# analyzing-research-papers

Structured framework for reviewing research papers from PDFs, DOIs, or arXiv links. Extracts core contributions, tests reproducibility, and spots methodological flaws.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 552 installs · ★ 6
- Author: seabbs
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/seabbs/skills/tree/HEAD/plugins/research-academic/skills/analyzing-research-papers/SKILL.md
- Updated: 2026-08-25
- HTML: https://affpapa.org/en/nevibecode/skills/analyzing-research-papers

## What it does

This skill guides an AI agent through a systematic review of academic papers. It processes local files in `PDF` or `Markdown`, paper DOIs, and direct links to arXiv, bioRxiv, or medRxiv. When a paper is locked behind a paywall, the skill instructs the assistant to pull the public abstract and metadata while explicitly noting access boundaries.

The workflow starts by classifying the work into theoretical, methodological, empirical, or review categories. From there, it isolates the main thesis, experimental baseline, metrics, and dataset properties. The assessment flags whether researchers shared code, seeds, and hyperparameters, or left out critical implementation details.

If a paper lacks open datasets or hides parameters behind brevity claims, the analysis logs low reproducibility directly rather than filling gaps with guesswork.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add seabbs/skills --skill analyzing-research-papers -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/analyzing-research-papers/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/analyzing-research-papers/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add seabbs/skills --skill analyzing-research-papers -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/analyzing-research-papers/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add seabbs/skills --skill analyzing-research-papers -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/analyzing-research-papers/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install seabbs/skills analyzing-research-papers
```

2. **Manually: put the files in place**
- File: `.github/skills/analyzing-research-papers/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add seabbs/skills --skill analyzing-research-papers -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/seabbs/skills --path plugins/research-academic/skills/analyzing-research-papers
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/analyzing-research-papers/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add seabbs/skills --skill analyzing-research-papers -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/seabbs/skills/tree/HEAD/plugins/research-academic/skills/analyzing-research-papers/SKILL.md

## When to use it
- extract key contributions and statistical results from papers
- evaluate research methodology and stated assumptions
- audit code availability and experiment reproducibility
- summarize academic papers from DOI or arXiv URLs

## Example prompts
- Analyze the paper at `https://arxiv.org/pdf/2301.12345.pdf` and list its main methodological limitations
- Review `./papers/smith2024.pdf` and check its reproducibility based on shared code and parameters
- Extract key claims and statistical evidence from DOI `10.1234/journal.2024.12345`

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: f8a23c9f4dcb9004.

BEGIN THIRD-PARTY FILE f8a23c9f4dcb9004
```text
---
name: analyzing-research-papers
description: Expert methodology for analyzing and summarizing research papers, extracting key contributions, methodological details, and contextualizing findings. Use when reading papers from PDFs, DOIs, or URLs to create structured summaries for researchers.
---

# Analyzing Research Papers

This skill provides expertise in systematically analyzing research papers to extract key insights, evaluate methodological rigour, and contextualize findings for researchers.

## Paper Access Methods

### Input Formats Accepted

**Local files:**
- Absolute paths: `/path/to/paper.pdf`
- Relative paths: `./papers/smith2024.pdf`
- Markdown files: `paper.md`

**DOIs:**
- Standard format: `10.1234/journal.2024.12345`
- With prefix: `doi:10.1234/journal.2024.12345`
- Resolve using: `https://doi.org/{doi}`

**URLs:**
- ArXiv: `https://arxiv.org/pdf/2301.12345.pdf`
- Journal websites: Direct PDF or HTML links
- Preprint servers: bioRxiv, medRxiv, etc.

### Handling Access Issues

**Paywalled content:**
- Work with available abstract and metadata
- Extract what's publicly accessible
- Note limitations in summary
- Suggest open access alternatives

**PDF reading failures:**
- Request text version if available
- Try alternative formats (HTML, arXiv)
- Extract from DOI metadata

## Analysis Framework

### Initial Scan

**Identify paper structure:**
- Abstract and key claims
- Section organization (IMRaD vs custom)
- Figures and tables overview
- Reference density and key citations

**Classify paper type:**
- Theory: Proofs, mathematical foundations, formal results
- Methods: New algorithms, techniques, computational approaches
- Application: Domain-specific use cases, case studies
- Review: Surveys, systematic reviews, meta-analyses
- Empirical: Experimental results, observations, measurements

### Content Extraction Priorities

**Must extract:**
1. Main contribution(s) and claims
2. Methodological approach and assumptions
3. Key results with statistical evidence
4. Limitations acknowledged
5. Related work positioning

**Important to capture:**
- Experimental setup and validation
- Implementation details
- Performance metrics and comparisons
- Dataset characteristics
- Reproducibility information

**Nice to have:**
- Future work suggestions
- Broader implications
- Alternative approaches considered
- Failure modes discussed

## Quality Assessment Criteria

### Methodological Rigour

**Strong indicators:**
- Clear research questions
- Appropriate methodology for questions
- Controlled comparisons
- Statistical significance properly assessed
- Limitations openly discussed
```
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