Skip to main content
skill CX Codex

github-deep-research — Claude Code skill: deep dive into GitHub repos

Runs multi-round research on any GitHub repository and writes a structured markdown report with a timeline, metrics and Mermaid diagrams.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓
npx skills add bytedance/deer-flow --skill github-deep-research -a claude-code
Open on GitHub

What github-deep-research does

The skill works in four rounds. First it pulls repo data through the GitHub API with scripts/github_api.py, then searches for an overview and key terms, digs into architecture and community sentiment, and finishes with commit history, issues and pull requests. The script runs directly, without read_file(), and accepts summary, info, readme, tree, languages, contributors, commits, issues, prs and releases.

Output lands in research_{topic}_{YYYYMMDD}.md, built from assets/report_template.md: metadata, executive summary, phased timeline, metrics tables, strengths and weaknesses, sources, and a confidence assessment. Mermaid diagrams cover timelines, architecture and comparisons.

Python is required. Without it github_api.py will not run and the first round fails. Every claim from an external source needs an inline [citation:Title](URL)] link, and confidence is split into three levels, from official docs down to social media.

What it helps with

  • reconstruct a project timeline from commits and releases
  • compare a repository against alternatives
  • gauge contributor activity
  • map architecture from code and discussions
  • produce a report with timeline and diagrams

Example prompts for the agent

  • Research https://github.com/bytedance/deer-flow and build a report with a timeline
  • Do a deep dive on this GitHub repo and add an architecture diagram
  • Compare this repository with alternatives and rate the confidence of your findings

How to install github-deep-research

1Skills CLI (Vercel)

npx skills add bytedance/deer-flow --skill github-deep-research -a claude-code

2Manually: put the files in place

~/.claude/skills/github-deep-research/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/github-deep-research/

Step by step, with commands for each platform: How to install skills in Claude Code: five ways

Questions about github-deep-research

How do I install github-deep-research in Claude Code?

Run npx skills add bytedance/deer-flow --skill github-deep-research -a claude-code. The skill lands in ~/.claude/skills/github-deep-research/SKILL.md.

What does github-deep-research do?

It runs multi-round research on a GitHub repository through the API and web search, then writes a markdown report with a summary, timeline, metrics and Mermaid diagrams.

What does the skill need to run?

Python, for the scripts/github_api.py script. Without it the first round of GitHub API data will not run.

Source text

SKILL.md · MIT
---
name: github-deep-research
description: Conduct multi-round deep research on any GitHub Repo. Use when users request comprehensive analysis, timeline reconstruction, competitive analysis, or in-depth investigation of GitHub. Produces structured markdown reports with executive summaries, chronological timelines, metrics analysis, and Mermaid diagrams. Triggers on Github repository URL or open source projects.
---

# GitHub Deep Research Skill

Multi-round research combining GitHub API, web_search, web_fetch to produce comprehensive markdown reports.

## Research Workflow

- Round 1: GitHub API
- Round 2: Discovery
- Round 3: Deep Investigation
- Round 4: Deep Dive

## Core Methodology

### Query Strategy

**Broad to Narrow**: Start with GitHub API, then general queries, refine based on findings.

```
Round 1: GitHub API
Round 2: "{topic} overview"
Round 3: "{topic} architecture", "{topic} vs alternatives"
Round 4: "{topic} issues", "{topic} roadmap", "site:github.com {topic}"
```

**Source Prioritization**:
1. Official docs/repos (highest weight)
2. Technical blogs (Medium, Dev.to)
3. News articles (verified outlets)
4. Community discussions (Reddit, HN)
5. Social media (lowest weight, for sentiment)

### Research Rounds

**Round 1 - GitHub API**
Directly execute `scripts/github_api.py` without `read_file()`:
```bash
python /path/to/skill/scripts/github_api.py <owner> <repo> summary
python /path/to/skill/scripts/github_api.py <owner> <repo> readme
python /path/to/skill/scripts/github_api.py <owner> <repo> tree
```

**Available commands (the last argument of `github_api.py`):**
- summary
- info
- readme
- tree
- languages
- contributors
- commits
- issues
- prs
- releases

**Round 2 - Discovery (3-5 web_search)**
- Get overview and identify key terms
- Find official website/repo
- Identify main players/competitors

**Round 3 - Deep Investigation (5-10 web_search + web_fetch)**
- Technical architecture details
- Timeline of key events
- Community sentiment
- Use web_fetch on valuable URLs for full content

**Round 4 - Deep Dive**
- Analyze commit history for timeline
- Review issues/PRs for feature evolution
- Check contributor activity

