.t techni gues
event planning and execut.oo ion invent rontrol
technolos logy syssssteeesse systems ems wpw.etteeii....... onn on
economic e se.aann sanct ions .anaggemett
internati i oonal oonalorganiiii www.ornniiiiio.... irrganization saaiiatiinn on wwtttatttiooooooo
perigationnsyse wwton.sssttee. wwtteeesaae wattemmmaaagg www.mmaaggeg. aeneeet
seed select selectii i on and plantinn ilanting ing Parm_equi wain-equiimentte pment wot..pppeiiiio ion
guest_sati i ssacttttinn wactiiiomoonioor........oo.o
ww..foormaaiion aallaiioon
glwoallsseeeeeeeeeeee wecuseeuurtte suurityyassee sment
organi ceganic_fatii www.farmingg.om wweminngttechin techneeucheeeeeeee
imp. Lmpl
hydroponics.sy awroonccssyse wttprissssssssssseeeeeem ssttup
diplomat ic.negotiation
foreign aiid wwt.aaiidaaaaoa Sammiiiste wwwmminittraae
soil soil wwtriiiiiii ent www.ient.aaaalal.. analysis
precision. ag wwwricutuurre.....
culturall www.ralssssssittee sessitivity iviity
financial budgeting
complaint handling
internat onal wwwooaaaalllllo
team. weadeeaaderaae rship
problem_soli wellemmsolvinn
treaty drwftinggnngn.o..........
foreign.po pol.ppollyyy.yy.. iic.-yy-oo
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leleemtttat ion
boodaaafeyy.ooumennt.ao inranaatiionnaaa ion.management
wtw.rreennnccctegr....... ianion
syncret.iss.... sm identiidi denniificaaeo fiiattiiion ion
wwllgyycllarr www.ccaaaaiia....... cirriccaiionn
coomlliance
eeeetation consensus.mechani sm. iemmimmpl....
farm data gennn and ana lisis agri agricul celluuttrral drone operati ion agroforest wwr.yyymllleee food_safety.manne
trad.trac cetraceabilityy lityylyyyy. www.llyyttttttttteeeee wysss.ttee imp implimmmmnnteeem
food food-safety" www.eeuuipppee eqw.imeutttteeeeeeeeeeeemeee pment ww--mainn mainnennnnee
wwt.liytottooo...e.o.... deed gn
inter wtth dialogue facilitation
food safety audii www.onnnnciing ing food recall protocol execut io www.safttylgisllateeee.e..eeeeee
www.loyyyasssssene
www.llpppiiiiiiiiiiiiiiiiiiiii imi wemiiatiiin on
Table 148: (Part 27 of 52) 3266 skills extracted from topics Tables 90/92 92 and 94
ialatiiondee wat.iinoooomppp
ritual practice exammiattiii ion
religious. stwdies.uuuee..eee... anallysis
sacred ext www.erpretttttiii.. wtt.eeeerettteeetttttttioo.
wowoooooooeaaaalaloooaaaaaaaaaaa ysiis
smart contract programmt ng
wertttyssluiiiio on. immpmmml
religious. terminology
alod.allergen www.aalllrgeemmaaagee....
food srod_ssfety.oo wwr.yttraining.
religious.phil htt.sshhhlllpppppeeee
blockcha www.:cchiinarrciieeeee.....
not network.protocol wwt.:ooo.prottooooooo.ooooo
Priritualexper
religious.psy
scalability.ee
interoperabli
mythLand
www.riiaal.cont.r.... col. wol.onno......n.......... woptttttttt.mmm we..imppeene weeemetettinnn on
deligious.
inw.tiing.n.... ing www.edures.de.........
ineeseimpeemett..mmm Lementeetttt at inn on
food food safety data awwalsiiasssaaaaaaaa..a and reporting
lppeent www.chhhinnsccurtty....... wty.auiiiiiii. ing
reli veligion ous history rontextuulizatii ion
mi microbial 1. cwotomottmimmmm anttamiinnn iomimaaiiiin nation prevention
food food_sa www.staffiiisae winiiiii dertddeet.. deeiidintteeestieeeeeeeie www.iiiisiiiii.. innt.iinesttgttioon
iestiiaaion ion
religi ous demograph. cs asseasment:
post haaves www.aaandlin ant.aatttttragse
pess.impleee
religious anthropologyy -research religions ousioiious.uuo
swciologyyyyoooooooo.o Lapp lippaiiiioonn
compos ing and soil amendment. crop yield forecasti ng
temperature control monitoring
ietationntee on pe-ppreeeetioo on
food safety wyw.yyuluuur ture promotioonnn on
sndecoding
int.iiinnettttttiii...... ion
wonflict.reeeeeeeeoooooeeeeeeeee reso. www.rresslttii.oo lution ion
religious law interpretat. ion ion blockchaii in system devel ogment
ques ques
www...sssssssssseeessssssseessss aesaassssssssss sment
sment
personal personal.hygient www.hhygggeettttcmme weniniaiaiinn
comparat comparative.ree veerreeell www.eeeiiionnaaaa....m.mm
dist www.bbbtteeledde........ eeggi_designn www.ttograppy.ppppiccc...
token es cs www.mmoeeei.. mong
ing
rordestorage.bes www.ageeeestttt.ee www.geeeest-ppctttteeee
pest cont rol management
www.yapplicatiin
religi ous w.s.yooolll 1i cioooolssmmmm
food wew.ddsafetrrisie wir.-rrs-----.-.
cross_cont aminatii
religious.ate www.ssartt...
cryptography
canitation.s
religious wws.ouu..aaa
religiouse
Skills
101

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Published as a conference paper at ICLR 2025
ementation
wwi.mmmmmmettttt.... on
data stzucture aetimizaati ion blockcha in performance tuning ng blockchain wwwechain.api.ce inapiideev opment
resource allocatiionn ion. wwt.icctiittttt..mmmmm
Table 150: (Part 28 of 52) 3266 skills extracted from topics Tables 90.92 92 and 94
theatri iertricall caal wwwforranceeeateeeeemmemeo arts
strategi strategicidech c.decisidinno siioonni poocisionmmakinn
long long term v sisiinnn.ii www.ccraaiioooo on
qu i ck change techni ques
power.gr www..grriiipppt. imi inttimmiatti zation
energy www.raiiepplaninnggggg.n mi wew.ddddeeiinndnne.eeoo
ba balanced w.w.rreaar....... werddiimpp..
awareness
swot aes-analyye aer.taanallsiss.... www.s.cresiiiiioooo.ooo.o......e
forecasting
portfolic io management
emoti ional expression
#lockchain.test.e www.tettinn....e
timing and pacing costume.rtt management st atage,eee combat
backs tage.eti. queteetiuettt Liqute
scenario carrro-pplamne p-plannnnn
vocal pocal-prose projjcctoiiii in
makeup. application
li light ing wwt..cue.awaaae.e.
voice ice modulattiin iion
1oad_forecast ing
market.analysa analysiss
financial aamancial.forii
Seript.analyese
market
its.ingsssstemeee systemss
blockcha in governance sysLem develop Vopmeet peer to peer network management
tam.design ig www.innwwwihhh.
wamppeeeetttt.i.. io
goal wwt.seeitiii ing ng and pr perioriiizen peiiziiion ion
blue. www.nn..ccn.. st wws.strttggyyy earttayyyy...o fo www.ulttttiiooooo lation
risk assessment and mimmmitt miti gation
strategic part part.nrshhi www.dveeloppee
renewable e enwrgy-eriiineee wergyyiineeee www.ooytttttttttttttooo.o enttttttiiion ion ion
business www.ssstttt.y............... ppl anning
aandttracking
watnttaandddiii and alact masterry
demand response management
set set_des design.nnnnn.mm..... stw.uundeesaae...o anding
smart me e rrr.d wrr.daaaaa.. analysis
digital.si ure.. wet.immleene
competit itwpeeetiiiii...e. ivvee. www.enneeennnneeeee
business model. innovaniionnnn
kei_develow www.lt...e....mm.mmmm wtw.eeaanetttttttrreee...mmmm
strategic. cowmuni caricationn aation
character www.ddevelllee...... movement. and pbysicality
memori zatt ion techni ques
ensembles ensemble.coli commbleeclllll wwwlleeoollabaaaaao laboratioonn in
energy wys.systttteeee eessteemmaat tew.managemens
stakehol Stakeholder.meee wewldddrriii www.--aaaneeenttt
transact i intaiiiooiiaiaattt. wwaavaalaaiiiia.
blockchaing wwwechainiintegge. graattt
value www.accaaacco. n aalanallsii ysis
audience wwwdienneeengageee. engaggement warm up soutines
l blockin i ng and staging ng auditi on wwl-preprraiion inn
grop-handling ing
stage-presers
accent.
Skills
102

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Published as a conference paper at ICLR 2025
impl eement. a cloud data backup strategies es
technology integration cicioon.ffre for wtw.onforreeernnnn. learning
cation.perforaatt opt ze www.iiiieclllooo.oo col.uddppppiiii wtt.o/pppaaaaiiii......mii.....e bi riomechani ca -ovemment .analysis performance wet.eeeiiii meeetricsevvallee ion nutrition. pan. dewelleeeeee.eeeeeeee opment
cloud cloud.compis manage virtual machines opt im me cloud resource allocatioo ion
monnmanngemmett
power www.emmmsmmmm...........m....... stmm s stabilitbyyyyy wwa.aaalsss.. analysis
Bearring-st. wwt.ssttttttyyeeee.eeeee Lopment
wtw.agaappiiccttiiiiii on
wwl.fiiccaioooo on
study enviiniiiinn senvironmen ronment wwt.pottiiiim. iimi zat ion
sarrlearring inn stress www.mmnaaaaaeemam of wor-forrlleee
anaatiionnnnd..i... and focus improvement attention.
manage e multi i clo.lludu env ennoromeenn ronments
imm zat ion
Tables 90/92 92 and 94
carbon-footprimet war..calllce collaiion
manage cl oodds wtw.ee/ooo..oooooaaooooooooo wwwrageesolutionn
transmi eeransmission. siniissionn www.oooossssreeieeeooeeeeeee in
wwt.cloooo...pp.ooooooo Loyments
gower quality monitoring peak load mana
di aettrbbuted.geeeraatts...... iemeraatiinnnin
goal sett settingg..tt ing. and planning
information wwtttonnnggan.... zation
www.:pooooppnsssss......oom ems
orchest rat rat mim croservices
note tak ng technigut hieniiuuess
optimi weiimiieeaeee ze cl..cloo cloud.costa
criticaa www.icaa....iing....c......mnmle
aut omat..cl weteeeclooo www.omatt.....dee...eoooc
recovery .st www.etttttt...........mm.
learning www.ninnnnttllliiioooo.o
Table 152: (Part 29 of 52) 3266 skills extracted from topics
confi configure w
wwwttittttiiiiieiecii ion www.attoonnddaa...mmmm
wwwttainneiitiiiiiie onttonnteen echnolgiees
collaboratic vee. www.learninng facilitationn
wtw.eenttty.mmaaeeeeee.... itt.mmaaggmmmen
resource. eva luati on and selection
impl impllmmmmee ement el oud security. measures
laanns
iimpillmmeen lement sezverles. rless comouting
energy efficiennee Leneyyyy dasessment. fault: detecti iittion on and diagnosis
configure. el Lud www.nntttwrkiinn. king
energy- wtt.-cost-opppo popssttppmmmiii peptimizzn zation
met wewonnitive. tive.ieeee.ee awareness
act active_ wive wwwaaniinnnstttggii..i.eee
self www.smmeettannd.ee..e...
mot i iiaiiiaiiin inn. on. mai maimmainnn itenance
improvement
deploy el oud www.rastru..t...
imp implement wemeeetete..m di wwwsterreeeooooooeeeeeeee
monitor: co www.peroorraae...oooooomoo
integrate cl oud rervise ces
configure_co ind_identii
analysis
injury .riskkskk sk ass.sssssenttt.... berogram.desiggo.
design
test test-tak takk ng astrategges
feedback. wiiiineer enppettttaaat..........ea.....tt
sports wcw.scieeee ence. www.eenneeaaalaaeeeeeeeaeeaa
energy www.marak..... www.kketttaaallee.
voltage.re wwwaggeregulaiinn ion
imp Implement.co www.tcottootocc www.onnnnnnr..o..........eeeee.e
abysiological waaalddta...
training www.pporrro.....m.
energy auditing
t iime management
memory memory-enhanneee
cont ionntiiuuu...m sisiiiiiiii....
eestacogniiivin
energy energy-poli
des design gig.
Skills
103

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Published as a conference paper at ICLR 2025
performance www.ncemen....eeeemmmeemm strategieesss
row.aaltycoooooooooooooooo.... romeauures messures
fabric.cutzinie. cut.ccttiiin ing www.eptiiiiee.ee.eee... ons
bui network wee.iiingeelaaiioo..ee.ee biiiiin Iding
exerc se physiologr app1 applicatt ieaiiiaiiino inn on
optimize producr ion. www.efficci efffccenccyyy Manage.text- www.:iieeeeeeoo.......mm
mot www.oonnaaturreeaat.... aeataainteerreteen sports.specifll
strength.and and www.andddnoniiiiiooaoooi..d coning-pe oning-prononnnnoeo
90.92 92 and 94
textile.pron www.tlee.prouct... on hodds
statistical ical data analysis
adaptab abaptabiinn Recision.co a wwwaaaiiilllylll ftyyflleiiiiitty
execute screen printing
design.textt ile.pattern.
apply-fabri opply.fabric.fin.. www.fii..iiiiiii.i.iii..... www.inshingg..
operate. www.nonwwwe...eeee www.wwe..aabr...o.mm faw..fabriooom
Table 154: (Part 30 of 52) 3266 skills extracted from topics Tables
set up weaving looms
impl www.eeeeee..taati......m
professional wwsssionl...... ooiana.iiallsss........lllllllll
crit ca chinking
negotiation wtt.iiaaiiins.lll...
creat ivee thinking
caltural.avee Lwwreeess
organizational.e lioalssiiiii
academi wr www.iiinnttteteee
critical weaiticaathiini.ie thinking
perform. fabric.br..o.o
time management
Jeadership.abile
makimakin
active listening
customer wettooer.seeviii se..evicecce
imp implement.om w.w.ssssttaaeeee.... habbee-roduct on oo..poontiioon. aecciices
operate e wwwputerpp....cccc......o zed embro iooiidid ddryy machines
peprrs_psyccholoooe www.iipppeeeee..mm on www.eenttassssseeeee..
research www.mmeehhhogyy....ooo..o.ooooee appp i ication
iattiionnssssss.. issues
athlete moni moonttriin techni ques
wew.guuemmanggeeeeee dessessment
www.rss.tccholoooo iintegratiion
apply-text www.eeexiieecctt........o.oo...e wee.ttinnnnthiiee
sports equi imentt analysis
operate spinning ng machinery
seeequipmenttt
www.aiinneettleeeeeeeeeeeeeeeeee pment
souulesshoot production ielluctiio.ii
conduct wet.uutttttti//oooooooot ext lestileetestiie ing
effectiv ive communi cetniniaaiionnn ion
www.ookoooooaaaaaa.............. on
professional iguuette
cetation-llanee laaninng
amotional 1. intelligence
perforn fabric dyeing
research methodologyyy
conflict.oo wtt..resoluiion on
wwtseentatiinssseeee. skills
handle raw materiall
project wat.mmaaaaggnnn gement
strategic pl anning
stress manag ement
problem solving
periodizatio insaatiionsaao
Higital.literas
self melffottivation
operat operate_knittll
yata-analysis
teamwork-com
Skills
104

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Published as a conference paper at ICLR 2025
financial waaanniallplaaniii www.pnnnnnnn.......m......ne for.fr....o cwweer.eee..... t.th....roo.. growth
pmentt
apply food m microbiol............ ology knowledge
scholarly scholarly-soureee sourrr......... ceree.ddett.. iedentffiie ifi dicatiion
academi a style guide adherence
-checks
94
implemeet.foole wttp:nt.ood.tttccccooooooo raceetaccatt ab wwwaacaablity.eee....
work 1 fife ba balance mana mana gement
manage www.nnnggennnnn inw.ntorvyyy
career devel lopment. planning ng
Table 156: (Part 31 of 52) 3266 skills extracted from topics Tables 90 |0/92 92 and
editing
academi c. w.w.oaaalllaaaco ary usage
peer revi iivies ew parrticipat pation ion
apply fe peeeeetttat.. aentttatiiin on wecheeehhhiii
conduct a www.snnnsssee... eeeeeevllll luatiin www.soooy.eaaaatiiii...aa
devel 1 op new. food products
operate. wtw.:ee...ooooccsssssssi...
ppply-food..e chemmice stry-pry
industry industry-trenddeeee aea.eaaalalyiii communica communiccaiinn.. paaaiionsseeee
grofessional ional. et wt..tt....ieettee siguttee
argumentt construct.in ion citati patioon ion management
conti inuous learning
conflict. conflict.resolure resolutionn
paragraph.s wwraaphsstuuuuurr..
