Skill: quqxui/research-paper-compare (version 1.0.0)

--- name: Paper Compare slug: paper-compare version: 0.1.0 homepage: local description: Compare research papers by retrieving full PDFs from titles, URLs, or files and synthesizing differences, strengths, weaknesses, and evidence-backed trade-offs. --- ## When to Use User wants to compare multiple papers and may provide paper titles, links, or PDF files. Use this skill when the goal is to identify differences, strengths, weaknesses, assumptions, and trade-offs across papers. ## Quick Reference | Topic | File | |-------|------| | Input handling | `input-handling.md` | | Comparison schema | `comparison-schema.md` | | Output formats | `output-patterns.md` | | Evidence rules | `references/evidence-policy.md` | ## Core Rules 1. Retrieve and read PDF full text for every paper before doing substantive comparison. Do not rely on abstract-only evidence. 2. Normalize all inputs into comparable paper records before comparing them. 3. Compare papers on shared dimensions only, and avoid direct metric comparison when evaluation settings differ. 4. Mark evidence quality and uncertainty explicitly for each paper and each major claim. 5. Produce a table-first comparison, then add concise analytical synthesis. 6. If a full PDF cannot be obtained for one or more papers, stop the comparison and report which papers are blocked.

path: comparison-schema.md
content: # Comparison Schema

## Goal

Represent each paper with the same analytical fields before comparison.

## Required Paper Record

For each paper, extract the following fields:

- `title`
- `authors`
- `year`
- `venue`
- `pdf_source`
- `evidence_quality`
- `problem`
- `motivation`
- `core_method`
- `method_components`
- `assumptions`
- `training_or_construction_setup`
- `evaluation_setup`
- `datasets_or_benchmarks`
- `metrics`
- `main_results`
- `claimed_contributions`
- `strengths`
- `weaknesses`
- `limitations`
- `best_fit_scenarios`
- `notes_on_comparability`

## Extraction Guidance

### Problem

State the task or research question the paper addresses.

### Core method

Summarize the main method in 2-4 sentences using technical precision.

### Method components

List the main modules, mechanisms, or stages.

### Assumptions

Record modeling assumptions, data assumptions, or system assumptions that affect fairness of comparison.

### Evaluation setup

Record datasets, tasks, baselines, and protocol details needed for valid comparison.

### Main results

Summarize the most relevant quantitative and qualitative outcomes without overstating claims.

### Strengths / weaknesses / limitations

Separate author-claimed advantages from evidence-based critical assessment when possible.

### Notes on comparability

State whether direct comparison is valid, partially valid, or weak because of dataset or protocol mismatch.

## Cross-Paper Comparison Axes

Use these default axes unless the user asks for a more specific focus:

1. Problem setting
2. Core method
3. Key novelty
4. Experimental setting
5. Main strengths
6. Main weaknesses
7. Limitations
8. Best use scenarios
9. Comparability caveats
10. Evidence quality

## Minimum Quality Bar

Do not compare papers until all required fields are populated at least at a usable level.
If critical fields such as method, evaluation setup, or main results are missing, report insufficient evidence.


path: output-patterns.md
content: # Output Patterns

## Default Output Order

1. Paper acquisition summary
2. Comparison table
3. Analytical synthesis
4. Confidence and evidence notes

## 1. Paper Acquisition Summary

Briefly list:
- resolved paper titles
- PDF source for each paper
- whether full text was successfully read
- any retrieval or extraction caveats

## 2. Comparison Table

Use a compact table first.

Recommended columns:

- Paper
- Problem
- Core method
- Key distinguishing feature
- Strengths
- Weaknesses
- Limitations
- Best-fit scenario
- Evidence quality

Add `Experimental setting` or `Comparability caveat` if they materially affect interpretation.

## 3. Analytical Synthesis

After the table, provide a concise analysis with these subsections:

### Shared focus

What common problem or theme connects the papers?

### Main differences

What are the major methodological or experimental differences?

### Comparative advantages

What does each paper do especially well?

### Comparative weaknesses

Where does each paper fall short?

### When to prefer which paper

Explain selection logic based on task setting, constraints, or research goals.

