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.
Multi-round research combining GitHub API, web_search, web_fetch to produce comprehensive markdown reports.
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:
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):
Round 2 - Discovery (3-5 web_search)
Round 3 - Deep Investigation (5-10 web_search + web_fetch)
Round 4 - Deep Dive
Follow template in assets/report_template.md:
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
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| Low (50-69%) | Social media, unverified claims, outdated info |
Save report as: research_{topic}_{YYYYMMDD}.md
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:
The HTML file will be saved alongside the markdown file and can be directly opened in browser or hosted.
[citation:Title](URL) format immediately after each claim from external sourcesGood - 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.