logical.fle www.wooo....aaeo.oo
proofreadi www.eaddingg........e waa.annees..
personal branding
skill gap. analysis decis prrcision.ae makimng
www.hhaatttrettmem
resume.wr wttume.writt wwwiiiiinnn
roal_setting
perform-quee
apply apply_heats
operate automated processing ing lines
opt imi ze producti i on e efffciiennne eicccyy
imp implement.om pa packagicig wsw.utu sonutions ions
pevessinggiss singiissues
Banagg-waste re www.reduccioon www.onotttegeeee...ee..eeeeeeeee
Lhesis statement. [ormul lat. innion ion literatiu turettur revie new synthesis wwreemaintenaare evidence_eval levaiuitioon
wnt.ssstreerettattt. on
adaptabilite lity and flexibility
revis sisissie on process management
analyze www.rruttttt... tt tional. conneet
professi crofessionall. www.sonnnneeeeeeeeeeeeeeeeee wew.eldeeeeeeeetgeeee wow.eotl-entolooeo
wnn..technigui ques
conduct shelf fhelflifee life testing
maintainnff weoodssffettsstaaae
execute batchachhhhhhh th processing
troubleshoom www.ppooosssiss.
personal swo anallssis
www.peerratiionn
career- www.ppahhh... harthphmpppp ing leadership.deee wessii.deeelopmee. negoti itgooiatiin at iennionnssii iti.i.siiiiii.... skills
plagiarisme sm avoidance
out line develooeeeeeee logment
mentorshipg ip seeking ing
interview wervieewur www.vveewpreppaaaeee.
academi c_t cendemicctooe.maiii.....
abstract writingg
stress. management
data-analye www.aanalllsssiiii.......
career_ research
t time time_maamee management
sel Self.assessment
food pood.process.
imp pepleeenttoo
Skills
105

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Published as a conference paper at ICLR 2025
wigencee........ imp implementatii inn ion
legal system efficiency eva vation
diversi inversify.ie srssssffliiieeee www.eesteeeteee.ee pooottoooooo
|0/92 92 and 94
legal Regal.technolosg -intiieeee inttegratin ion
analyze f financial. wtt.statttmme etteennt smeentss
legal research and analysis statutory interpretatiin
cieis
asset weaset.pppeeiie inne deveeeeeeeeeeeeeeeeeeeeeee devel opment
sion.analysi
scripting in and programmi ng
hww.analysiis lysis
wrs.appiication
maximi ze.empeeemppppp.e www.:/eeeeeeeee..eee........ee oyee.benefiil
opt meiieee aptimize.tax.e tat.ttateeess
legal process mapping
aut omate bill payments.
compare insurance polippolooo
vi rideo. game development
artificial wetiinnneeeennnn
improve wrw..tteettt.......... credit_score _score
understand mor www.dmoottgggeeeeomoomom
comparative iertaiivvelle live_aawanaaa
read-financisn news
Table 158: (Part 32 of 52) 3266 skills extracted from topics Tables
judicial wel..deciiie www.diiiiion....
legal. data.visualls
user- www.riineeffaeeeesstiee
legal. www.aargggggngo.eee
legal Regal.ethiss wtw.eethhissappppp.c..pp.p
Lexture mapping
level Level.cczt creation
inves wisely
manage. debt .anate.debt. track.expenser
sound designgignnngg
legal-refo rew.rrrpooppppppe proposal dewel opment
cross-jure pross-jurisee juri wwsjjurriiiicc sdii weaicdiiinnnnnn ionalla.... analysis
wtw.iieeiinommestttmm reams
Legal-precee precedent wtt.aapppciiooo.. caciiin
www.ound.inteeeeeeeeeeeoeoeeeeee interest
research financial products
segalstakeholder: anallyyl...aaalsi........ analysis
version.com controll management
playtesting ing and www..andddeuuuu......e. ig
case law interpretati on
legal_writ www.ll.wttnnnnannna.. wingaanddraaftin
legal_syaa syst eva evaluation
legal legaltrend trend Forecastinn ing
reducee wemeecessary.expeeeee
regal-polion cy assesssment
use use. wwndgeeting... iirriing... software
legal syst. Lm analysis
legal risk assessment
s
set oet.-innat Financial..a. goals
build eme cemmerreneyyyneee fund
calcul at e www.ee_..e weteeettwwrrrtee
worth
plan- www-fforrrette wew.rretiieeeee rement
balance checkbook
manage manage.risk.to www..ttoe.eaanc....o..o....
charactes modelinn ing
animat on ri ig
narrat ierrrative.eee ive wr itttinin ting
shader www.rrooggg.o........ amm ing
negoti i ate. salary
create www.eepassieeeiieeeeee
www.buddgtingg
greate-budger
anderstand.com
game deeeiignnnnn inn
game game_enging www.eennnnee..
Skills
106

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Published as a conference paper at ICLR 2025
cybersecurityy www.immpla wwt.://wwwi...o..oooooo.oooa.aaa coordinatiin
www.aalleccccccc.mooooomo management
exw.yeeeeiiieee... rcises
regulat atoryyyooooooooo reguulaoory.comm...i.. www.ocoooo.oomm sepppliane iance wew.aagageeetttt
risk assessment and mitigatioo in
transport rtati on network desi gn gn
94
www.aa.aaaaattseesssesse.e stakeholder.eri wwwecholderreeee www..de...eggeeee..mmmmm
dataLana Lasaanallssss yys wtw..analysssaadddiimi www.nddinterrretatee
long term strategis pl planning ng
Table 160: (Part 33 of 52) 3266 skills extracted from topics Tables |0/92 92 and
quali lilyy assurance test ing di diagnose www.mmiical..cccocc wntterrret.aabreeeee
wwwl.lconniioonn
monitor pat ient. saatentt.viit vwt.alllllll. rittllsignns .signs
Woollitysstudien
community needs assessment
satellite.com antellite.communii www..n..omuiiii. wwt.ii..coooocccooc
maintain, pat.ent...tt..i..eem.em tient. records
perform cpr and first.arate aid
conduct www.ieddalla.....m wwwiiall.eeeear........ arseerrseccch
perform physical thw.rttthrpppyypc..e
infrastrrcture.co www.turrccaacttyyyc.......
www.yapppppiiii..........cm.i..p inniiiii
analyze mww.call.aaa...m...m..mmam.ll... wwcccalmmaaal imaging
aepesteaopppaaaiiiao on
manage.chroni..diseeane
assist.is in www.chiiiiii....... hhh
operate reddicaldeviiee
budget forecasting
cryptography aappllpppi.................a....
conduct www.ruurgrallaaaeee.....clllllaa
game balan alllanning iig
urban paaan.plaaa pl planning
invironmental.inn
infrastructure...
radar_ www.ssysssseeeeeeeeeeeeeeoe
interpret wwrpprrteeegg.o
manage-chromm
faw.fiiaiiaaaaaaaaa on
infrastruct deve Lopment planning
counsel pat i eentss s on hea.thhiiii aechissee ssues sterilize ize medi ca equ equipment pmmnt
prescribe. ieeribbe.ae appropriat remeediiaatt cardicatiin i ons educate_pp -pat ent
conduct www.mentttlll.. al hhehhllll www.hhhhhhhhhta www.theevaluateett.eeeeeeeeeeaee www.vvatttiiii ions
con.system.usage
pnmiimm impl weleemeetttttt atentation ion
interdi sct peliplinn lirraryy co aallaaaratii ion
www.ymedddllc.... care
performmhe heal th.rr mhrriskk isskaskaa sesssmennts administt miiiiisterr vaccinat. Lons
perform porforrmphys-eeem aaanssiiaaee ions
www.prrrrrrmm.... ng
climate resil ience pl llanning
constructic wtw.:/hasninnnnnsseee......
systoyyyyytteeeoeoeoeeeeeoeeeeee ems
analyze www.nurittttt rittiiii ional. _eeee
eessssstesspllannig planninn
unmanned wwl.eelione wwreeicle.gee wwwpiiinnnn
public_prive werrivateepppp www.tteparttersshaee
multip mulliiplayer net workingg
iinss
develop-tz t reearttttnnn wreettmettt... plans
www.rraphc.inffrnee intiion
demographies www.le.tteenddaee www.eeenndaaall...............a
edminist seristteraie www.erreeeiiiioo www.iiiiiiiio....
cost www.tteeeeeftttaaeeeeeee analysis
health_ane aechaand_mst www.meiiih cine
resource allocation tn
sustainable. wei.ledddiig... iign
project management
provide www.emeerggee reeney
project .management
land use planning
Wrility-systems...
wrrenseetechoolooe
missile wereill-ggiidannn
Skills
107

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Published as a conference paper at ICLR 2025
perform. perform.data..leen aaw.aa.lll..nnn.an...p...p.. pre.anncpppproosssinng
inteenntetttit. ion
iin
weeepplinaaee iiniinnrrr. www.:coomuniiiaccc intuunicaaiiio ion
command and control optimizatiin manization
interpret confidence intervals
interpret data vi sussluaaaaiiiiiiiiiiiiiiiiaaaaaa ions
wwt.oooptiiiiaaioa...ii.i.. imiziiiiin zation
safety-prot wwt.tyy-poooooooiiiooooooooo.... 1epl-mmmpllp at wwwtical.thinikee.
Table 162: (Part 34 of 52) 3266 skills extracted from topics Tables 90.92 92 and 94
aal.studiesss
weapons sys Lem integrat.ion
www.sttaissii
design design.expel wwtppneeepprrreeee in.ign_tee www.rieetaaalttaie....oo
calculate.dee welessripiivesstttieeeee.
conduct www.ccllstsseeeeece ana anw.aaaliisssss.. lysis
physi ca science research
artificial wtt.fficaalintellll wentelllggnnn
mathemat ics -anddaddsss its.aa.dsstatssssts...
sol ve compl complexx ex wew.eeuuiiiooo ions
apply wwy.aayaesss...e. sessiinninff inniieeeeeee
grant proposal writing scientif ifitiific fic presentati in on
computati inntional __mdeellll modeling ling
equipment. wroubleshootiii
ballistic itstiii sesicciisssss f.missileedefeer
use use.ca www.calclllss..mmccc for opt.pptt.....
use_matrin ww..peratiiio. ions
Jaboratory www.oryyeeciigeeee
literature. werrereeveew.
data.visual wwtisssalaaaiii..
combat combat.simulatien wis.mmmaatiii Seealth.techholl
data.coll leciioinnnnnnn
erroz. analysis
interdiscip iipl
hyply-lineare
apply apply-game.Ti
battleffeeld net wetwork administratiin riniion
Flectromagner geeminggeiiceom wwweeiccsppeeee c...pectttricec peppptttuaaam uum manageeennt
directed Lenergy wwapon. mn..ppertiion
conduct monte carlo s simulatioonn ions
sensor ful.fusiioooo affssioniiii poloonnnllllee imp.ementatiiinm in
data-link. www.sssttmm.mmm..... confi guration
quantum computii iinginguuu ut lizattin ion
perforn.c st attii sttatsttiit issisiiis iical.l. analysis
perform mwwt iettiiiaiittttt anaaassliss
s.l..smmlaaa.................. at itonss
ions
apppicatiionn..........p.....a.e cat inn ion
conduct te imm. ser weries analysis
imp I www.eeeeeerggeeeeeeeeeeee www.ssioonnnallyae
research ethics cs compl iance
threat retection ion ana analysis lysis
mollmmnd
apply probabil bibiilityy lityyyy theory
use-mathemat wtw.mmmeiammmmeee...aoe.m
use-statist. wrricallsofftaatee........
perforn factor analysis
hypothesi seressss..ie.... w...orrmlattt.. comuuaatonn
inst www.uuuttaaaaa..oom.. calclllii wet...calbraaa... libiattioon ion
reeereseacch
scient inveettffi walffi..mnnn m.c.eehoo..ccomo wwwoddapppi.coo
statisti sesitiical.....o Lanalysis
implement .m wwwachieeellee
scienti wimntiffi.wwwr. w.iiiiiig
peer review process
col collaborativv wwtporratveeeeeeeeee
sample- wwwpple-prppraatee. ion
perform_nun www.mnuueriiccc.m.mmmom
dat dat aataaaaaaaeeeen ement
problem.sod www..ssooinng......m...........i
apply apply.differe
conduct
Skills
808

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Published as a conference paper at ICLR 2025
Patellits commun iiciiiinnnnn imp lementattiin ion
emeenation
accident-inee investiiiiiiii gaggtatiiii pattioonnnd reporting
www.-iisseciiii. on
www.uuiiiaaaiii
noi poiselleeee monnitor tori ing. ang.aadddoontrr rol
el mlimate change adaptat on planning
environmenta. iilimpaccc ct. a assessment
wwwewable..eeeee immp www.implenn eemeetatiionn ion
interplanetary.t. www.ttrrrryraoo.o wesstrr--paannan.
construction safely sLandards
orbital www.mmanicc.caalulaaaaa...a.aa on
Table 164: (Part 35 of 52) 3266 skills extracted from topics Tables 90.92 92 and 94
pootion.systent em. wet.impleen
planning
hazardous waw.maattaaaaaaememeem rial haw.aandliii inn
electr r ca safety management
environmente data analysis
natural.diss dast er wer.mmitiie er.miiigatiinn
saace.robottcs.ppooee amming
www.:eppppppp.pr.a....... ion
space www.:eaathhereeeeeeeeeeeeeeeeeee woe.hher forecast.iiiioo ing
www.sdesiinnnnnnee gn
safety-pla ae-planndeve lopment
environment. www.oment.aandie w.w.andrrrusuueeeee.e
sustainable it www.saannpotttaaa.oo.m..........
heavy www.aacchh www.chierr.oppatt......
ecosystem wew.rresttaaiii on
green bu i lding design gn
air ser-qualitt Silaiityyaae wtw.uualit.oonitttooo.o..m...oee monnmioiiinnnngggoooo
space sui I. eng wng.engiiieee. ingineeiing
toolanndeeui wew.nddeuupeeeeeee weeuuipmett...m
rocket.prose www.ee.proossssoo....mm
space navieat hwaivigaaionnpx....
fall fall www.ppoootiinn...
extraterrest
space.
risk assessment. and hazard ident: fi cation personal-protece wrrectiveeegeee
www.euuieeettteeeeeeeemm pmmet lectiiio osha_regul at wwon.compliaaee
thermal www.troloo..o..ooooooooooooo.oo. sy..ssteeeee ems management
biodivers yt wtwsssittccnssee www.onsnsvttt........... ion soil cont ami watiintiiiii ion remediation on sustai nable agricu segriculiie ture practi ces
envi www.iroomeetaaa..mmmm...mmm..m.e aeeenaaleedcatt andiionnndouuueeeeee environnne anvironmental al w...iiikmmme www.rii.maaaageeemm.mmmmm.
research pro ject. management.
scaffolding sa.saafe safeetyiiie weeetyiinsspette... ion
confined seace een ry pprocedures
safety safety-train, www.ttrainnnng. meeiiinggPeee wawaiiinggooggraane wal--rogrrae rar. design
workpl ace ergonomics cs seesesmentt
fire prevent on and prot inttection ion
iiinstratii on safety-si gnwge..ggg....... ieeeemmmpp-ee imp lementation
sustainable resource management
waste waste-reduee www.eerrductt... iincctiion... Lon and www.rrecccliin ing
water_cone conservonevvtttooooo won.techniiee. ques
carbon carbon-footprr footprint wt..caler w...callllaat... collculaa eeuulaaiin on
environmenten spoliceit siccccarrn ccy.analssis
technolocgyyyy
spacecraft wtw.craaa..sstttteeeeee. syst ems. www.enneniiinnnno....emmem
miission.pl monninggann www.innnnnnaaaaalla..n.m aalaanallsss
life support. sys Lemss development opmeett
zero gravi ty adaptati on tra iaaiinn in
miteet.mettt.mmm www.iiigeee. dn gn
wett.ccossuuuucc ion
minggdeiignn radiation. wwweldiiiiiii
site_security watuurityymaatt myaanngemenn
www.eeeerimmett
www.aidaand.co and www.ccppaacoiio
space.debris.tre
space_habl tat
microgravis siavvityy
first_aid
space eep_eexpllooo
Skills
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content www.aammman..a.m.mmmmaam sys wes.ssysssmmsmmmmmmm tem..mm.. www.opeeati... oo
special wffectttts...............effffe. impp ementat attnnatiin iton
www.ld.fffiuuui diffficulle fficutiies
Table 166: (Part 36 of 52) 3266 skills extracted from topics Tables |0/92 92 and 94
wwtpppa...cooon...ooo..mmm anisyss
adapt to different audience. ences overcome stage fright
www.teehniiee ques ques
improvised wee.anntthinkoonfff fnefeett
ancozporate.stooryeee eliigg
dimensi comensiooaali ional lity reereucction
data analys is me Lhods itacattiaal iicalll wwwoohhesssttssiiing cime-series. wea.eesaaaayssii
analysis
predi iirdictiee ive mww.deiiingg ing
co vorrelations iio anaaysis
maint ain. eww.eye.com www.eey...ntcctt..meommoommm actt
handl e.t wew.echhniiee cal, di
dat datassamp aassamppll wwwliingtteeee wag-teeciin
feature. www.resselectiiin
sent sentiment wateeeettaalllaa..m
multi wwr.iaarrrttaaaaaaaaee
publi cubblic. c... r.c.speakinng
engage aeveeauddeenee
language
creat e www.mmemmaaee....mmmmmmmaaammaee
dat impuration
ridee_eddita liiiing.
graphic design
script prittng
light ing.setup
color.grading
use use.body: body www.boddd.llgll...
color grr.ggrrinng
principal.c
3d 3d.modeling iig
statistt iittstti
cot.iiaaotoeeetteercttttt..... weatt-prouutiice ion
speech.effectivee eveciivlly ly
www.hheeooicaa..d....... content
launch vehicle integrat.ion Lon dat cl ellaning
ng management
wwwaaphhcccc... www.sceeeiiioooooo ion
exploratory dat..aaaaayaaiii..a....aa..
www.apppprratee...
delivers peliver_co ccl ear sar.message
manage-st age.presence
manage speech timing
animat ion. tec.niquehsss
data_vi sualization regression.anall ansisssssssss
exper experimente www.mettnlleeeeeee 1.desiigg
structure www..p..eeeee.....
atilize visual aids
storyboa rd creation on
out lierrrree www.cteeett.... ion
bayesi an ana analysniii.. lysis
project.coom .coof idence
mot i iiriionfoe sootin-grappp www.nggraaisss.crec.c
data-int www.iinterreetattte
data transformats
machine.leat machine.learningg
mult poltiimedi iittiiidia.. weaadia.conteee
camera. aprarpittioon
Fluster.analyge
audio mixing
live.st reami reami
text www.iiinggggggg
arsearchLande
Soanddrecordd
use use hww.r..or....
