github-deep-research
bytedance/deer-flow
Conduct comprehensive multi-round research on GitHub repositories with structured reports, timelines, and analysis.
What is github-deep-research?
Automates deep investigation of GitHub repositories by combining GitHub API queries, web search, and content fetching across four research rounds. Produces structured markdown reports with executive summaries, chronological timelines, metrics analysis, and Mermaid diagrams. Use when you need thorough competitive analysis, project history reconstruction, or in-depth open-source investigation.
- Executes four-round research workflow: GitHub API → Discovery → Deep Investigation → Deep Dive
- Generates structured markdown reports with executive summaries, timelines, and confidence-scored claims
- Creates Mermaid diagrams for architecture, timelines, and comparisons
- Extracts and prioritizes sources from official docs, technical blogs, news, and community discussions
- Analyzes commit history, issues, PRs, and contributor activity for timeline reconstruction
- Includes inline citations for all external claims with confidence scoring
How to install github-deep-research
npx skills add https://github.com/bytedance/deer-flow --skill github-deep-research- GitHub API access (public repos require no auth; private repos need token)
- Python environment with requests library for github_api.py script
- Web search and web_fetch capabilities enabled
How to use github-deep-research
- 1.Provide a GitHub repository URL or project name to trigger the skill
- 2.The skill automatically executes Round 1 using github_api.py to fetch repo metadata, README, tree structure, and contributor data
- 3.Rounds 2-3 perform web searches with progressively refined queries to discover overview, architecture, and competitive context
- 4.Round 4 analyzes commit history and issues for timeline validation
- 5.Review the generated markdown report with executive summary, chronological timeline, metrics, and confidence assessments
- 6.Check inline citations and source categorization to evaluate claim reliability
Use cases
- Competitive analysis comparing multiple open-source projects
- Timeline reconstruction of a project's development phases and key milestones
- Technical architecture deep-dive for unfamiliar codebases
- Community sentiment and adoption analysis across multiple sources
- Due diligence research on dependencies or third-party libraries
- Software engineers evaluating open-source projects
- Product managers conducting competitive research
- Technical leads assessing technology choices
- Researchers studying open-source ecosystems
- DevRel professionals analyzing project adoption
github-deep-research FAQ
The skill supports: summary, info, readme, tree, languages, contributors, commits, issues, prs, and releases. Execute via python /path/to/skill/scripts/github_api.py <owner> <repo> <command>.
Official docs/repos (highest), technical blogs like Medium/Dev.to, news articles from verified outlets, community discussions (Reddit/HN), and social media (lowest weight for sentiment only).
High (90%+) for official docs and multiple corroborating sources; Medium (70-89%) for single reliable sources; Low (50-69%) for social media or unverified claims.
Yes, but requires a GitHub API token. Public repositories work without authentication.
Structured markdown file named research_{topic}_{YYYYMMDD}.md with metadata, executive summary, timeline, analysis sections, metrics tables, and Mermaid diagrams.
Full instructions (SKILL.md)
Source of truth, from bytedance/deer-flow.
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:
- Official docs/repos (highest weight)
- Technical blogs (Medium, Dev.to)
- News articles (verified outlets)
- Community discussions (Reddit, HN)
- Social media (lowest weight, for sentiment)
Research Rounds
Round 1 - GitHub API
Directly execute scripts/github_api.py without read_file():
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:
- Metadata Block - Date, confidence level, subject
- Executive Summary - 2-3 sentence overview with key metrics
- Chronological Timeline - Phased breakdown with dates
- Key Analysis Sections - Topic-specific deep dives
- Metrics & Comparisons - Tables, growth charts
- Strengths & Weaknesses - Balanced assessment
- Sources - Categorized references
- Confidence Assessment - Claims by confidence level
- Methodology - Research approach used
Mermaid Diagrams
Include diagrams where helpful:
Timeline (Gantt):
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):
flowchart TD
A[User] --> B[Coordinator]
B --> C[Planner]
C --> D[Research Team]
D --> E[Reporter]
Comparison (Pie/Bar):
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
- Start with official sources - Repo, docs, company blog
- Verify dates from commits/PRs - More reliable than articles
- Triangulate claims - 2+ independent sources
- Note conflicting info - Don't hide contradictions
- Distinguish fact vs opinion - Label speculation clearly
- CRITICAL: Always include inline citations - Use
[citation:Title](URL)format immediately after each claim from external sources - Extract URLs from search results - web_search returns {title, url, snippet} - always use the URL field
- Update as you go - Don't wait until end to synthesize
Citation Examples
Good - With inline citations:
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:
The project gained 10,000 stars within 3 months of launch.
The architecture uses LangGraph for workflow orchestration.
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