github-deep-research
Skill821 starsChanged 8 months agoMITView on GitHub
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.
We split this skill into the rules it gives an agent
7 rules. 4 are common, 0 are distinctive, and 3 are very specific to this project.
4
Common
Most of the 4 edited versions we read keep these.
0
Distinctive
This author's own choices. Worth a look.
3
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Common: line 21
Broad to Narrow: Start with GitHub API, then general queries, refine based on findings.
- Common: line 68
Use web_fetch on valuable URLs for full content
- Very specific: line 77
Follow template in assets/report_template.md:
- Very specific: line 137
IMPORTANT: After completing the markdown report, ALWAYS convert it to HTML format using the provided md2html.py script:
- Common: line 165
Note conflicting info: - Don't hide contradictions
- Very specific: line 167
CRITICAL: Always include inline citations: - Use citation:Title format immediately after each claim from external sources
- Common: line 169
Update as you go: - Don't wait until end to synthesize
“Common” is measured: kept by at least 6 in 10 of the 4 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
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> treeAvailable 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, 2025-04-01Architecture (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" : 25Confidence 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}.mdThis 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
- 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.Other versions4 projects use a version of this.
Word for word · 0
None found.
Edited · 4
- bytedance/deer-flow★ 84k99% same
- EvovexAI/EvoFlow★ 43699% same
- CreamyLong/quill★ 299% same
- WinterDDo/claude-skills★ 099% same
Wrote their own version · 0
None found.
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository25 other files it gives its agents.
- backend/AGENTS.mdAGENTS.md
- frontend/AGENTS.mdAGENTS.md
- backend/CLAUDE.mdCLAUDE.md
- frontend/CLAUDE.mdCLAUDE.md
- .github/copilot-instructions.mdCopilot instructions
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Your trail
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Now do this for your own.
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