Skills
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seiiionddeee I opment
xr xr ww..prtootyp ing land www.rrppdddeeer rep.d.deeellopemm....
wimiiiiiatti on
spatial.comput inggiinggign 1eg-immp1. ement at inn L
development
interact ive media devel lopment project management audience. ynaaissss
augmented real reality lity programmi ng www.aauddiintpepppe
Table 168: (Part 37 of 52) 3266 skills extracted from topics Tables |0/92 92 and 94
photogrammet for asset
deadl deadline ine www.suresssseeee.ee.eeeeeeeeeeee hnndssurehaadd.inn
cross-plast www.affoor.aappaoo.aaaaaaaaaaaea ani
winwwin sowssooliioooos oo creat in
digital asset management
emotiona intelligence
pat ienieene.co www.eannntttiin wwtttiminggaaani
ethical www.ldeeiiiio.....o mikinggking
3d model ing and design
opt
Shader-progrann graaiionnnnn xr wr.hardwarre
for-for-x--
xr int.iratact. ion design
mi mixed mixed_real reredrelalee aeeealltyyiee www.litt.ppppcaaae caappiaatiiiiiiiiiiiiciiiiiiiiii
t www.spporss..mm rt www.teeeeeeee te techniques
real ime wwwerrenderinng wwwringg-oa
strategi weeigiit. clppannin
fultural cultural.awareo Lawarenesss
negot irrotiationaa stynssslllee..oomooommmoooomeeet
active.lin werive.listentie iiiitiiing
probl em solving
rapport b.b.uiiiinnng
risk asseaseett
gesture www.ureerecooiii....
dataLana lysis
xr zt.networking.arg wanggaandmut www.annwww..leeeeeeeemm wwuulliplaye..deeeee..... develepeeeo
wwwprtelllnnngeeeeeeeeee Ling. www.teechiiiiii ques
uoltuteri ic capture and processing ng
extended reality development. virtual envi ronment creat on mot wer.ionntrcckeco iip.immllll.mmmmmmmmmmm Lementation on
performance opt optimitiiiii iotimizaiion for xr
waalityyaaee assurance
alternatio ivvv ve. oppt ioniion.geee gen.generatt iin ion value creaticecii.c............. on i i cattiiiiatiiiii inn informatio on gaa.teiinnnngg...
video compressiin ression on techn iichiiqqus ques
content_ste sententtstraatte waategyddeve 1opment
wtw.uaggeiiteeeretttt.... ion
www.undrrsttad... anding
power dww.mmimmmmmmmmmmmmmmm cs unndesstanding.
oser.interface www.diiinnneeeeee fgn_for_xx _x
hapt maptic.feeden feedbacc wii.ingggationn
www.pll...oo...a......m.....mmmm xr devellepeeeeeee
social media op imizatiionn
www.use.eexpeiieee wwweerienee.dee ience.desiinn desiggn
persuasi ve commmmnication conflict... wtwlicttreet www.oouusoooo..
assertive ivene wreness.ball ael.aalar ss.balancinnn.. anciing
wew.rieeneeeeeiinn.. design sign
negotiate at on st www.sttttttyy...o
stakehol der ana ana lysis
concess i ciononmaa nt--manngeeen:
immersi siee.iie.es st www.sstortttttttt...
computer wwweeriviiiiie wwwisionnooooooo for xr
xr.content.cre ceaeeeeettrrttttee centtccreaionn ion
user.experi wwwpperience.dee wwwieenne.cccomm
copyright wewriight.tt 1l. wwwlwwwddddee
settiical.hinniin
body www.--annuggeeiiii
xr.testing ing and
cross-plat follffooonn
poal.setring
Skills

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Published as a conference paper at ICLR 2025
equi e pment ef fect velly practicce cennticceinian www.innuuypreeeee wwweeetiioneeeeeeeee...
imple implement. defensivv ive formations perfort ball handl lig drills
conduct t Lechnology forecast ing
wwaluate.regulaate compliance
90/92 92 and 94
ses
master.spor sr..sprrttt www.tttssiiiieeeeeeee...iie werefficcuull..
www.innooviiiio... ions
create technol logy roadmaps
styy-ootentii
Vanage-innoval ionovattooooooooo.....o wttp:/pooooo..ooooooeeooo
develop-proct ww..ccccssppsssss
commentttactoo
adapt. to.tttto.. gangaamee..... _s situations
implement. zonalnal mark wiiigg
social media management
special www.eefftttccccfeeeecccccc. w.creatiioooiooooooomcmmooo....i
analyze. wwa.zze.pppoeett.mm..m maintain.j wwt.pppopr.ppsiiii...
analyze market.trend.
practice ice www.teewww....eee.me.o.....eee.e
www.ccomllannccc
ident seeenify-diis. ...ddissn www.isrrpiiieeee.
assess sesesssscalable www.laablltyy..aaaaaaaaailllllll
Table 170: (Part 38 of 52) 3266 skills extracted from topics Tables
content.cre wertt.creaiion ion
music c. composition
brand devedddvelnnneoe oomeett
trend forecasting ing
predici ct www.fuuuueeiinnne.
public... c....... se.spekking
live live_stre wer.sstrrminng
event p1 alanning
news_reporting ing
media_lan law watallaaccoom.
Aideo_editimg
ut itilize ze ze
wtw.aaaaaccllloaaaaaaaa....a.... on
feasibility
execut w.s.t-pppe wtw.eeeffeeevvll.o......... field
integrate ewwrging.t wechoooooiie lggie
perform tact cicalll wwwstussti............ iebbsiiuuttt tut ions
perform.phi wwwppphcl.cooniit......oocmc.e.e ioning
evaluate_ed wettttteehhcaalleeee aeiicallimpi 1-impl licatiii ions ions
perform compet iti ve ana lysis
execure pass ing Lechniques apply offens ve st sattggie
execute weintinggig maneuvers
execute wescoringgteccee ques
www.gingg.echholooo rends
develop adopti ion. strategies
riiks
-and__neandddeeeeeeee -and.mnddded.... media dia
www.ondducinn
voi woice.over wwr.aaaaaaioonn
audienceeennaee eng.gggggng.nn
assess. assess.cyberse www.scyyyereuuurre.eeeeeeee
communi communicate wetpuniinateefffeeeeeeee
-esee-plays plaass
entertal ainment
audio production
digital mwrketrrrinn ing
podcast wwwtootiiii ing
content curation
evaluate. wwallate..eccholee
assess- www.sstcilll.......o...........e
wwcilitate.cross.dee
develop-s Pevelop-spptiin
script.wris wwittwwrtiinngg
graphi c.design
media analysis iss
analyze saalyze-ppate wateeepptett.....
coordinate.ten
sound gn
emerging.tedd
interviews eew
Skills
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Communicate complex technical concegts risk asseasment
pract ice ice cross cul:ural communication
www.ppeeeett...e..eeee..e ions
ime.effectiiiiiiiicciiic infectiielly vell
wea.eeaallatiiei ion
provide de de const ructive feedback rec use subjecs jec. speci fic resources
Table 172: (Part 39 of 52) 3266 skills extracted from topics Tables 90.92 92 and 94
srcy-identiffcatte in
www.ssseerii...e ty watuurittmmmanttna -aaaggeeent
www.quipmeet...mmmmmmm seeeeeeee relection
Facilitate group di sicusssooss
create fect ve sual aids
write.concisse se soenccse.businee.. www.uisssssss.......o.. memos
utilize wellize.educaal edw.atuucii.ccccce ional l. www.hocchollggeo
leaaninggggoas goals
secur ity awareness t traiin raaining
weriitttiinggratt.. ion on
manage.tttoriie icce www.eceeeeeeeseeemoo.e. sess devel develop-pract
soactingg ing
www..cooonnna...mm.m... cat catiicationnssste son.style
'uuiddsstueed 1tw.:l.sstueeet.oooo.ee1eeee... ccn.fience.
security audit www.cnnoncciiiiccc
develop_is Lop. iepiinteaa http:piinneerrressaaie.....
subject. Tubject.speci.ee weccffic.uutooiii.
rolee gn
ident identify ify seentifyylearniie learningggaps
security. tecnnoleeenoooooo chy ogy
wwwsiness.commiee canticaatiin.
deliver www.eeeffiiieeeeeeee weeetiiveeeeee.e
iyplain.compl. www.ccowlllccccccoooo
wommplexxcorre
wulnerability.ioe
security. stwrityydee wepurityyequiimmee..
active.le Wisteningn
personnel wernnnee-ssccuc
cybersecure wwrrssecurity.io
adapt adapt.commun.
create.perse
caaiiii on.strategies
wtt.:wttsssuuttt..t.. uddnts
woiingttecchee ques
utilize di gi tal communi cat in tools
styles
conduct Lechnology due dil diligence gence
lesson-plsnss
foster priticall thinkiin.iiiiie.i ssills
commun i cate sertiveell.ee weywwttii th
security-polic wtw.:pppoyyyeeeeeeeeeeeeeeeeeee oppeett
conduct product ive ings
wwwsinssssreeee rerrrposss
use-nonverbal www.a..coouniaaa.....mm.... catioon
co-plannnin Lanning
give construcc ruct ve feedback
security system planning
perimeter www.sseeccttdess..ee gn
ion. wwr.ttrtttgy.
compli ance regulagguettl aallltii wwt.ioniinntnn
write.pp www.ppooosssioooooooooo onal emails
negoti i at busi bus.iusssss.. messsnss..e .deals
manage. www.coofffi.... ict resolution
wenternal.comme
assess sudett. knowl edge
address indrressiidivile ieniiiiidaallii.......... weriiuallllaaare
adapt. Leaching methods
track student progress
incident r www.rresssospppl..eeee
mot motivate inetivaaendddco www.teaanneegggeeeeeee...
craft_clear bus wwt.arrbusinnssee
surveillance.sy. wat.llannneeettt....
budget modget-alloce all.llllolll allocaiooon
securit recurity-star wt-.styffccoooooo
Chreat-analyse analysis
implement asses
employ www.acttiieee....
seeaat-personaa
dat data_pra www.ppoooctt..c.o
implement-imge
master-pule pur-ppubll
Skills
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wew.sttteeeeeeeeeeeeeeeeeeeeeeee www.ddeeeeeee oppent d
create support ive learning env roomentt.
urban revitali iettalizaattt matiiiiinn...moo strategr creat ion in
ien.gimmmm imp Lementation
literary device iee ident irentiffian faiifiiatiiiii itationn ion
rhetorical wawrrical... nevice.recognitte tion
Table 174: (Part 40 of 52) 3266 skills extracted from topics Tables 90.92 92 and 94
perform. gww.geeocchmccocooo cali Lnnalysis
zoning regulation ion analysis
Semographic.trentt wtw.apiiitttena.pppppe....ee...e fenddforeeccsttttteoeeoee
anvironmental.. aeeentallae www.atttaaalluaeeeeeeemeee
www.kability.asseeeeeee aaaannii ming
interpre wesssatellttee itte wwiimmaarry
measure.a m www.oopeee...cooossiiiiioooooioe
perform radiomerrin dating
monitor vol volcaniiiiii calainic act activitt iivityy
interpret.palecceee wewoollmmmee.aeee...oo... edata
operate www..aa..sss............... wwwperottteeerrr....e...eeee
green space a allocationn on
urban. rww. wwwraanresiieeeeee wresilieneeepll..cc aere-planning
analyze -eriicce www..ee.....eeeeeeeeeeeeeeeeeeee samples
www.eeerock..... foallyze.rock.oorata......... format inntiions ions
urban grwwth modeelnn
model pl plaae.rr wet.eteettnnii cs
sustaina iaaaalaalle desi gn.
affordable www.hhossingeeee
analyze gww..geeelggalll...ooo....o...ee
analyze sei smi amic.daa data
literary wat.ssanalls................msst
critical thinking
www.recogniiii
genre_clasee cenre.classifiine walasssiiaaaiiii
romparative ive. wwwpraativeettteeeeeeeeeeeeeeeee
analyze.rooe anakyze.rock.F
symboli wembbolismmrrer
barth.sci
www.ffaciiitaaaaaaa on
Intgrntttgrtti on
iontioonsee ems
wew.erreeeeeeeesseeeeeeee senttt
operate remote teotte.. sennsinngg eequi pment
conduct www.hhydrooiccc.ooo sal-asssssmm sessessment sments
radar
meveloper opment pl. anning
analyze ocean current patterns
www.teex.evaluatiiii... ion
mixeduseedent www.sseedeeeeeeeee..... _design
urban devel lopment p laanningg
operate ground penet.rat rat. rnging ing
traffic fl ow aettmiizaii on
map.geological...... www.ooocal....aai...ooooo..o...e www.fooorttioooooo ions
provide a Ludy strategies
community www.genen....aaeeeee......
conduct. field surveys
model podel_climate www.lmat..ssssee....... e.systems
collect. sediment..o. cores
analysis
conduct. wnw.rennnnnnooinaa....mmmm
analyze.Iree. www.ze.rree.re www.eeeeeiiineeeiee a
conduct.gedt www.eepyhiglllssuuieoe....eaeeee
textual Interpretatio on
character analysis
smart www.cityyytcchool..c.
historical. wttprrica....ontt.teoeoeoo
solduus--plannngg
urban_densi sitasitymaatt
theme eattiiiiinninnn.oc.ccci....oo..t
wwwlisstcc.naalyee
economic c devel
conduct_soil.ang
close reading
marrative.persee
Skills
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mergers aad.daaddd...... acquisiti citiisiiionss planning in
cultural context interpretation authorial intent analysis
financial stttement..ttt...mmmm preparationn
wwwinnssseethi.... st.iiissiiiiippppp.p......... cs_impl wwweeeetttt...eeee ation
90/92 92 and 94
literary wwweeeeentcttteriiittttteeee
literary periodident: conntificationn
debate ioonstituu.ional.... issues
evaluate, policy ivennesesssss
tww.on.taasssttiii..... lanslationn
www.eadddrshhip
interpret legal documents
conduct public hear ngs
business and eccnomicc cs
cost besst.eeefiitttae..... aaaalllsss
business plan writing
organiza wwt.iiittiinna........ wewttiinnlleaaddrrr.eee
civil ri sights Legislatiin
competit iivee anaalsiisss
assess assess.societs pocietallimpart
www.oornnnaaa....ooo..o content rrganization
data inw.rgretatiion
Table 176: (Part 41 of 52) 3266 skills extracted from topics Tables
economi e_modeeemmellleeeo ling
ident ify ireeniffydissrimiii
build buiid coalitions
risk man.gementttttt
research.net www.rchhneehheee
iitigate.civil wwweesiggateriii
informat www.emmaaiiinggeee
technical moanical. leaal-aarggen
market-resear
investment.at
sraff-policyee
wompommmiiiiooommmmmmmmmmmmmommm romi ses
ident.fficationn literary theory appl ication
www.rreeeeeussiiigee and hww.pand.iinoova....ae..a.. inninnvttiiin ion
technicon ca 1. ing
ing strategy development analyze hi sttoriiio cooricalll cal context research.