## Report Structure

Follow template in `assets/report_template.md`:

1. **Metadata Block** - Date, confidence level, subject
2. **Executive Summary** - 2-3 sentence overview with key metrics
3. **Chronological Timeline** - Phased breakdown with dates
4. **Key Analysis Sections** - Topic-specific deep dives
5. **Metrics & Comparisons** - Tables, growth charts
6. **Strengths & Weaknesses** - Balanced assessment
7. **Sources** - Categorized references
8. **Confidence Assessment** - Claims by confidence level
9. **Methodology** - Research approach used

### Mermaid Diagrams

Include diagrams where helpful:

**Timeline (Gantt)**:
```mermaid
gantt
    title Project Timeline
    dateFormat YYYY-MM-DD
    section Phase 1
    Development    :2025-01-01, 2025-03-01
    section Phase 2
    Launch         :2025-03-01, 2025-04-01
```

**Architecture (Flowchart)**:
```mermaid
flowchart TD
    A[User] --> B[Coordinator]
    B --> C[Planner]
    C --> D[Research Team]
    D --> E[Reporter]
```

**Comparison (Pie/Bar)**:
```mermaid
pie title Market Share
    "Project A" : 45
    "Project B" : 30
    "Others" : 25
```

## Confidence Scoring

Assign confidence based on source quality:

| Confidence | Criteria |
|------------|----------|
| High (90%+) | Official docs, GitHub data, multiple corroborating sources |
| Medium (70-89%) | Single reliable source, recent articles |
| Low (50-69%) | Social media, unverified claims, outdated info |

## Output

Save report as: `research_{topic}_{YYYYMMDD}.md`

### Formatting Rules

- Chinese content: Use full-width punctuation(,。:;!?)
- Technical terms: Provide Wiki/doc URL on first mention
- Tables: Use for metrics, comparisons
- Code blocks: For technical examples
- Mermaid: For architecture, timelines, flows

## Best Practices

1. **Start with official sources** - Repo, docs, company blog
2. **Verify dates from commits/PRs** - More reliable than articles
3. **Triangulate claims** - 2+ independent sources
4. **Note conflicting info** - Don't hide contradictions
5. **Distinguish fact vs opinion** - Label speculation clearly
6. **CRITICAL: Always include inline citations** - Use `[citation:Title](URL)` format immediately after each claim from external sources
7. **Extract URLs from search results** - web_search returns {title, url, snippet} - always use the URL field
8. **Update as you go** - Don't wait until end to synthesize

### Citation Examples

**Good - With inline citations:**
```markdown
The project gained 10,000 stars within 3 months of launch [citation:GitHub Stats](https://github.com/owner/repo).
The architecture uses LangGraph for workflow orchestration [citation:LangGraph Docs](https://langchain.com/langgraph).
```

**Bad - Without citations:**
```markdown
The project gained 10,000 stars within 3 months of launch.
The architecture uses LangGraph for workflow orchestration.
```

© bytedance · MIT · bytedance/deer-flow

Similar skills

All skills for Git and GitHub →

From the same set18

The whole set →
frontend-design The skill picks an aesthetic direction before the agent writes any code. Useful when you want a landing page, dashboard or component that does not look auto-generated. CX Codex +1 3,700installs data-analysis This skill inspects uploaded Excel and CSV files, runs SQL against them through DuckDB, and exports the results. CX Codex +1 3,600installs video-generation Generates videos via Gemini Veo or MiniMax using structured JSON prompts, optional reference images, and an automated Python script. CX Codex +1 3,000installs consulting-analysis This skill plans a research framework, then turns collected material into a structured report. Use it for market, financial, brand, or competitor analysis. CX Codex +1 3,000installs image-generation Builds structured JSON prompts and executes a Python script to generate images with optional reference files. Useful for character design and complex scene rendering. CX Codex +1 2,900installs deep-research A skill for thorough web research using multi-angle analysis methodology. Use it instead of basic search when you need to understand a topic from all angles before writing content or tackling a complex question. CX Codex +1 2,800installs academic-paper-review A skill for automated peer review of academic papers following top-conference standards. Assesses methodology, evaluates novelty, identifies weaknesses, and delivers evidence-based critique. CX Codex +1 2,700installs chart-visualization Picks a chart type from 26 options, maps your data to the right parameters, and renders an image with a JavaScript script. CX Codex +1 2,600installs

Project sponsors

Companies that keep this analytics open
Gold sponsor

Impressions, clicks and code copies — open statistics. We keep the numbers public for the same reason NeBlask itself is public. Become a sponsor