- 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 Reference sources - Add source references near claims where applicable Update as you go - Don't wait until end to synthesize
github-deep-research
安装
npx skills add https://github.com/bytedance/deer-flow --skill github-deep-research