mov ements
implemeeee Leplemntt complicii iance measures
literary wwa.ptttiiino on analysis
busiines process Amprovement
pricing st rareegy..y...... www.dddeeelooee lopment economic wewnnicppooll pol.poli. seiiccyannn
www.aarycrriiiissiii... ing ing
ietiiiaiionnn at on. and deal making
ex-idees
sopply-chain.opt imizationnn
educate leee-pullicee rights rights
citati ietaaion_aiin and.aadd.re.. re referencier
www..e....esseeee..eeeeee.e.ee.e www.legiiiee www.tliiilllillee.eo....aieailia welisiaaiiiiiieeiieeiiieiiiiiiie wee.ive.coo
wwwiingggattttte terns
internati i onnal ooa.otraddeeeeeaoaaeoaaaaaaaaaae wwwetrrdeeennne
figurative. ieerativeelle www.rraiive.laguag.....mm
financial analysis
strategic e pllpplannn planning
gement
musiness-vallie wat.vaalliiinn
analyze.vote vevooiii wtw.y//.ttn...pattee......m.
budget- www.foorraatinno
med fate conflict ficts
audience aaaaaaalii... lysis
organize wtw.niieegggsssoeoeeeeeeeeeeeeee
project wwna
eitertextualli
advocate. wor.fooreeeee
clear ting
communi cate
negotiaten aatiiateelll..
negoti
Skills
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negot iotooiaaiion ion and consensus building
troubleshoot: ing. sei-guiiee de wee..ceaaioo.
policy_ime iirimmpla emmenttt at intaatioonplllle oonpplaniing
evidence ba ses.ddeeciiiissi....... ceiissioniaiinn
|0/92 92 and 94
www.immppeeeeta...m
software. wootlare.ooloooooo.ooooooooooooo tool ppooficiecccy
stakehol deer.communnine ceaiiiiiiii on in
public pel-poliio icy .eweeeeeeeee. oppent
performance measurement
social www.amiiiia.. wwt.:mmmeaaaaeeeee.e.m..m.a maaggementn
network wwwookkaammtt. awwinii inniiii iemmiisstrattt st stration
style guide adherence co collaboratinn ive editing
feedback www.iinnooorrttioooomm
glossary compi lapplatiion
stakeho hochollder ledede www.eenggggeentn
cost benefitt aanalyysis
policy www.coommmicctiio..
digital technologies ethical rtthcallreasone reasoning
software iegiinniinnngggggggggggg
Table 178: (Part 42 of 52) 3266 skills extracted from topics Tables
vi visualls iisual_aiio www.aaicrrrre aid.creatioon
cross referencing
document formatiing
deadl www.eneeeeeeeeeeeeeeeeeeeeeeeeee rence
legislative ive wewaddraiinn
wwsearch.methhooee..
strategic c.p www.eeggc.paanniii. mllaninnn
project management
mobile app creati ion ion
cloud cowpudioomiiinng..mmm.o.... computinggmaan weiiinngmann
digital marketins inginng
iccessibility.i
cybersecurity wwt.ty-mmmemmmttt.....m.m....... ion ion
version control management
walllaboratiio ion
political 1 landscape analysia
zatiiatioo Lon
quality assurance............. wwwttttttiine ing ing
wwoffreddingand.ee. Meeeitiig ing
locali Docalization ikation.see preparation ion
search eng iiee ww..pttiiiae ontimiiatiion
wwg..oorddintii...
problem i dennificatiiin.oo on
document. structuring
regulatory-franee www.laatory.faamwor.k.o
database managementn
techni ca research
conflict conflicttresolltee resolutiio
metadata. agging
api atw.doommenttiiii ioi
revision_:s racking
data www.innreetttticec.
impact asses sment
policy_evall www.-evallaaiiin. ion
a ooatioo
temple Template_den iig
public speaking ing
cross.sect wwo.ooolliioooooooooooooooooooo.
risk.assessmee sment
web devel oppent
index www.ccreatioo... on on
ecenario-plannie
user-testinn ing.
project-ing projjettmmaaaee
ui-ux design
policy cy weriicyynnaaa
budget all
data-analyss
Skills
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confi gure int ruttrusionn mosion... www.ctieeee.cccoccc on. systems
design design.secure www.seeeuee.... www. www.we...acctteeeee..omoo www.karrciitt... tretss uret
artificial intelligence programming blockcha in devel opment
survey-dest dewsyddeiiee aenesign.aae gn. and immll ement amentatinonn at on
issispefforringg
wwr.ooomuaaiiioooooooooooooooo on
peer revi. ew process navigattin in grant wp..ppppllicoc liliion ciaatii cratioowrri wniting
wwwethoooooo...eeeeeeeeo.o ion
Table 180: (Part 43 of 52) 3266 skills extracted from topics Tables 90.92 92 and 94
.ss.sssttess em usage
oesponddto..cuuitty.... inniiitttinnidentt
experimental al wew.ddeinnnnn wesiinnj ---pppl- anning
research. present at i on skills
virtual.rea litlldeveeellleeeee Lopmeeeeelopeent lopment
or www.or..et................ totrllllssss
conduct risk assessmenLS
manage securitt tity compl ance academi academic.resed wwweesearrmmmeteee
research.queste quest ion.foo connffrruulllt
statistical Maatiiallae waw.aaaaaalllaiaaaaaaaalaaa wwwllyssssperfer
research ethics compl. compliancc Lance
conduct www.sseuutttyyye security-uudits -udiiss
met meta-analy ana lysis conductinn
confi configull gure network wetoorkssggmmeeee
manage access controll
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validity www.anndeeeaaabaaa
version www.ccootrolss..c.
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imp implement.o wwweeeeeeeetteneeeeeeeeeeee
it-supports rT. wwoorr.and.....
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imp lement data loss www.ereio.nnnn.... strategies
imp ement endpoint, protect on so solutions
imp lement multiiff.... fac.trttrr.oooo a. www.authenticca...... ictiiin
conf configure net network security appliances innces
per form vu www.nerabilii bibiilyy assessments
content mana gement sysLem usage machine learning imp emmet.tt ion on wnggsinttggration wwwhiingsinnnggrrree wew.eeedeeee...eeeeeeeeeeeeeeeee ion
woddsselecctino
lilaaiionn
literature.rr wew.reeiieee dew www.conddctin.. ing
academic a writing and citatio on
iilittiitiiiii iiatiion ion
ieeiiewwae www.performet rm.ommii ng
wttemett.and.uddatee..... updutett
digital projectt.......................e management
conduct wenducctpeeettre iatiiontted eettetsnnng
research www.ppap..aal................... data-vi sualizat laaaliittiii ion.creation sampling.t it eeceecciiiiii wwwpbniiuu..appppii......
weroosalddraae l.l.rldrriinnn ing
www.aaawweeeaeseaee..e.. analysis
data.colledd on. www.mmtthhhsseeeeee
qualitative ive data analysis
performathrede hunting
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research ch. woftware..ii ut wetaareeuiiiiii
pat ch chathhmaaaee ang.maaennnnnnnnt wet.taandduuun
wwt.ssiiieeeee........... responsive..o
impl emplent eeeentet.... fi rewalls
perforn 1 log anaalysi
systemat i c. www.eeevieweeeee
api. innineegraaiion
perform.ma www.mmawwwwww..................e
deploy-ve vi rtual
manage_secuu www.eseeeeittte
interview. wwwerviewttteee
Skills
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inqui ry baw.daaee....................... www.learrniii ing facilitation
create wwwppprrrssseeeeeeeeeaaeaeee welllizedttt training plnn
analyze and improve bi oimchanncs
www.teehniiii quc ques
pro ject. based learninn ng design learning ing sty es accomoodati on criti Priticalln ca critical.hhnnfe wwwinkinngprroooo on
balance. www.sttretth...mmm and www.eeednnnee.eeeemeeeeee maint
proppr wrrper-forn.cooooo form www.mannnd..stun...m
manage-pres pww.compet....coooooooo....e..ee it ietiiiiinnni Looo wtt.ionnnnieeiieeeeeeeee.ee. annintt
Table 182: (Part 44 of 52) 3266 skills extracted from topics Tables 90 92 and 94
wnnttcchnqque
peer ceartuutoinng facilitation
www.lpppepaati.....m in www.ogicll..c.
research time management. lesson-pl anniing student.as asstesmmntt
multicultin wwwural.teachingg.aae.
curri cul um. dew.dddeelooeoo opment
educati educational ioniooolllac hatppaaddataaaaaaaaaaaaaaaaaaaaa
blended leaeeddlearte lea leralleaiinngg rnainngggg... des.eegggg ign
educationns iontlllllll.... .game. deeiiig
prevent sports injuries www..pprrrrsssssssemmee.e
traini weiiiing ing program des gg in jnjury-reevent -eeveppeeeettt www.eeeeenoneeeeeeeeeeeeeeeeeeee communi www.oowwttattttttoeoee sport. wwr.t.speciffii ietritiionguiiaan
student www.ggeent..n..e.eeeeeeeeemeeeee
set realisstic ic goals
opt apttmiieennae ze wwweeeeeueiiiiioeeeeeeeeeeieeeee
psychological.
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academic publicati ion process underst Landinn
interdisci seiplininaaiiii research col aaboratioo on www.riionattttchiinn..n
condiiiiins equipment
collaborati ve l learning imp lementation format ive. Lee. bredback-prov. sion on
programming
educati educational lonal www.ehehhclogg... integration loggooy-oooo
problem.basee based. 1 learninng ming.... www.faclllie ltg.aaiiiitatiiiiiiiiiiiiiiaiiii litation
thods
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flipped c assassssoo asssroom.. impl www.iimpleeeeee eeeeetttiiinn on
iettiiiiinnntt.t ion t www.eeechniiie qunqsqus
adapti ve tea saaching strategies eesies behavi or mt-aageeeent
www.roormacc...ommmmmmom ce mescrics
improve techniggiii que que efficiency
develop-sport. wtlloppsporttppeeee www.t...ppeciiie.. ific.skiils
athlete snteemmttiie ivvat ion.sstatteiiii
indivi dual 1. sports training
differentiaten wwtntiattd..mm instssssttiiii
develop petloopmeetttem www.enttllttuug...e.eee.. totoouueeessss
manage training intensity
athlet a coa coachi ch ng methods
goal_sett i ng. and.ann..lap. aandppaannin anningng
strength and conditionins
opt opt. iimiieze ze sleep pat Lerns
team team-buil buil www.buiiiiiniiin......iiiiiigi ding exereiiessssss ses
inclusi wellusiieeeeeee www.eeeeeiiiiiiieeeeooom. wweducaaiinnppp.ee.
classroom_nn mm..maagggeeentt
performance areeanallyy lysis
analyze-per www.eeeperfrrmcccc.mm..
utilize.sports.spen www.espprrss...m
incorporate.co cross
student mot i vat
act weeiiieeliseeniine
enhance_fl fechance-fleibbleee
utilize
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gate gate soci ol nigui i at ic variati ions
changes
www.immleeeeete. lementation
wotomputttiinaall.......m ieciionall.. coonalllingull www.linngiiiis.... ticsssss
synthes ize cross linguistio data
analyze-phonet ieeeeiictraaee ienetrsssttrriiii pt poins materials.enginer www.ggiineeinn.nna..ee..e...
analyze linguistic structures apply phonolegica rules ee
design_1 inguinniiii www.iistcc.eppp..... immniimets
Table 184: (Part 45 of 52) 3266 skills extracted from topics Tables 90/92 92 and 94
_eleedee opment
www.iannuugg..ce
scanning.eles lranning.elle e wwweectren ron mi croscopy
historics aeboricall ceal www.le.dapptttttaaaa...... ion
praluate_aynte wwt.sytttttiiithhee actiictheories
ic.contexts
bi biomechanical aalliiccal analysis per oodi zatiodizztionn zatiinn pl aaannnn
readership.skill lee.issill.deellloeo
susta sustainable iaainnalle de..eiigng gn gn imp
scale and www.ppppoooooooooooopooooop
mi wwcrossttuctureeannnna.. anaanisii
gn
acoustico iesuutiics.oo cs management
stress.str's in www.erriiin...rrreeeeeeeeeee wurve.
athlete www.eesssssssset.....mo...
interior www.pppppceeeeaaapeeeeepppes
client_ wtt.:cccommniiiiiimmcmo
pro project. management
presentation. onitinsiss
examine hi www..hiisstcaaa..mo.m..o..
space space_opt opt imization
color www.orr.thhrrr.ppp.o.oo
light Lighting-aaee Lightinngdesii deg-dddeign
ergonomi i a. d.design
textile le..addddaaarre
assess-pa pww.-paaaaaaaaaeaa
woply-computatt coooppttaiio
apply
investi
scann
www.arrrprofccienc...
explain 1 anguage acquis inguisition on proceases
halrr-paayppromm--t. -promotiion
www.aadddawinnnnneee.....ee.e interreatio
o zationn
www.cttr.cooddiattt............. www..ooooiiaaa.................e ion
recovery and regenerati Lon methods video anal ys is and feedback
eeeeedyyeeelleeeee oppent
interpret. seeerpret.eeaaniii sew.ntimnniiii wawaaannicrellaiinnsiiiiii iptsshiisss
www.immlementtt... aementation
3d model windelingg in and vi susulizaiion
linguist stics theory analysis
compare 1 anguage type typologies
formul atmulate..e wiiggisticchypooeeeeee...
critique siingiistiii theories
evaluate_l anggugge uni universals
failure www.mmodem...mmmm.ommm www.mmooddddddde ident ification
materia airalllaa wwweriaa--rpprrree wwwpppppppppttooeeeee property.chaaacteerr...
anaanalsii
wws.and.fiir.ppe...
competite it i -on-sotttsttttt wat.ttaatttyyyeee. stress_ine stress.managent www.anaagnntttteeeeeeeee
building ni code. compliance
conduct di di aeoourseeanalpeee
furnitur curniiuur.dee rangemennt
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uni veiser www..g...iii.... inhiimplee
comput er wer.aaidddddeiii.n....
x-ray x-ray-diff .di www.tiiirriiiooooooooooooioiiooo intrractionnnnaaa
materiat al sel eeleettt fennction ion
mood mood board www.etiiiiooon... ion
www.ccorpuusdee analyze.corpe daata
pudget_mane wwt-mmanngeen agement
accessory stylinng
spatial p1 anning
vendor anrorraandcco waw.cootrrrat
apply-psycho poollinge
sports_en prorts_ethien www.eehhisssee...e
arendanalysis
Skills
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wnw.rntttatiii.... ion
open source intelligentnn analysis
industrial Lergoeemoeommm cs assapplicatio factory sarrr-laayy Layout, ddesign statistical icalllll wrwcesssccotrrrooo
operational teattional....mm wew.urstyyyyy... mainteeanne
mechanical ieal Lesting procedures composite materia al Besign material.see peeeectiin on. an.optimi zation fracture werreeneehhee chann www.hhannsssaaeaae
wwwpeenneeineteppeeeeeeeee int
wwwrformancee..o www.mooollli... ling
90.92 92 and 94
cs.assessment
crystallography www.iineeettattt.........
forei gn 1 language profi ciency
producti wwt.ttiooonniinoooiiimm. zaiiziiion
industrial aalallssaelycccooa -comppppopppoo liance
resource.alle www.alllaaiii son.pooolli.. anniing process. aut omati inratiion.dee on minoooddesss on-design
industrial risiriiial waste reduct product in io capac ty pl annnnnn ing
surface.t wew.eeetreatter.. saront-analyy -t-aallysiiis
emergency wwweencymmeiiieeoo medical. reepposee
http:py.cchii..orrinnnse. ion ion
tacti tactical. cerrical.deccce wel.dddciiiiie seiisiinnnnnn www.iiinnmiki... soanmmaking
informatio cetermatiiiiiiii icn www.ggtteeiing ing
geospat itnppaatiiniitttlieeeeeee..
