Skill: gpiox/paper-pdf-two-pass-review (version 1.0.0)

--- name: cvpr-pdf-two-pass-review description: Analyze uploaded CVPR paper PDFs with a strict two-pass workflow. First pass extracts verifiable facts with page/section/figure/table evidence only. Second pass produces a 2000-3000 Chinese research critique grounded in those facts. Use when the user uploads a paper PDF and asks for deep interpretation, critical review, reproducibility assessment, or PhD-level reading notes. --- # CVPR PDF Two-Pass Review ## Overview Run a deterministic two-pass paper analysis workflow on an uploaded PDF. Prioritize evidence-grounded outputs and reduce hallucinations by separating fact extraction from interpretation. ## Workflow 1. Confirm the user has uploaded the PDF in the current chat. If not, ask them to upload it first. 2. Load and execute prompt in `references/round1.md` to produce a structured fact sheet. 3. Load and execute prompt in `references/round2.md` using only round-1 facts plus PDF evidence. 4. Enforce evidence tags for core claims: `[Evidence: p.X, Sec.Y, Fig/Table Z]`. 5. If any required information is missing, explicitly state `Not provided in paper` or `Insufficient evidence`. ## Output Rules - Keep analysis strictly based on uploaded PDF content. - Do not introduce external papers, benchmarks, or assumptions unless user explicitly asks. - Keep language concise and technical. - Preserve required length in pass 2: 2000-3000 words or characters as requested by the prompt. ## References - Round 1 prompt: `references/round1.md` - Round 2 prompt: `references/round2.md`

path: agents/openai.yaml
content: interface:
  display_name: "CVPR PDF Two-Pass Review"
  short_description: "���ַ�����CVPR����PDF������ʵ��ȡ�����о����"
  default_prompt: "�ϴ�PDF��ִ�����ַ�������ʵ��ȡ -> ��Ƚ��"


path: references/round1.md
content: You are doing FACT EXTRACTION ONLY.
Do not evaluate quality, do not conclude impact, and do not use external knowledge.
Use only the uploaded PDF.

Task: extract a structured fact sheet from this paper with evidence locations.
If information is missing, write `Not provided`.

Output format:

# A. Paper Basics
- Title:
- Task type (classification/detection/segmentation/generation/multimodal/etc.):
- One-sentence research question:
- Claimed contributions (as stated by authors):

# B. Method Facts
- End-to-end pipeline (input -> modules -> output):
- Key module 1:
  - Purpose:
  - Related equation:
  - Evidence:
- Key module 2:
- Key module 3:
- Training objectives/losses (list each):
- Inference procedure (if any):
- Time/space complexity statements (if any):

# C. Experiment Facts
- Datasets:
- Metrics:
- Baseline list:
- Main results (only key numbers):
- Ablation settings and numbers:
- Visualization experiment types:
- Efficiency results (params/FLOPs/speed):

# D. Reproducibility Facts
- Training hyperparameters (batch size/lr/epoch/optimizer):
- Data preprocessing and augmentation:
- Hardware setup:
- Random seeds/repeated runs:
- Code and model release status:

# E. Limitations and Failure Cases (author-stated)
- Limitation 1:
- Limitation 2:
- Failure scenarios:

Requirements:
1) Add evidence for each item when possible: [Evidence: p.X, Sec.Y, Fig/Table Z].
2) Record facts only. No judgments.
3) End with an "Information Gaps" list (missing details needed for faithful reproduction).


path: references/round2.md
content: Now produce a deep research critique based on the fact sheet you just extracted.
Constraint: use only the fact sheet plus PDF evidence. No external sources or guesses.
If uncertain, explicitly write `Insufficient evidence`.

Output language: Chinese.
Length: 2000-3000 Chinese words.

Required structure:

# 0. One-line Overall Verdict
- Problem value + method essence + empirical credibility

# 1. Motivation (Why)
## 1.1 Problem positioning
## 1.2 Prior-method bottlenecks (3-5 groups)
## 1.3 Motivation storyline (observation -> contradiction -> hypothesis -> entry point)

# 2. Core Method (How)
## 2.1 Method overview
## 2.2 Core equations (variables + intuition + relation to classic methods)
## 2.3 Design intuition and key trade-offs
## 2.4 Failure boundaries

# 3. Experiments and Results (What)
## 3.1 Validation logic
## 3.2 What results truly show
## 3.3 Ablation insights
## 3.4 Extra evidence from visualizations

# 4. Critical Review
## 4.1 Key assumptions and validity conditions
## 4.2 Baseline fairness and experiment fairness
## 4.3 Reproducibility risks (High/Medium/Low)

# 5. Research Insights
## 5.1 Field contribution
## 5.2 Transferable methodology
## 5.3 Top 3 follow-up directions (prioritized)

# 6. Closing
1) Three takeaways
2) Two major risks
3) One next step if you were the author

Writing constraints:
- For each major claim, add evidence tags: [Evidence: ...].
- Include at least one intuitive analogy per main section.
- Lead with conclusion, then provide evidence.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)