npx skills add ...
npx skills add bytedance/deer-flow --skill github-deep-research
npx skills add bytedance/deer-flow --skill github-deep-research
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.
Source Prioritization:
Round 1 - GitHub API
Directly execute scripts/github_api.py without read_file():
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):
Architecture (Flowchart):
Comparison (Pie/Bar):
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| Low (50-69%) | Social media, unverified claims, outdated info |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
[citation:Title](URL) format immediately after each claim from external sourcesGood - With inline citations:
Bad - Without citations:
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, 2025-04-01flowchart TD
A[User] --> B[Coordinator]
B --> C[Planner]
C --> D[Research Team]
D --> E[Reporter]pie title Market Share
"Project A" : 45
"Project B" : 30
"Others" : 25The 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).The project gained 10,000 stars within 3 months of launch.
The architecture uses LangGraph for workflow orchestration.