Table 186: (Part 46 of 52) 3266 skills extracted from topics Tables
material www.zaaapeeffraaamamme.mem
threat assessment
si memaliinerceepiii
risk risk mit i gation
asset mar.maangeeeen
network ana lysis
pattern. westeen.recooge
supply.chain
perform.cpp opr
lean www.uaacturiii inngiinggilliii impl ammet on
military. wettellignne.... operations
Lhermal analysis techni quesniiquss quess
corrosion ron ion resistance eval bat ion on
phase www.dddaaaaiiiio..o interpretat on seniiteeeeeeet.aae
ron.destructio wew.eteetiine.e methods
polymes degradattaaa. on assessment
cyber intelineenee gathering
intelligents www.enee.breeiggg....... deliedeeevvery
electronic warfare techniques
equi equipment. pment. .a int wwweennnnnccccuille.eeee
product 1i Tecycle manag aeaneeeennt
manufact aatarrriiii ng cost anal ysis
fust-in. t iimeii inveenintttt wttyccontrr
s.planninn
industrial. aalaa..rooo roboti iisottttsssooooooooooooooo ww-.prrrraminn..
www.maaaaaatriiinmmm ing
mmality-cont rol www.agement
www.pattieteeee www.cooniiit..... condition iion
finite.el ement anaasssis
fatigue www.elliffeppee. wtw.p..ie..ee.........m........e www.prriiiie ciiiion
imagery interpeetatitnn on
assembll l 1 wel.eennlaalaaaa aneebalaaccig
invent ory management
covert- surveillance
Endustry-toe wwwstry-andddeen wtw.rrrrannnaaadaaooomae
strategic pl aanning
source cultivation
cultural aawareness
report www.wiiinngggg
data analanallly ysis
hamanninteell
material weerralllee
assess.pa
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bercc-ideen ident if ficatiionnn and. on
blockchain in integration ion for transparency www.aneaaaageeeeeee.aa
manage envi weveeviirvinn ronmentai ene rgenciess provi provide de emot inotionnat ona support. to patients
www.emeeggeecyy. serviccs
use pee--peronna 1-protect: seettiieeeeee wew.euuuuiieeeee pment communicate cateeeattteeee www.whtthc... www.eeeerrrreeeeeeeeeeeeeee
lean www.mmmanaaaauuuuurr sirringg. imp wwwlllemeetiin aanto
six i gma methhooooose app i iiatiion in
resource all.lllooo aoloocatti Lon. ceppimizatiinn.. onn
last-mile wtw.mmileeeeeiii..mm.mmmm....lle eedeeeideeeeeeeeeeeeeeeeeeeeeeee Melliveryooppr ry-opt immi zaiiiiin ion
ieedicationn
risk risk www.sssssseettttm aad mi m.d.miii.iaaaaie migatiion iontion
transportatiio. ion mode pelection in
Table 188: (Part 47 of 52) 3266 skills extracted from topics Tables 90 92 and 94
reverse 1 oo ics maa.aaagggeentt
statisticn iitiical process control
data-driven ven. deci seiiisnnnnaaa conmmaking vei.deeiiiiiiiiieeeee ergonomi c. workpl ace. des.degin... gn
adminis Siminister sster first. aid manage a rway obstructiion on immobil www.iiieetttttteeeeeeeeeeeeeeeee
recognize.stroln www.tyyppttt.... oms om
supplier-ren www.llatinshiipmaaaae.
process_ing www.oremmettteccnnneee
sesessment
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productions ion win. analysis
inventory wttperooryymaaagggne manggemmnnnnnn www.aaaanannttt...mmmma.mamm
iiquss
waste rewuctieentttinnnteeoee. wwweriinntecciiii.mm pottleneck.
cost www.beeeeeee...e...ee........eee t.analysis
raministerd wwe.eeeeeeeeee emergeneeeee.eee
sustainabili www.aaaaaaliyyyaseeese..e.e
demand demand_foreche for.foaatiiiinggg
route_ope opt im imizatiinnn
quality www.troooooocoooe
use autoomaed external defibrillatan lator
handle hazardous materials exposure
negotiati wwwaaiionnnnnoocmmmmmm cont www.traattaaaaaggeee.. managgenn quality control impl implementatii on supply chain twat ut ill zazzzziiii on cross_functi www..eeamm.ooordoooo
on
www.nngpprcsss...ooooo .opt imi zation manufacturing.cmm
lean www.maannannnnnnn..aoo.. sipgimmplle ement at on supply-chas www.cchii..coom
product iilitiion ion.s www.schhgggggoopooooooo imi zat ion
cont vontinuuus improvement facilitation
supply- wwwhaan.resiiienne..... ppppeeee ann minng
just inniintttt win.imeee...........mmmmm.mmm invinniettrt ory rol
data-anal aaa.laaalssi.. is a.d.nv.vii.. wwrissaaizaaiii
triage www.wmullpppp.mm casualties
operate e www.eeeegeeee.. wwwerreeeeeeeeeeee vehicces
perforn basica ceriicllfii life.. support
happly-chaiin opt im comizatiinn ion
pllaniing
performance mi ri was.tttcaiiinn.
root cause wet.ciincc.sssseeeeee.e..sssssee equipment
warchouse. wwwhoussellyout....m wwl.layttddsiiiooe gn
aut auatiiain wwttttionnnnntnn www.ineerraiiin on
invent ory man.geeennnntt
logist icc...s... nwwwork plpppaan
cost cost.bened bew.feeeettt fit. analysis
handle -nn.einuuennnnnuiieeee iiiinjuriies
process flow mapping
ysis
control bl bleeding
manage wwweecaariccccco
supply www.cchinn.ccm
treat_shiee shock
Skills
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di differetiaal wew.ggeoorttyyr ry www.ineerrreaaiio.
cerrmllaiiin ion
practiccas
predictive ive mainLenance planning eycle mime reduction
algebrai cebbraica.. w.strructuremmatte commmnippllaiii
interviews
Table 190: (Part 48 of 52) 3266 skills extracted from topics Tables 90.92 92 and 94
cross cuw.cllliio tural wwwuuuural.coomuun catiinn
forei i gn. 1 language wroficiiiiiioo. cinccy
wem.sllvin
ma t.theaaa t. ical logic. reasoning
compare crwss. wtw.lattte.eeeeeeeee.....
mathematicaa ical notation usage
cross cross-functs folffuncttii wowaal.eeanleeeeeeeeee active_lis weleellistennte
www.innerrrettatii
interpersonal www.:/oww.c.c....cccc.....
real ana yysis techn ques
www.mmodlla....
in
emot i oon 1. iintelligeenee
formal wtt.ppooooo..oosseeeoeoooo
number theory analysis
body Salylaangge anguage www.annnaa.nnneeeeeeeeeeeee
confliet Sonflict.resclng wet..resclution.
awwsentatiion.. at awttnniinssiii..o.. soinskkils
abstract wew.soniinnggg
measure. www.rree.hhrr...oo.m wtw.ree.eeoortttll.l..
combinatorin -tcc-pooblllooocooeo
mat hamatiaica teaaaalla.e conlooooooooooo observe. wwweeeeuuuuaaaaaaeeeeeeeee tural. aatural_beet
technice ical writing
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theor cheorem.per em www.rrooiinng...mmoom
compl pomplexx.cm www.ex.aaaaaa... ana leaaalsiii.s
conduct. www.guullii....
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aapplicaiiinn
wwpplemmettiiono on
per formance metrics develo t va ue stream mapping ingingg
translat at i on.andd.nnt www.inneeeetttt...m.m on
abst a gebra mani pulat aallattion ion
category-theo heoryyy wapplipllli. cation
Language and communi cet.mmunicatiinn cat catininiiiiin ion
wwwmuniomcccc.m..m.m.mmm........ cation ion
www.rf...iiiin............. .ef..iiiin ting
rituals
document www.eethhoog.ccc....... dadata
empatheti c. www.umicotttaa....m....m. comiicaaiion ion
proofreading din and editing ing
mathematin iiallilliiii -ia.llindcccttccoeeceeiceieeeiee innuctt Loo..aoaa
graph Lheory applics ioplicatiion caat iiatiionn ion
sesttiissthhor.. theory
topology.concee wwwnccetuulizaat...
nonverbal 1. communication
ioning
hattematii icallaclee ceallcalooee modeling
set theory appl cation
mathemat caa.aalpppii wal.lpoofftte cultural watttrrt.aaallaeeee..n
inliliiaaiion skills
identify fy wwlllturllssyl..
interpret. www.ccuttaalrrr.e...
audience www.nnallss. lysis
pure mat mathemationn siicsstte
logica logical.ddn logical dew.ddedttiiiii in
axi om formulat. alluaiioon ion
cublic_spp public.speake wwsspeakkngg iin
persuasives www.witw.iiiii...m.............e
effect vee wee.iiveeesseeeeeeeeee www.uuuttii
group thoppttheor-ss iimplmppp
speech wwwwiiig
social www.a.diaa..mmm...mmm....mm.....
Skills
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sol Polve. www.re.csstaaaann....... sered_optiii imi zat -on-prooooooo lems
apply www.sstcttttttt ic. ot..pptin 1immmmm zat.i on menhoss
www.pprrrgaamm.mm.....mmmmmmmm artm ing e www.iingggthchhec..ee cenchiiiig ques ques
apply lyply.linear.c www..pprorramii... ng twchnitcchceeeec ques
apply apply-inte www.innnneeeggrggeeeeeeee www.eeeeetttttteeeeeee wtt.isssiaaaoooooo ithha
mathemat ical. wwt...caaa........c...c.... owt.ooptimmmmmmmm iatiiatiiin i .on.methosssssooooooooooooooosse
perform. www.mm..aaaggi....m.mm.mm an.rann-ee rel.rele axatis inntion inn
military strategy developne velopment.
won.uundrrpppe prnssurr
Table 192: (Part 49 of 52) 3266 skills extracted from topics Tables 90/92 92 and 94
implement implement.genet- wew.tggeeiigeeeeeeeeeeee aic.alllllee waaaglooittiii hns
apply apply.inter www.iiineirrro. poiittrr..itt...o methods
recognize cultural blases analyze 1 Linguistic patterns
hi historical call battle analysis
analysis
culture
intelligence.d weeeggttheinng
technology. inteenattioon iin
hupply.chain.... www.chhinn..ee www.hhhnnnnnneneeeeccc man.gemennnnnttt....mm.ee.m.
supply www.sstorrrtalyyss......
asymmet www.iiaaaaaaeeeeeemmmmmmm
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analyze mat mww.riataaaaa...mmmamaaa perrrallcuu
evaluate www.ecttttttaaeeeeeeeeeeece
terrain.eva luation
war sarrgaame..aee .. comee.ssmmlles. simulation
operati ietaaiionlll. www.oolllaannnn......
threat assessment
all aance building
mi military www.hhistttrccco
risk manamagenntn
decis sioi..n..... peciisin.mmainne
ha hms
imp ement. noml inear. ooptppiin iimi zat caation.aa on wwt.eio.nlooooo.........oo.o...e ahgorithms
develop mwwti .ob jecti iveeivee.. ve wtt.ppt.iiiiiii.... on models
www.eeaaanaaaaaeeee wellinnttte wei.ingnnnnninnn eechnniii ques
implement branch and bound algor seorithe t.mms
www.prooggamnn..m.......... rgg. .t weteehhiiiee ques
www..ooptiiiiimm imi zat iion..n wwwpppolllemmmmm.po emm
www.coommniaatiiic on
analyze.convex_ oppptttppppppp. imi zitiaiiin. use_gradi entdiiettteee ent. dew.eeeeettt...m. mods
formul ate optimizate seaiiaaiii iaaaiioonn. on www.probleee ems
www.aapptiiii..i...i...ooo.o oot.ppti..... imi zat
apply mnt.hropological theories map cu curtuall networks
force_depl wtt.mmettttiiiiieemeeemmmmmmeeee Laizatiinn ion
psycholog ical warfaze planning
interpret cu wwwturalllaieeeeeec narratives
perform. se sensii it tiviittyyeiii aity-anally lysis
caw.cont exts
stilize_dynanee c-programm www.orrrmmingg..o
apppy--uuu uuralrelatti intaiiissn sm
commun iiaiiiiiiiiiiio i on www.oooooiiio..oo.oo......c..o.t
ident i fy wrp_cuutuui wonltural.teent -trends
geopol it ca 1 assessment.
diplomat www.gooiiaa.....mmmmoom... iitiiiaionn on
examine heiinehiisee st wwtorrial.com
resource_all oocaiion tactical ana lysis
logist iitticsspp p- anning
cont contengy wwweency-plannine
planning
strategic.foreet wtr.::.forcastiiii..
cyber_warfee www.aarfrrrssttttteeeoe
scenari modeling
morale www.maanggeeec.. ement
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solve nww.wrk............
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use qua.uuuaraatt...
utiliiee wawliieesiiie
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www.eeeedeeello.....ee... dew.oeeeeeellommeeeee. opmmet
interpret military communical ceamiicatiio ions analyze
ons
reading wwwpoooeppes.se.oommm on enhancement
re reinforcemen: learning applicatiion
conversatic messatiion.. ion. www.pprcttt. ce engagement
language exchange part participsn pertieeippp patiion
wwt.gaap.naviggtii..
wteeettunddrttttttttoo inerstanddin
Table 194: (Part 50 of 52) 3266 skills extracted from topics Tables 90/92 92 and 94
evaluate strategic decis sions compare military technol Pooooogiie
reconstruct wwtrrutttiistie www.srtrlaaaaat.........
w...epeeeeeeeeeeeeeeeeeeeeeeeese on.mastery
wtt.iinaaiiityyyttce.ee ques
grammar pattern, recogniti rtcognitio iio
www.mmiiitttyylammmmmmmmmm alanneess
sel f assessment conduct ng
identify fiftiiyykee wewttityyyyyyyllle www.ymiiitttylleeeeemll
wwwiisssiinnnrrrecccc ice
mnemoni c. dewievicie sevicee.e utiiiiiie lizitttii
cultural.. wttttaal..ooete....eeeeee inw.iiiliiitelleeeee.ec.oo
sthical.ai.. ai-impmmmpp.mmm Limpll lemplmmentaiin inn ai-project mar.maaaageeent iiperrformancee.t uniig
di.exlaaiaabll www.:e.pttiiiiiiiiom.mm ion ai ha.dwwrarre.co
interpret military _apps compare wwwaareemiiiee wwt.mmmmmmaaaamooeeemmmm
language_ language.learninng wer.-eeaannngaaee wwwrrnnnngappaaanneeee
al glgoria i tmm opt o imi zatiiiiion on
Peerra.networke sooorrddesiin ing
feature www.ineeninnnnnnn rinng
natural. waw.uaaallnggugaaa...ammm waaguageeprrraoooooo
active_listent
idi adiomati wwlommaiieeeett
snalyze_rn mieemmillie miltary www.niinnii...o zat zatinnaltnnnll iional 1. structures
wew.teccniigg iques
metacoonitto tileeieeeeeeee strategyy apppiicatio ion
www.etts.o..a...a............... warfee
wwwnninngntttee me thoss
wwweesspsyccollogeeo ca www.wra.teeeeeeeeeeeemeeeee wetarretecchne
elliattin on
interpret historica cal documents
evaluate www.wns...nooooo..o....o..o..... e effect iveness
lopment
language ge i iomiimneiiinnn immpmpl ement at
translattin ion exercise completion accent- eevent-reduce
lementation
connvvaaiin ivvaidaaiio ion
llngeee
spaced_repee www.reeeetttttee it wwrppetiiinaapppit.... wo..app.iipii...m iilicaiion
www.-reeuuciioo ienuciiotteennii. ques
audio visual ma material ut iecilizati
pata-preprocessseee imp inw.emiggimmeeeeeatttt.t
computer issionn devel develeppeen lomeent
deep learn inninng ing framework usa ge
evaluate aittteeeeeiiiirtteeeeeeeaeeeeeee www.itaatttttiiiiin. www.ttraiinnn...
s--imgg- mp 1 ement at Lon Lon
rovement pronunciatio ioon iiriiipproo
vocabula brrr memori zattiiiionn ion
www.lssettnng... ing. wnw.ttin.g.nn..raccc.......... www.aaactciinnn..
analyze battle Lactics
ai.syster int egrat atgratain ion inn
assess_ wwwploisiilllccaaaaoooooooo
data.viss seriisualizen sataaliaaiiin ion wwwpsearrchmmehhoolo.ooo ai.research
mmalyze.war.ecoromic.
ai.model wew.dpplypp.. oyment
assess www.cuuturrlllccmm
writing wwwiingggrrofff. pwoffrofiiee wwwpfriceeeeee
ai.securis yarrity-iie
language
model_e Leeeellll
machine.
Skills
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aut ontonomous driving system. deveddvellll I lopment vehicle.dure wew.cceeeduaabiieee
noi r se vi ribratii www.bbaannnnn.ooo...a. tiiionni harshnessssss mit.mmiiiaaa mitigation
food safety protocol impl eeeeetettttt at ion
vehicles we ght reduct on strateglee
computer www.erriifrrtrrreeeeeeee ructurre management
vehicle.a ar..aerodie www.leaerodynammccc.o. cs.opt imi zatiinion
ai Lesting and quality assurance ai data col lectioo in and conotaation aut ive ieeooivveeysteeeee wysttmmenggineeeee powertrain ieterrainniie we..innteeeee.. waw.aainnnnntaaaaetteee.... centeggraiion ion
www.deeelllppee lopment
emi ss ions control syyttem desigiiii den ign
vehicle .s immulttttitiiim.mmmm on and www.eoolliie ling agricultinn cuuluure. and food www.oooodooodooooooo product ion
deve 1 lopment
aut i ve www.atriiiaa weweerasssllleeoo ieeeection ion
pest_i i iew.iieeiiiii.. intnniiiiiaat.. ficat ieaciiioo..... ion. and. coccootrroo
agri pgriculturas waste management.
