---
name: paper-analysis
description: Read, summarize, and critically analyze scientific papers. Extract key findings, methodology, limitations, and contributions. Use when user shares a paper (PDF/URL/DOI), asks to summarize a paper, critique methodology, extract data from a paper, compare papers, or do a critical review. Triggers on "summarize this paper", "analyze this study", "what does this paper say", "critique this methodology", "extract findings from".
---

# Paper Analysis

Systematic scientific paper reading, summarization, and critical analysis.

## Paper Acquisition

1. If user provides a DOI: fetch via `https://doi.org/DOI` or Semantic Scholar API
2. If user provides arXiv ID: fetch via `https://arxiv.org/abs/ID`
3. If user provides a URL: use `web_fetch` to extract content
4. If user provides a PDF file: read directly or use summarize skill

## Analysis Framework

### Quick Summary (default)
Provide in ~200 words:
- Research question / objective
- Method (1-2 sentences)
- Key findings (2-3 bullet points)
- Main contribution
- One key limitation

### Deep Analysis
When requested, provide structured analysis:

#### 1. Paper Metadata
- Title, authors, year, journal/venue, DOI
- Citation count (via Semantic Scholar)

#### 2. Research Context
- Problem statement and motivation
- Research gap being addressed
- Theoretical framework

#### 3. Methodology Assessment
- Study design (experimental/observational/computational/theoretical)
- Sample/dataset description
- Variables (independent, dependent, controls)
- Analysis methods
- Reproducibility assessment (data/code availability)

#### 4. Results Evaluation
- Key findings with effect sizes and confidence intervals
- Statistical significance vs practical significance
- Figures and tables interpretation
- Are results consistent with claims?

#### 5. Critical Assessment

Check for:
- **Internal validity**: confounds, selection bias, measurement error
- **External validity**: generalizability, ecological validity
- **Statistical issues**: multiple comparisons, p-hacking, small N
- **Logical issues**: correlation ≠ causation, survivorship bias
- **Reporting issues**: selective reporting, missing negative results
- **Methodological rigor**: appropriate controls, blinding, randomization

#### 6. Contribution & Impact
- Novelty assessment
- Practical implications
- Theoretical implications
- Future directions suggested

## Comparison Mode

When comparing multiple papers:

| Dimension | Paper A | Paper B | Paper C |
|-----------|---------|---------|---------|
| Research Question | | | |
| Method | | | |
| Sample Size | | | |
| Key Finding | | | |
| Limitation | | | |
| Strength | | | |

## Domain-Specific Checklists

### RCT (Randomized Controlled Trial)
- CONSORT checklist compliance
- Randomization method, allocation concealment
- Blinding (single/double/triple)
- ITT vs per-protocol analysis
- Dropout rates and handling

### Observational Studies
- STROBE checklist
- Confounding control methods
- Selection bias assessment

### Machine Learning Papers
- Train/val/test split methodology
- Baseline comparisons
- Ablation studies
- Statistical significance of improvements
- Computational cost reporting
- Code/data availability

### Qualitative Research
- Sampling strategy (purposive, theoretical, snowball)
- Data saturation
- Coding methodology (thematic, grounded theory)
- Reflexivity and positionality
- Member checking / triangulation

## Output Style
- Use academic but accessible language
- Cite specific sections/figures/tables from the paper
- Distinguish between what the paper claims and what the evidence supports
- Flag any red flags clearly but diplomatically


---
Source attribution: beita6969
Original project: https://github.com/beita6969/ScienceClaw/blob/e925f732c7ca435f557e88075bc47a58f1e984f1/skills/paper-analysis/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2025 Peter Steinberger

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: beita6969
Original project: https://github.com/beita6969/ScienceClaw/blob/e925f732c7ca435f557e88075bc47a58f1e984f1/skills/paper-analysis/SKILL.md
Attribution basis: Source repository record

Fields of work: Data & Analytics, Education & Research, Healthcare & Life Sciences
Roles: Academic Writer, Clinical Research, Data Analyst, Market Research Analyst, Researcher
Specialties: Specialty not specified
Tools: Semantic Scholar Api, Web Fetch
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: d60e56ad447efda3dd140dc09ffd127cdfd02a6ea6e7032fdde644a9a0336de3