github-deep-research

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():

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):

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

HTML Report Generation

IMPORTANT: After completing the markdown report, ALWAYS convert it to HTML format using the provided md2html.py script:

python /root/.openclaw/workspace/deer-flow/skills/public/github-deep-research/scripts/md2html.py research_{topic}_{YYYYMMDD}.md

This will generate a beautifully styled HTML file with:

• Professional typography and readable layout
• Syntax highlighted code blocks
• Styled tables and blockquotes
• Responsive design for all devices
• Print-friendly styling

The HTML file will be saved alongside the markdown file and can be directly opened in browser or hosted.

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:

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.