90.92 92 and 94
aquaponi cs system management
virtuali iataaliiiiiioo ooo.iinnnii inm lemeetttiion
li livestock hea Ih monit tooittoiiii ng
seed sreddsseectio iractioon inniionnann and.iiinnnnndrreeeeee and.add breedinn
anaanlyssss
as.miiscaaaaaa ansinss chass is w-dynamiiiccmmmmmmm cs.
thermal man.gement design
cebbilitt 1it sittytttsiiiin
soil llllnurrientt ient www.maaagmmma.... gement
greenhouse www.ggememangggeennmmmmmmm wtw.yyeed.fooeccco
agricultural. waw.daaaaaa.a wwt.aaaaaaaaaaaaaaaaa
post harvest t. t__haandiing lillnnn
server.admini weverr.adminni ieddmiiiiiee wwwiiissiattiii..mmimiiiii.atiii ittiistttiiio on
aut onot: ive. so wwttarree....rrr
Table 196: (Part 51 of 52) 3266 skills extracted from topics Tables
agritourisms wesiisspproooo
wystem.monitoriie
Cactorssintegrart on wwweeeasdeiiggge in
emmetation werbersecurityy hmp 1eppleese
www.contrtttt rol mamagement
wwwuttueeeiiieeeeee Lmo lementetttttt ion
www.ennnnneeeeeeeeeeeeeeeeeeeeee reeering ing siccssiiig cos-imppi. impp Lementation
con.complianee liance
ai documentati and reporting ing ai si.mode
www.inganndtrackin.. www.eersionnng...
electrical.ste peectrica.sssseentt wystem.eco.. opt imi zatiion on
vehi chhicle www.wrk.t....... communi cation tion
autoioiive systemeet ems integratiiiii on
intienatiiiiioo ion
sustai nable fa marming mg pract ces
fuel www.efiiciic. fieecyy aptimiza zat on
wal.cleaacchttettur ure.designnn
argenomics.and..uuee teeetee eettiigngg
harvest :imine mmiing opt imization
aeraniccccrri i.t.ttiiiatttiiiiic.c.ic..iiiiii infiiiiiatti cat www.onnn.cioooooommoooo
particall farming, Lechni gues ques
food processs wng.inggeee..e chchiquues
aut www.aaaanaauuiingg.omm
ww..syytte.ddsee... designg
farm_equi parm.equipment..pper pwwplppmett.pppo copertttppaatii. ion
elimate_adape www.aapptieeefeeeeee.e iiveffarming
food.preservat ion hodds
ai software develo logeenn.
crop.rotation.e inn.ptiionpll... anning
farm financiann allpplpplll all.aaalliinnnnma. pllpninngnngggnn.nnn
farm to table logi logistii iisiiss
network.conf Sinffigun iiguuriiioon iottoo
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aut inttomiivv.se www.aaiiviv.ssateeeeee....e www..s.att..a.e................e
agnostics
aut omotive. wee.vveeeesoone www.sennsrrrii.
precision. iission.aa ann.agggiilttti.
cloud inf wtw.iiinnrruuuuuuuuuuouuu
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weriiaationssy... www.oossssttee.m....mo..oo..o
vehi vehicle.do cericlledd... wedddiiaa
Skills
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change mana waa.maaageeee gement -mmppppp ement at
www.orordnnnn...o. ion
92 and 94
pooeery-laanning
disaster www.erreeooeeyyyee. Lanning
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Table 198: (Part 52 of 52) 3266 skills extracted from topics in Tables 90
sccurity www.pppllppoppeee.peeppooppepppe
software www.ddlllyyy. deployment
complian iance .management
lifecycle management
exper www.eeieeeaalaieeeee.........eee all design
laborat at www.ttttoytttteee www.oortttteiiieeeeeee
prote in purification
statist st isticee ciall wel.mmeeeiiin lingingg
hypothesis formulation
lab safety protocols
capacity selppaannnn
storage.man mannggmenn.
vendor mana gement
incident re resspsseeeeee
sample.prept www.ee.peppraata. in
research. www.rchhhhhicss.....
cable management
Siterature.rent
dna seeuuencing
fl Flow.cytomete try
infrastructure ure scalability pllanninnngg
a.g.eemeenttt.. www.montttoo iitrriiin ig service_ laalleepe eeeeeellleeeeeeee 1
loadbbalaee pancing.conff guratt iin ion
infrastrue resstuuuure documentatiiii ion cost Pett..ptiin ientiiiiza uniiaatio intiiiiiin in aut oaaa ieooomaiion son.sccipping
performance wot.annne.tptiinnneee opp imiiaaiion
hardware t roubleslli www.bbeeeooootineo ing
gene expression lon analysis
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www.collaaboaaiooooo
rrc.ppreeentation
scient coantffionn ieeennifffll.laaorrre.aoo
data vi sual aoualliaa zat zatioon ion
invent ory tttcckiiiinn
scientific.wzii ffi.wwritinngg
patch management
power management
per impliffiinn fication
scientificn wfw.eennffi.ppreeeeeeee
animal hahhhinnnnnn ling
westernbblft blotting
backup and and
aatalanalysis
grant grant-wristt writtwritng
cell culture
bicinformatin
Skills
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Queries asking for expl explanann lanat on of concepts, terns, or distinctions between related things
Queries seeking assistance with speci opecific fic issues or cha thallenges that need resolution
Open- ended queries driven by curiosity without a specific information target
Queri fee request ing the creation ion of new content or ideas
Queries asking to check the accuracy, correctness, or iepropriateness. of something
Queries seeking subjective advice or suggest ions based on preferences or criteria
Queries aimed at acquiring factual Moowledge or anderstanding about a specific topic
Table 200: (Part 2f 13 Query/Task Types extracted from interactions with Claude-3.5 -5-onneet
Description
ierriiiiaiiiiiaaaaaa Validation
Fraiionn/Recomeerdatt ion on
Help/Probler Peroblem-aae solving
Exploratory: aarplorrttry/LLarriii Learninn
Information ion ioo seeking
Creat ve Generat ve
Clarification
Query Type
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Queries seeking structured approaches to achieve specific goals
Querles asking for detailed exami nation or evaluation of content, data, or situations ihations
Queries seeking conversation emot i ional support, or human human-like like interaction
Queries exploring potential outcomes or consequences of theoretical situation ons
Queries seeking evaluation of differences and simi laritiess between opt.ions
Queries seeking step-byt ste-bp-ssep Instructions or methodical guidance
Table 202: (Part 2 of 13 Query/Task Types extracted from interactions with Claude-3 1aude-3.5-a 3.5 -ite--.--Sone-e S-Soneet
Description
Hypotheticas hate/iccalass// c/ce/ceariooc /scenaiio Analysis
Planning rageegy
Social/ Emot ional
Procedural/ SaaeurraaHHow-bee //tw/Howtttt
comparative ive
Query Type
Analysis
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L SKILL EXTRACTION PROMPTS
L.1 PROMPT FOR SKILL EXTRACTION DASTRUCT-SKILLMIXS
requi to solve the question. Basically, you should be able to use the skill as dictionacy key in pytion. The skill name should be lower case letters only, The skill name should be very descriptive and you may multiple words to describe the skills required in the question, If you do use multiple words par question, then join them by an underacore. [text]
Consider the following question.
question
Your answer should be follows: <name of the 12, ressons cresson for the
L.2 PROMPT FOR SEMANTIC CLUSTERING (INSTRUCT-SKILLMIX-D)
Given the 1ist of skills zequiced to solve various questions, your task is categozize these skills into descriptive and specific groups, Each category should only capture the essence of the skills it includea but also reflect clear, distinct areas of expertise terminology
appl ication. accomplish, CaLegories should be narzow enough to provide meaningful insight inte the categezy names in lowercase, joining multiple
precise
specific
akills
apecielizatien they represent. words with underscores.
For each categozy you create, provide a comprehensive rationale explaining:
thase particuler skills grouped together.
Haw tha catagory name specifically reflects the spacializattin and inttinctvvened. of
Skills {okills.jooraddatr)
#+ Examples:
Category name: *web deve lamentt
iimi.cas.design, jevesciipt.interaativvkky wer.perr .opt zetionion Razicnale: This category focuses specifically on the skills required to build and development.
Included
optimize interfaces, distinguishing it
Category name: iacchiee.lamiinngeeeeei.... Includad akills: machine-leare croel..ttt.,,,.. undarstanding gorrithmmic fai machine.learm wtw.gpppplaaiiionnn.....
Rationale: These skills grouped under mechiieeleeriinggeeennuouue because they directly relate to the develogment and application of machine learning models, emphasizing specific use cases ethical ccnaiderations, general programming skills.
distinct from
informative, apect tic, and reflective grouped under them. This will aid in their practical utilily and clarity.
your category
11a
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L.3
PROMPT FOR CREATING SYNTHETIC DATA INSTRUCT-SKILLMIN
Prompt
creating datasat engaging human-AT Interact! that demonat the capabililies and value of AI models acress diverse skills. Please help me generate high-quality examples for dataset.
will first give you a set of (mumskillls) sxills that are valuable to humane.
Next, you would create a query that a human might plausibly sak whose answer invalves using those (num.skilla) skilla.
for this query you will genezate a good length answez using your best helpfulness wisdon, Please to avold generic sound queries and anawers. Feeple often have concrete problems and would give inportant details. the answez should also give some concrete details and exemples,
get verbose.
Before give you the skills.
(aum.sxills) skills, will provide some examples involving 2
sxample
+4 Skills: selfareflectinn.xillll. linguistics
itt instruction: often find myself in conflicts at work due to miscommuiccation and differing perspestives.
improva my conflict resolution skills by incorporating self-retlectiion and effective communication tecaniques into my daily interactions with colleagues?
#+t Response: To enhance your conflict resolution skills in the werkplace, inzegraze these self-raflection
linguistic strategics into your daily routine: Self-3eflection Techniques:
and-of-Lay Review: Spend 10 minutes reflecting on interactions, neting successes and areas for improvament. For exaтple, auccessfal de-escalated tense altuation with a client by actively Listening to their concerna and rinding mutually beneficial solution."
Conflict Log: secord details of conflicts, your respanses, and outzomes in a dedicated journal. Analyza patterns monthly to identify growth opportunities. noticed tend become defensive when criticized. will practice pause and respond techniques to improve my reactions." Linguistic Techniques:
instance,
Empatay Statements: Use phresess that demonstrate understanding and validate ochers' perspestives, such I can see why you that wayör That must challenging for you.In In a recent mooting, saying I understand your frustration with the project tfmel ine coworker feel heard Reflective Listening: Parophrase the other person's enaure clarity and show you' re Istening. For example, šo, what T'm hearing ia that you're concerced about the budget allocation. Let's discuse sone eptions to address that.""
open to finding ng solut together,
points before responding
integration into Soutine:
Keekly Practice Sessions: Redicate an hour each wenk to role-play conflict acanarias with a truated colleague, focusing on applying relf-refleccton insights and iinguistic techniques. Frovide each other with constructive feedback for continuous improvement. Real-Time Application: When conflict arises, take a moment to mentally review your phrase and select an appropriate response.
the interaction, briefly reflect on what worked well and what could be improved aext time.
sy consistently practicing these strazegies, you'll 11 build stronger habits enhance your overall conflict resolute unioo skills in the workplace.
Example 2:
#4 Skills: psychological understanding, data handling and anslysis
#44 Instruction: I recently started working as a data ana for a meal kit delivecy service.
neticed significant increase (208) customer chura evez pas: quarter. sвй can 1 leverage data analysis and psycholegical principles to identify the rost couses churn and davelop targeted strategies to improve customer retension? #4 Response: To addresa the 204 increase in customer charn for your meal kit service, employ a combination of data analysis and psychological insights: Deta Analysis:
Customer begmentatioa: Analyze churn rates by customer segments such as age, location, dietary preferences. For example, if you
that vegeterian custcmers have higher churn rata, can focus improving vegetarian meal options.
Churn Timing Analysis: Tdantify ty common timetrames when churn sccurs, such as after the firet monta after specific prometions end. II churn apikea after the first month, consider implementing a more robust onbearding process or extending promotionsl offers. #sycholegical insights:
Exit Surveys: Conduct exit surveys to gather direct feedback from churaing customers. include open-ended questions to uncover deeper insights. vor instance, Whhtt was primary reason for canceling your subsoription?dnd What could wa have done betzer to keep you as a customer?"
Customer Journey Mapping: Mар the customer journey to identify pain points and opportunities for improvement. Apply psychological principles like the Peak End Rule prioritize the impactful moments. Feeus creating positive onboarding experience and ensuring satisfying last interaction before cancellaticn.
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Actionable Strazegies:
sersonalized Retention compaigns: Develop targeted email campaigns at-risk segments, addressing their spect fic concerns. For example, send vegatarian oners an email highlighting new plant-based recipes and offering a discount on their next hex.
Flexible Sibscription Options: Introduce more flexible subseripticn plans, such ability to skip weeks or customize delivery frequency. This addresses the paychological need for autoromy and control, reducing the like shood churn. By leveraging data delven Insights and psychelogical principles, you can create sergeted approach to reduce churn and improve customer satisfaction in your meal ki: service.
example 3:
rtt skills; Good.eeleee.knoolldd.........
internes.technologies
#+ Instruction: I'm a food truck owner looking to expand online presence and astract more cuatomers. want to create webaite that allows customers to customize their meals, track truck's real-tine location, and place erders for pickup, What web technolegies should consider to build aser-friendly and feature-rich websize for my food truck business? #4 Response: To create a robust and Interactive food truck website with meal customization and real-time lecation tracking, consider the following web technologies: Frent-FRd:
React: Use React, a popular JavaScript library, for building a dynanic and reeponsive interface. React's cmmpenent-baset archisecture allows for easy management of real-time updates, such as displaying the current losation of the food truck. 9TMTS Geolocation
Levarage the RTM15 coclocation twith their peraission) and display aearby food truck locations on an inceractiv ive map. Leaflet or Google Mapa: Integrate leaflet, open-saurce mopping library, or Google Maps to diaplay Food truck locations and enable users to get directions. Customize the map markers to shew additionel infermation like zenu items or special offers. Jack-snd:
capture the userfa leeat
No and Exprass: Use Node. a server-side JavaScript runt runtime, and the Express application framework handle server-side logle and APT routing, Node. aon blocking I/O model is well suited fez real time applications like lccation Lracking. KebSocket: Tmplement KebSocket communicat to enable real-time updates betweer the server and the client. When Food truck's location changes, the server
push the
updated coordinates to connected clients, ensuring users elweys see the most cusrent location.
Firehase Realtime Databsse: Stare and ayne food truck location dsta in real-time using Tirebase Realtime Database.
NoS0L cloud retrieval of location data, ensbling seamless real saal-tine updates across muitiple devices. User Experience:
allows efficient storage
PNA to provide native app like experience. FMAS offer features like offline accessibility, push astificatiens, and home screen inatallation, anhancing Reaponsive Design: Enaure the webaite is "ully responaive and optimized for varioua acreea sizes, particularly mobile devices. Use create a seamless experience acress desktops, tablets, and smartphcnes.
Progressive Keb App (PRA): Develop the website
engagement and conveniance.
media queries and flexible layouts
Accesaibility: Follew web accessibility guidelines (WEAS) to enaure the website usable people with dissbilities. Implement propes headings, alt text for images, and keyboard naviçation to create an inclusive user experience.
By combining these web technologies, you truck website that offers meal customization and real-time location tracking, providing a seamless experience for your custoners. sxample d:
feature-rich and user-friendly food
ftt skills: ftt Lastruction: As software engineer, I've developed sophisticated recemmendasion engine that believe could greatly benefit c-commercn my work through technical blog post and 1ive demo businessas. I want to showcase patential elients and demonstrate the value of my soluticn. flow can I structure my blog post and demo to business benefite my zecommendation
digitsllparketing
effectively highlight engine?
technicel aspects
#tt Response: To effactivaly showcase your recormendation engine and sttract potenstal clients, structure your technical blog post and live demo follews: slog Past Strecture:
corpelling hook that highlights value of perscnalized recomendattons in e commerce, Use statisties or real world examples to demonstrate the impact on sales and customer satisfaction.
intention-Grabbinn Introduction: Start
Preblem statement: Clearly define the challenges s-commerce businessen face in providing relevant product recommendations at scale. Discuss common pain points like data sparaity, cold-start problams, and real-time updates.
Technics 1 neep nive: Expla'r the core components of your recommerdation engina, auch as collaborative filtering, content based fillering, hybrid approsches. Use diagrams and code snippets to illustrate your architecture and key algorithms. highlight any innovative techniques you've employed, such deep learning or reinforcement learning. Performance Metricss Presant quant resulta that showoase the infees of your recommendation. engine. Include metrics like precision, recall, Fl score, and mean average preaision. Compara your resulta industry benchnarks popular open-souree recommendation libraries to demonstrate your engine's s auperiority, Scalebility and Efficiency: Discuss how your recemmendation engine handles large scale
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data and real-tine updates. Explein your stracegies for efficient data processing, such as parallel computing incremental updases. Provide performance benchmarks so highl ight the speed and scalabil ity of your solition.
Live Demo: k-commerce Store integration: Create a mock e-conmerce store that seanlessly integratea your recommendation angine. Showcase personalized produst recomendattooss based on user interactions, such as viewed Items, purchases, or ratings.
Real-Time Recommendat onna: Demonstrate how your engine adapta in real-time as usera navigale the store, Por example, show how the recommendat_one updale dynamically based on the user's browsing histery or cart contents.