## 4. Confidence and Evidence Notes

Explicitly state:
- whether each paper was compared from full PDF evidence
- whether text extraction quality was high / medium / low
- whether direct metric comparison is safe or unsafe
- which conclusions are robust and which remain tentative

## Optional Variants

### Theme-focused comparison

If the user specifies an axis, reorganize both the table and analysis around that axis.
Examples:
- method design
- evaluation quality
- efficiency
- robustness
- applicability

### Shortlist recommendation

If the user asks which paper is better for a goal, end with a brief recommendation grounded in extracted evidence, not preference language alone.


path: input-handling.md
content: # Input Handling

## Goal

Convert user inputs into a validated list of papers with accessible full PDFs.

## Accepted Inputs

- Paper titles
- Paper URLs
- Uploaded PDF files

## Workflow

### 1. Parse the input set

Identify each paper candidate and classify it as:
- title-only
- URL
- local PDF

### 2. Resolve each paper to a full PDF

#### If the input is a title

1. Search the web for the canonical paper source.
2. Prefer sources in this order:
   - arXiv
   - OpenReview
   - ACL Anthology / conference proceedings
   - publisher or author project page
3. Find a PDF URL and verify that it is the target paper.
4. Download or fetch the PDF text for analysis.

#### If the input is a URL

1. Inspect whether the URL is already a PDF.
2. If not, identify the paper landing page and locate the PDF link.
3. Verify title, venue, and year when possible.
4. Retrieve the PDF full text.

#### If the input is a local PDF

1. Extract title and basic metadata from the file.
2. Read the full text.
3. If metadata is weak or missing, use the extracted title to search for a canonical source and citation.

### 3. Validate evidence sufficiency

A paper is eligible for comparison only if:
- the PDF full text is available, and
- enough text can be extracted to identify method, setting, and results sections.

If extraction returns only metadata, abstract, or a fragmentary preview, treat the paper as blocked.

### 4. Build a paper record

After PDF retrieval, create one normalized record per paper using `comparison-schema.md`.

## Failure Policy

Stop and report blockers if any target paper lacks accessible PDF full text.
Do not proceed with cross-paper analysis based only on abstracts, snippets, or search summaries.

## Search Notes

Use web search to discover canonical sources, but use the PDF full text as the primary evidence source.
Search is for retrieval; PDF is for analysis.


path: references/memory-dimensions.md
content: # Memory Dimensions

This file is optional and should be read only when the compared papers are specifically about memory systems, retrieval systems, long-context methods, or agent memory.

## Additional Comparison Axes

- memory representation
- storage unit
- write policy
- retrieval trigger
- retrieval granularity
- summarization or compression strategy
- forgetting or pruning strategy
- latency and scalability implications
- memory-update reliability
- long-horizon consistency

## Usage Note

These dimensions are optional extensions.
The base `paper-compare` skill remains domain-general and should not assume a memory-specific lens unless the user asks for it or the paper set clearly requires it.


path: references/evidence-policy.md
content: # Evidence Policy

## Non-Negotiable Rule

Do not perform substantive paper comparison unless the full PDF text has been obtained and read for every paper in scope.
Abstract-only evidence is insufficient.

## Evidence Tiers

### Tier A — Full PDF, high extraction quality

Use when the paper PDF is available and the extracted text clearly covers method, setup, and results.
This is the preferred comparison basis.

### Tier B — Full PDF, partial extraction quality

Use when the PDF is available but extraction is noisy, incomplete, or hard to parse.
Comparison is allowed only if the key sections remain interpretable.
Mark the output with caution.

### Tier C — Metadata or abstract only

This includes:
- title + abstract pages
- search snippets
- landing pages without readable PDF body
- citation metadata only

Do not use Tier C as the basis for comparison.
Stop and report the missing PDF evidence.

## Claim Discipline

For each major comparative claim, ensure the evidence source is traceable to one of these paper components:
- problem statement
- method section
- experiment / evaluation section
- conclusion or discussion

Do not infer strong claims from titles, abstracts, or benchmark tables alone.

## Confidence Labels

Use these labels in the final output:

- `High confidence` — supported by readable full-text evidence with good comparability
- `Moderate confidence` — supported by full-text evidence, but extraction or comparability has limitations
- `Low confidence` — evidence is partial, noisy, or cross-paper comparability is weak

## Comparability Guardrails

Do not compare metrics directly when:
- datasets differ
- task definitions differ
- baseline sets differ
- evaluation protocols differ
- reported numbers are not aligned to the same setup

In such cases, compare design choices, assumptions, and reported tendencies instead of claiming strict superiority.

## Failure Handling

If even one requested paper lacks usable PDF full text, report:
1. which paper is blocked
2. why PDF-level evidence is missing
3. what additional input is needed from the user

Do not silently downgrade to abstract-based analysis.


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