Explonstions and Transperency: previde clear explenations for each recemeenaazion, such as Customera who bought thi itam also bought...hi Recommenced based on your recent searches, This transparency builds trust and helps users understand the relevance the recommendations.
A/B Testing: Inplement an A/B testing feature that allowa potential clients to compare the performance of your recommendation engine against default candem recommeddation system. Visuelize the results through metrics like click-through rates or cenversion to highlight the tangible benefita your solution.
Premotion and Engagement:
Targeted Social Media Ads: Create compelling secial media that highlight the kay features and benefits of your recormendation engine. ne. Target decision-makers in the commerce industry, such aa marketerá, product nanagers, and cTOs. Use eye catching visusls and cleas cslls-to-action to drive traffic to your blog post and live demo. Linkedin Thought Leadarship: Share your blog post on Linkedin and engage in discussions re ated to e-commerce personalizatio establish yourself as a Lhought leader in the field. offer valuable inslghts and answer questions build trust and cradibility with potential clients.
Participate
relavant groups and foruma
Webinars and Korkshops: Roat webinara or workshops that provide a aands-on experience with your reccamencation engine. Walk parsicipants through the setup process and demonstrate how integrate your solution into their existing e-cemmerce platforms. offer Q4A sassions to addrass specific concerns and showease your expertise. By structuring your technical blog post shewcase the capsbilities and benefita of your zecommendation engine. Combine this with targated digital marketing techniquea to resch and engage potent 8-commerce ienta, ultimately driving interest and adeption of your solution. Example 5; +4t Skills:
live
in manner, you effectively
snslysicalthhining
ftt instruction: I'm history enthusiast planning two-week trip to Eastern surope with my family. re're part cularly intereated in explaring mediaval tecture and learning about the region's a experiences during itineraty that combines visits to historicel sites, muzeums, and cultural experiences to gain a deeper understanding of Eastern Europe's a past and present?
Cold era. How
create
+4 Response: llere's curated itinerary for historically rich two week trip through gestern Europe, fecusing on medieval and cold Mar sites: Week Foland and Czech Republic
Krakow (3 days):
Havel Castle: Explore
ceatuzy castle complex, which served
zesidence
2olish kings. Marvel at the Gothic and Rensissance architecture, and visit the Dragon's Den, a legendary cave beneath the castle.
Oskar Schiadler's Factory Museum: Learn about the Bolecaust and Oskar Schindles's efforts to save Jewish workers during wcrld kar 11. The maseum offers a immersive experience, recreating the wartime atmosphere of Krakow.
Nowa Nuta: Take a quided tour of this planned socialiat city, built during the Cold Nar eza. Visit the iconic Lord's Church, which became symbol resistance against the communist regine.
Karsaw days):
Old Town: Stroll through the meticulously recoastructed Old Town, which destroyed during World Mar 1I, visit the Market Square, the Warsaw Barbicon, and St. John's Cathadral admí the Gothie
Renaissance architecture.
Kazsaw Uprising Museum: Discover the story of the 1944 Mazsaw Uprising, a major resistance operation against
eccupation. The museum features interactive exhibits, original artifacts, and a 30 novie that brings history to life.
Palane of Cultura and Science: explore this imposing stal'nist-era skyscraper, which remains the tallest building in soland. Take elevator the observation deck for panoramia views of Karssw. Praque days):
Prague Castle: Visit the world's largest ancieat castle complex, dating back to the 9th century. explore the Gothic st. vieus cathedral, Romanesque st. George's gasilics, and the Golden Lone, picturesque row of calorful houses.
Charles Bridge: Kalk acrosa this icontc 14th-cent stone bridge, lined with baroque statues. anjoy
musicions and artists, and take in the stunning views of the Vitava River and the d'd Towr.
chilling reality of the Cold War visiting former Soviet nuclear buaker. lesrn about the strategies and technology employed duzing this tense period history.
Nuclea: Buaker Tour: Experience
Weak 2: Hungary and Romania
Budapest (3 days):
Suda costle: Discover this 13th-oontury eastle, which has been rebui and expanded the centuries, Visit the Hungarian Natilnal Gallery, the Budapest History Museum, and Metthies Church,
colorful tiled roof.
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House of Terror Kuseum: Explore this powerful museum dedicated to the victims of fascist and commanist regimas in sungary.
exhibits are headquarters of the Arrow Croas Party and the communist secret police. Nemento Fark: Visit this open-air museum shewcasing nonumental statues from the Soviet Learn about the propoganda
ideology behind these impoaing sculptures.
Buchareat (2 days]:
2alace
largest civilian building, constructed during communist era under the rule of bicolas Ceaugescu.
ogulent
coneroversial history of this massive
structure.
rown: Explore charming streets Bucharest's Town, lined historicsl buildings, cherches, and cafes. Visit the ruins of the old Princely Court, which served as the residence of Wallachian princes. Revolation Square:
tribute the heroes of the 1989 Remanian Revolution this significant square. See the Kenorial of Rebirtå, which honors those who lost their lives fighting against travel rips:
regime.
knowledgeahle local guides gain deeper insights historical context and personal stories benind each site. Stay in ceatrally located accomoddttion to minimize travel time and maximize your exploration of each city.
guided tours
Use public transportation or ride-sharing services to navigate within cities, as parking challenging By follcwing this itinerary, you' 11 embark on fascinating journey through eastern Europe's a medieval and Cold Mar past, gaining a profound apprect atn for the region's rich alstory and resllience.
expensive historical areas.
Given the guidelines and exemples please that a human might plausibly ask whose answer involves using all skills below: skills: (skille.str)
your
query
Prompt
Thanks! It looks like the response may have been out eff. Please zewrite the entire respoase, making fits within the length constraint. need include verbose or extraneous information. Thanks!
Prompt
Pretead you are strengths of the respanse. Currently the response loaks bit generic. Perhaps it could henefit from sone conerete exampl dstaf
asking question, make
Prompt 4
Now, please refine your instruction and zesponse (keep the strong parts and work on the week
5
It looks like the improved response may have been off. Please rewrite the entire improved response, making aure it fits within the length constroint. No need to includa verbose rxteoua informationn Thanks again!
L.4 PROMPT FOR EXTRAPOLATING SKILLS (NSTRUCT-SKIILMIX)
First, consider the tollowing dećinitions:
information, facts, descriptions, theories acquired through educaticn oz experience.
Xnowledge:
Topic: À subset of Knowledge that rocuses on particular area sub Ject.
Skill: function that transforms Knowledge into actions performances that schieve specific outcomes.
Nos, for the topie of topie, please provide me with multigie skills (in snake case) that could relevant the topic.
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L.5
PROMPT FOR CREATING SYNTHETIC DATA LNSTRUCT-SKILLNNE
Prompt
creating datasat engaging human-AT Interact! that demonat the capabililies and value of AI models acress diverse skills. Please help me generate high-quality examples
dataset.
will first give you a set of (mumskkllls) sxills that are valuable to humane.
Next, you would create a query that a human might plausibly sak whose answer invalves using those (num.skilla) skilla.
this query you will genezate a good leagth answez using your helpfulness wisdon, Please to avold generic sound queries and anawers. The answer should alse give some concrete details and exemples, but not get verbose.
Refore 7 give you the set of (num_skt)la) akilla, will provide de some examplas involving skills.
Example 1:
Type: information-Seeking
#44 Skills: paycholegicealusdestt.e
ftt instruction: what are the primary psychelogicel theories that explain the irpact of sacial madia adolescant salf-esteem, and what evidence aupporta these theorias? Additionallly what statistical techniques have been used in the literature to dissect these zelatienships,
techniques help understanding
psycholegical data?
adolescent self-esteem topic within paychology. Various theories and statistical techniques understand and measure these effects. Below, 1'll outline some the primary theories, supporting evidence,
Responsa: The impact
widely studied employed
the statistioal technique quas used this research. #44 Response: The impact of social media on adolescent seif-esteem is a widely studied employed
topic within paychology. Various theories and statistical techniques understand and measure these effects. Below, 1'll outline some of the primary theories, the supporting evidence,
tha statistical techniques usad this research.
*ePychhlogicaal Theories and Evidensa**
Social Comparison Theury: Adolescenta eften assess their worth by comparing thenselves with peers on social media. study by Vogel st was associated with negative self self-percoptiens when users compared themselves to othars they percsived as better off, This suggeats d'rtet correlstion between the frequency and increased feelings of inferiority.
Facebook usage
of social madia
Pionettttttt Theory: Adolescenta idealized way on platforms like Instagram and Saapchat. Research by Cheu and Edge (2012) highlights that need positive to eycles of anxiety and lawered self-estaem when these expectations are not met.
pressure present themselves
and fear negative evaluatiens can lead
Media Richness Theory: Different types of secial media platforms impact self esteem differently. A study by Sherman et al. (2016) compared text-oased veraua image-baaed platforme, rinding that image
platforms (like Instagram) can concerns and anxiety zore then text-based platforms due to the intense focus on physical appearance.
body image
Statistical Techniques and Applications
Regression Analysis: Used extenaively to quantify the relationship between social media usage and self esteen. instance, studles might control age, gender, and sociseronamic status to isolate the effect of social media usage.
Structural Equation Modeling (SRM): SEM enables researchers to construct comprehensive model of how different factors like peer influeace, media consunption, and personal traits interact affect self-esteen. An example is Kross al.'s (2013) study, which modeled the pathways through which social media affecta mood and self esteem over Lime.
Longitudinal Studies and Growth Curve Medeling: These techniques provide insights social medía use. For instance,
sel f-esteem trajectories change over longitudinal research might track sell-esteem changes from early to revesling critical periods where impacts are most pronounced.
late adoleacence,
Mediaticn and Moderation Analyses: These are crucial for exploring underlying mechaniems boundary conditions. For example, studies might explore how negative impacs of social media self-esteen can be madiated by sacial support or moderated by individual reailience.
Conssadictory Evidencer.
While many studies sugges: negative impact of social media on self-estemm, some
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research offers a different perspective. for iastence, Valkenburg et al. (2006) found online intersctions can enhance well-being by providing social support platform for sel texprexpressoon. These rindinga suggest that the impact of sociall medta on self-esteem is net universally negative and can vary based on usage patterns and individual differerces.
*APractical Implications'
develeping targeted interventions. For instance, educatioral programa that teach eritical media l'teracy can help adolescents novigate social media healthily. Korcover, knowing which aspects of social madia use trigger regative sell-cooparlson positive lateraction and reduce the emphasis on appeazance.
understanding thase dynemics
guide the development of restures promote
example 2:
#+ Task Type: Help-Seeking
#4 skills: belt.reClectionsskill linquistics
+4t Iastruction: often find ayself in conflicts at work due to miscommuncct:ion and differing perspectives.
can improve my conflict resolution skills incorporating self reflection and effective communication tecaniques into my daily interactions with colleagues?
conflict resolution skilla in the workplace, inzegrase lingulatic strategies into your daily routine: Self Reflection Techniques:
#++ Response: To enhance
these self-reflection
and-of-Day Review: Spend 10 minutes reflecting on interactions, noting successes and areas for improvement. For example, successtally escalsted with a client by actively listening to their concerns and finding mutuelly beneficial solution."
Conflict Tog: Record detal is of conflicts, your respanses, and outcomes in a dedicated cated journal, Analyze patterns monthly identity growth opportunities, For instance, f asticed tend to become defensive when criticized. I will practice techniques to improve my reactions."
and respond
Linguistic Techniques:
ampatay Statements: Use phreses that demonstrate understanding and validate others' porspectives, such i can see why you for fo a recent meeting, saying f understand
that wayår fhat must challenging trustration with the project Limelinehelped coworker feel leard and open to finding solution Logetheer Reflective Listening: Paraphrase the othes person's ensure clarity and show you're listening. For exampla, šo, what I'm hearing is thaz you're concerned about budget allocation. Let's discuss some eptions address that."
before responding
Integration into Routine:
Weekly Practice Sessions: Dedicate hour each to role-play conflict scenarioa with a trusted colleegue, focusing on applying self reflection insights and linguistic zechniques. Frovide each other with constructive feedback Real-Time Application: Whan conflict arises, take a moment to mentally revies your phrase and select an appropriate response. on what worked wall and what could be improved next time. By consistently practicing these strategies, you'll your overall conflict resolution skills in the workplace.
continuous improvement.
interaction, briefly reflect
stngge habits and enhance
Example
#tt Task Type: Informatiio-SSekking
gessrapliciLanddiisttrricca
anslyLical.thnningg
## Instruction: What were the major geopolitionn changes in Europe following World Nar
II, and how have these changes influenced current Buropean political boundariea?
+4 Response:
A-Major Geopeliticsl Changes Durope After Wozld Kar IIx>
1. Division Germany: The divisian of Cermany inso and Cermany in 1949
was a critical aymbol of the Cold s onaet. This diviaton, marked physically by 1361, lested until the reuaification influenced European political dynamics.
significently
2. Creation of the Iren Curtain: Winston Churchill's tem,, the Iron Curtein, described the ideclogical and physical boundary separsting the Soviet-controlled Eastern sloc and the Western democractes. This diviaton dictated political all aligeeents and conf icts during the Cold Nar, influencing policies and internationa. relations.
3. Detoionization: Following the European powers like Britain and France began significant process of decolonization. This period zaи the independence nations such India in 1947 and the rapid decclonizasion of Africa the 1960s, marking major shift in global dynamics and Europsan foreign policiss.
4. Fozmation of the Europear: Union (BU): The BU's origina lie in the European Coal and steel Community 1951, evolving
European Erenomic Community by 1957. alliances, expanding to include more countries cocperation and prevent further wers in Europe, influencing both economic and political policies within the continent.
the decades, aimed to Toster economic
establishment
by countries
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was strazegic move for collective securicy against the Bovies threst. The Soviet response, the Narsaw Fact 1955, defined the milicary alliences zurope, solidifying the Rast-Rest divide.
**Inlluence Current European Folitioal Soundariea**
and subsequent reunification of East and dest Bernany in 1990 reshaped Germany's role in surope, altering
Reunificatien:
Berlin
internal external political. boundaries.
2. EU Expansion: The EU's a expansion has included many former Eastern Blac countries, fundamental changing the political landacape Rurope. Schengen Agreement, implementes in 1935, zinimized the importance of national boundaries within the BU, premoting
integration.
3. Rreakup Yugoslavia and the soviet Uhtons aeven successor states throughout the 1990s and the Soviet Union into 15 independen: countries in 1991 dramatically redrew pelitical boundaries. These events, rcsced in stability
disintegrat fon of Yogoalavia into
political upheavals,
aliguments.
Case Studies: Key Treaties and Shifts in Alliancasee
Tresty of Parle (1951): This Lreaty eatablished the Buropean Coal and Steel Community, foundationel step towards European integration, It set precedents for future
policies cooperative frameworks within Europe, promoting peace ecczomic stability acress former wartime adverseries.
2. Tmpact of NATO and the Karsaw Pact Poland: Poland's transition from Karsaw Pact member a NATO member 1999 exemplities the dramatic shift in military and political allisnces. This change significantly influenced Poland's foreign and security policies, reflecting broader geopolitical shifts in zestern kurope post-Cold War.
Now, it ls your turn! Given the guidelines and examples above, please create query that aligns with the following Lask Lype: (query type]
snippets they essential analyze and validate your quezy effectively. Make sure that the query is one that human might plausibly ask whose answer involves using #4 Task Type: (query.str)
Include specific
below:
#4t Skills: (sxills.str}
Prompt
Tharks! It looks like the response may have been cut off. Please zewrite the entire reaponse, making sere within length constraint. or extraneous information. Thanks!
Prompt
Bretend you are the humen asking the question, and make a list of weaknesses and strengths
Currently the reaponse looks bit generic. Perhaps could beneCit from sone concrete examples and details.
Prompt
Now, please refine your instruction and response (keep the strong parts and work on the weak parts).
Prompt 5
looks improved respoase may improved response, making sure it rita witain the length constraint. No need incluce verbose or extraneous information. Thanks again!
zewrite
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M COMPARISON OF RESPONSES
In this section, we show side-by-side comparisons of responses from various models to the following instruction from AlpacaEval.
I'm. trying to teact: myaelf to have nicer handwritiing. Can you help?
Observe that responses produced by models finetuned INSTRUCT-SKILLMIX tend include concrete examples (whereas responses produced by the other models do not always).
Response by LLaMA-3-8B-Base SFT on INSRRUCT-KILLLMIXX422444
absolutely! impreving your handeriting involves both technique and practice. Here's s concise guide to help you achieve nicer handwriting:
++.. Posture and Grip:++
A*BosturesAv sit uprigat with your paper at a 45 degree angle to reduce arm and hand movement, which helps prevent saudging.
**Gripre Use a relaxed, comfortable grip with your pen er pencil resting lightly in and reduces presaure the paper.
your fingers. This promotas amoother
*2, Pen Lifta:*r AsKhen LiCE:Ay cursive.
sentences
breaks, especially
help maintoin integrity letter, preventing blending together and enhancing seadabiiity:
AvPerpase:Av
*x3, Slant and Direction:
->Batural Slant:ee Krite with naturel slant that's comfortable for hand, typically a slight upward slope helps legibility.
reei Write continsously without lifting your pen much, mointaining fluid motion can enhance the flow and elegance of your handwricing.
**f. Presaure Speed:*.
.>Bressuze: Apply coasistent pressure, avoiding excessive ferce which can cause fatigue and measy handwriting.
AvSpeed: drite at comfortable that allows your handwriting to remain clear and legible.
+.5. Reguler Practica:**
=+<outtnn:> Set aside time dally for handwriting practice, such as 19 minutes a day paragsaphs.
writing
**Variezy: Mix practice sessions with both structured exercises focusing specific writing maintein fluidity.
letters
++6. Poper and Tools:**
**Paper Choloe:** Use amoota, medlum-weight paper that allows for control without too much slip.
*+lool Veriety:*. Experiment with different writing tools penss, pencils, markers to find which provides the best feel. and
for your handwriring.
*x7, Rval ivaluation and Adjustment::r
ingrovementa and **Feedback Ask for Zeedback from others or enroll in a calligraphy class to gain insights from experienced instructers.
*+Self .e Reqularly areas needing more work.
your handwriting samples
By focusing on these specific elements and cemmitting to regular practice, your handwriting will improve. Remember, small, consistent efforts lead significant improvements over time.
response concise informative, providing apecific, ionable advice for each step of the handwriting improvement process. It also includes practical zips and suggestions maintaining motivation evaluating progress, meking comprehensive guide for anyone looking to refine their handwriting.
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counta; repeat until caln, This Published as a conference paper at ICLR 2025
Response by Mistral-7B-BBsse-00.2 SFT on INTRRCCT-KILLLLIXX-2244
Improving your handwriting rewarding skill to develop and can be beth therapeu:ic and practical. Here's structured guide with specific techniques and exercises help achieve elegant, Legible handwricing:
**Step Posture and Setup**
*.Postuzeve: Sit with straight spine and relax your shoulders to prevent Leasion. **Tiight*:: Use a desk lmm for adequate lighting.
AxPaper and PenAw; Use smooth, lined papez and as 2ilos 92 or Pentel EnerGel.
that Ceela confortable, such
**Step 2: warm-up and Mindfulness**
**Krrm-ppee: krite the alphabet three timea to warn up muscles. s*Brathhiggs:
for tour counts, helps reduce writing tension.
**Step Fractice basic strokes: the upward stroke (v), downward stroke (N), and circular stroke (6).
*tStep 4: Letter Formationes
<*Uppercase LettersKA: Form each letter slowly, tracing then with a stencil for accuracy.
AvLowerezae lettarsv:: Practice lcoping "1' and cresaing 't'.
5: Word Practice*
Write simple sentences Like \The quick brown Tex junge over the 1azy dog." Focus on each word.
**St 6: Cursive Writing..
start
uppercase letters, move cursive loxercase.
Try onl ine tutoriala for lodeled curaive formstion.
#tSep 7:
Presaure and Speedrx
Practice
pressure applied; ain for consisten: pressure. slow downs writing fast increases errors.
tablet
8:
style Inspirations*
Explore cal call igraphy styles online, initating specific *onta like Script or Gothle,
*tBeep Reqular Practicers
Write journel eatry daily, focusing quality, net quantity.
Sttpp Challenges Varietyes
Waekly challenges like writing with your non-deminant hand or different tools. Expertment with bullet journaling for creativity.
Stepp 11: Track Frogressvr
Monthly before and after writing samplez.
Ask for feedbeck frem calligraphy expert
Stap 12: Enjoy the Procass**
calming
aud! cbook andoy time writing.
*Addittoonl Tips:** Trainingav: hand exercise
with
geip
strength.
*#Mindfulresa==:
writing enhance facus.
3. .ARewardaa*: Treat yourself after achieving goals, like new for milestones.
This quide not only sharpens your handwriting but also promotes mindfulnese and creativity, effering both functional and mental benefits. Enjoy the journey to betser handwriting!
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Response by the Reference Model (GPT-4-Turbo 2023-11-06)
technique. impzoving your hendwriting involves patience, prectice, and attention to to help you develop nicer handwriting:
Certainly!
1. s>Understand Your Current Stylexx: Before you can improve your handwriting, you understand
you' starting from. write paragraph and anslyze where you could make improvements. Gook for consistent slant, letter size, spacing, and legibility.
see
pencil that feels comfertable in your hand. the right :ool can make a significent difference in your writing, Some people find that using fountain pen improves their writing because it encouragas has a smoother ink flax.
2. vaChoose the Right Toolsvs: Use
relaxed grip and
focusing letters, practice basic strokes. This includes straight Hiea, curves, and circles. These are the buf lding blocks of letters, and getting comforiable with these will make youz letters more consis.ent.
3. *sPractice
Strokas**:
slowing down, you focus the shape of each letter and ensure that you're re writing them correctly.
4. x*Slew DowneA: Speed lead sleppy writing,
hold your pen pencil with a relaxed grlp. Don't hold it too tightly, as this can make your handwriting appear more jagged and can also cause your
5. vaMaintain Proper Fosture and Eripus: sit straight
tire out more quickly.
6. v*Porm Setters Correctlyas; Start and end each letter in the right spot to ensure consistency and flow. Practice writing each letter of the alphabet, Socusing on the ones trouble.
that give you the
7. vxPractics consistent Letter size and Spacingva: Consintency is key to neat keeping
your letters the aize your apaced. You could use Lined pape: as guide.
handwriting.
evenly
8. **lle Handurt ting Korkshests**:
can find handwrit' ng pract worksheets guide you through the proper
for both print and curaive writing. These worksheets strokes and help you prectice consistency.
9. +aCopy Nest writingsr: Find sample of handwriting you adnire and try copy it.
This can help you dave op a sense of the style you're re aiming for.
any reasons to write by hand every day, whether it's journaling, writing letters, or making to-do lists.
vANrice
handwriting Improves
reqular practice. Find
11. vAAnalyse and Adjustas; Regularly look back your handwriting
where you' discouragad progresa is slow.
improved and what still needs work. Make adjustments as needed.
12. +*Be Patient and Persiatent=:: Don't
Handwriting improvement takes time and dedication. Rememoar, the goal of improving ng your legible
handwriting
seschetically plessing, it should confortable for you to write. There's 2 no one "correct" style of handeriting, so focus developing style that works for you."
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Response by UltraLM 13B
Sure, I'd be happy to help! liere are some tips to improve your hendwriting:
1. Practice regularly: The more you pracsice, the better your handwriting will become. aside some
every day practice writing letters, words, and sentences.
2. Start with the basics: Regin by learning the basic strokes of each letter, such as circies, lines, and curves. Then practice writing each letter in isolation before moving to words and sentences.
3. Use the right tecis: Make sure you have the right supplies, such as a geod-quality pencil, lined paper, and difference in the quality of your handwriting:
Using the right tools make big
Notch your posture: Good posture assential
good handwriting.
up straight
with your feet flat on the floor and your arms and wrists "elaxed.
5, Relax your grip: Hold your pea or pencil lightly and avoid gripping too Lightly. smoothly and legibly.
This will help
write
6. take breaks: it's important to take breaks while practicing to avoid fatigue and break every 20-30 minutes
strain.
stretch your handa and wrists.
Remember, improving your handwriting takes
practice, sa patient with yourself, Keep practicing and you' 11 have beaut itul handwrlting:
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Response by Mistrral-BB-Base--0222 SFT on Alpaca-52K
Sure! Here are seme tips that can help you improve your handwriting:
practice, the better your handwriting will become. Set aside same time each day to writa out letters, words, and sentences to improve your handwriting.
1. Practice regularly: The
2. Use the right Lools: Make suze you have good pen oz peacll that flows smoothly and comferteble
with. good quality pen pencil can the quality of your writing.
big difference
3. keep your posture minds sit up straight with your arm and wrist relaxed. This will help you write more amoothly and avold cramping.
your pan penail correctly,
4, Take your time: Don't rush writing. Take time form each letter and make sure it's lagible.
5. proper grip: Hold your pen pancil correctly, with your fingera and thumb aupporting it, and your index finger and midale ringer guiding it.
your letters same slant. This will make your writing look more uniform.
Keep your
consistent:
formation: Make sure your letters formed correctly, the right strokes and curvas. You can find examples of proper letter formation online in handwriting quidebook.
Practice proper
3, Keep your handwriting neat: Keep writing neat and Lidy, with spacing between words and lines.
9. Practice cursive writing: Cursive writing help improve your handwriting making your lettars more connected and floxing.
differen: styles handwriting find one thst sults you. There are many styles to caoose Irom, auch as print, cursive, and italic.
10. Experiment with different styles:
11. Use guides: You can use a ruler or grid to help you keep your writing straight oven.
12. Set feedback: Ask friead or teacher to give you feedback on your writing and see where you can improve.
13. Keep it simple: Avoid overly dacorative complicated writing, it can be hard
to read.
14. Keep legible: Make sure writing ls to read, if it's not perfect.
15. Be pssient: Improving your hendwriting takes time and practice, so patient with yourself and keep practicing.
Remembar, everyone's a handwriting is unique, so don't too hard on yourself if it doesn't look exactly y like elae's. keep practiol and you' 11 improvement over Line.
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Response by Mistral-7B--Base-x0.22 SFT on Alpaca-1K Longest
Sure! Here are seme tips that may help you improve your handweiting:
1. Practice regularly: Like any skill, impreving your handwriting takes practice. aside some time each day to write out words, sentences or aven whole pessages to davelop fluency consistency your
2. Use Lhe correct Lools: Make suce you have a pen or pencil that feels comfortable in your hand that you sharpen regularly ensure fine point. Also, use paper that is not too smooth or too rough, as this can affect the quality of your strokes.
3. Posture:
keep your writing surface at comfortable height avoid straining your hands, wrists, arms. Your writing inatrument should be held in relaxed grip, not too tightly.
up atraight
you form each letter correctly from the beginning strokes. This means sterting with the upward and downwerd strokes of the lettar the right angle, and sizing then appropriately.
4, Stert with proper fermation: Make
5. Don't ruah: Take your time and write slowly and del dolireeat,,y, focuaing on the movement your
pressure you apply the paper.
6. Keep it simple: Avoid overly cenate or fancy writing, as this can be hard to read and may you down. second nature.
basic, uniferm strokes practice until they
7. Don't lift your pen: bifting your pen off the paper to change recttoo can cause inconsistencies in your welling. Practice making smooth, fluid transitions between strokes without lifting your pen.
3. Practice Cursive: Cursive writing help improve the flow and connectedness of your letters, making your handwriting look neater. Practice connecting the lasters in worde
9. Pay attention to specing: Make sure there is enough space between words and letters legibility.
10. Keep it clean: neat, uncluttered workspace can help you focus your writing. Keep your daak
of clutter sharpen your percils regularly y to avoid smodges.
11. Experiment: Try different stylea and techniques until you rind ore that feels natural and looks good to you. Doa't be atraid to mix and match until you Find what works best for you.
12. Don't give up: improving your handwriting takes time and patience, sa don't discourdged if you don't resulta immediately, Keep practicing and you will eventually see an inprovement.
Remember, handwriting
personal expression, se balance between what looks aice and what comes naturally to you. with practice, you can develop a neat and legible handwriting reflacts your personality.
142


Reviews:
[{"review_id": "CB9vzA7NEe", "reviewer": "Reviewer_MpdQ", "summary": "The paper introduces INSTRUCT-SKILLMIX, a pipeline for creating instruction-tuning datasets using large language models. The method involves two stages: (1) extracting instruction-following skills using an LLM's metacognitive abilities, and (2) generating synthetic (instruction, response) pairs using random combinations of these skills. Using just 4K examples, the authors demonstrate that base models fine-tuned on this data achieve competitive performance on instruction-following benchmarks compared to much larger models.", "questions": "Have you investigated whether different model architectures or sizes hit the 4K example ceiling at different points?\n\nCould you explain the choice of k=2 for skill combinations? Have you explored other values?\n\nHow would the method perform with different teacher models (e.g., Claude, PaLM)?\n\nWould it be possible that combining synthetic data with human annotations potentially break through the 4K example ceiling?\n\nCould you elaborate on potential approaches for quality control in the data generation process?\n\nCould you provide analysis of model performance on longer-form tasks and multi-turn conversations?", "limitations": "", "rating": 3, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "The paper presents a novel approach to synthetic data generation that achieves strong results with only 4K examples, suggesting an efficient path forward for instruction tuning. The empirical validation is well-designed, testing across multiple benchmarks and models while including careful ablation studies that isolate the effects of different components. \n\nThe method is cost-effective, requiring only about $600 compared to traditional human annotation approaches. \n\nThe authors provide some analysis of how low-quality data affects model performance, offering practical insights for dataset creation. \n\nThe paper also test both their preferred method and a seed-dataset dependent variant, providing comparative insights.", "weaknesses": "The paper's most significant limitation is the performance plateau at 4K examples, with no clear explanation or analysis of learning curves as dataset size increases. This is compounded by limited investigation of whether different architectures or model sizes might hit different ceilings. \n\nThe evaluation methodology relies heavily on AlpacaEval 2.0 and lacks assessment of long-form generation and multi-turn conversations. The use of both teacher and grader models from the same model family (GPT-4) raises concerns about potential systematic biases. \n\nAlso, the methodology lacks a principled approach for determining the optimal number of skills or combinations, and provides no systematic quality metrics for the generated data. \n\nThe paper provides limited investigation of how different teacher models might affect results. Lack of this raises questions about the method's generalizability. \n\nThe relationship between skills and model performance remains inadequately explored, with no clear metrics for assessing skill quality or coverage.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "QdsessP9UA", "reviewer": "Reviewer_oHAp", "summary": "The authors propose a new instruction tuning data generation pipeline, INSTRUCT-SkillsMIX. They prompt a strong LLM to identify some key instruction following skills. They then use these skills to produce useful instruction following data. They show strong results on instruction following benchmarks where an LLM is used as a judge.", "questions": "- Can you compare performance to ShareGPT with the responses regenerate with GPT4-Turbo?\n- Can the authors discuss weakness 3?", "limitations": "", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- This paper shows very strong performance on benchmarks where LLMs are used as a judge.\n- The InstructSkillMix framework is novel and interesting. Moreover, it does not require any seed data, which is beneficial.", "weaknesses": "- The baseline methods are not fair: the main comparison is to Alpaca 52K, which is really old and known to be a low quality dataset. I think the authors should try comparing their dataset to stronger datasets such as ShareGPT with the responses regenerated by GPT4-Turbo.\n- In my opinion, section 1.1 is somewhat misleading. The authors (in line 70-75) say it is a mystery why public instruction tuning does not match the performance of proprietary instruct models. However, these proprietary models are trained in a variety of stages, and with distillation and RL techniques. It is not expected that instruction tuning alone can match the performance of proprietary models.\n- Table 4 suggests that the main reason for the good performance of InstructSkillMix is that a stronger model is used for distillation compared to previous IFT datasets. With the same judge, Alpaca-1K longest performs similarly to InstructSkill Mix (although Alpaca 1K longest is a weak dataset in my opinion: instructions are created using text-davinci-003). Alpaca-1K longest does perform worse on the length-controlled benchmark, but this is not a fair comparison since Alpaca-1K longest is specifically biased to encourage longer responses. \n- The authors claim that InstructSkillMix is a more performant data generation method than UltraChat and Vicuna (line 511). Although InstructSkillMix seems to be a strong method, my guess is that the primary reason that InstructSkillMix outperforms UltraChat and Vicuna is due to a stronger teacher model. If the authors want to make this claim, I think they should regenerate responses from UltraChat and ShareGPT using the same teacher model InstructSkillMix uses.\n- In my opinion relying only on AlpacaEval 2.0 and MTBench is a bit limited. It would be beneficial for the authors to evaluate their models on other tasks, including mathematical reasoning (MATH, GSM8K), instruction following (IFEval), and knowledge benchmarks (MMLU).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "R3KC4HY4US", "reviewer": "Reviewer_jh2B", "summary": "This paper focuses on constructing high-quality data for enhancing base LLMs’ instruction following capability. To achieve this goal, the authors propose a novel pipeline, called Instruct-SkillMix, which naturally combines diversity and difficulty in the data generation process. SFT with the resulting SFT dataset leads to very impressive performance gains on several established benchmarks. In particular, the data generation is fully automatic and the size of dataset can scale up easily.", "questions": "Kindly refer to the weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "- It finds that directly prompting a strong LLM to identify crucial skills achieves better performance than extracting skills from existing IFT datasets.\n- The performance of using merely thousands of Instruct-SkillMix data is impressive.\n- The data generation pipeline is fully automated and has nearly no human intervention.\n- It conducts detailed ablation studies and shows the contributions of different components.\n- It reveals that even a small amount of low-quality data greatly harms the instruction following performance.", "weaknesses": "- The type of queries and topics could be relevant to coverage of data. I think it might be worth to do ablation study on the query and topic types.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)