Desktop github-deep-research
Conduct multi-round deep research on any GitHub repository. Use when users request comprehensive analysis, timeline reconstruction, competitive analysis, or in-depth investigation of a GitHub project. Produces structured markdown reports with executive summaries, chronological timelines, metrics analysis, and Mermaid diagrams. Triggers on GitHub repository URLs or open source project names.
git clone https://github.com/openyak/openyak
T=$(mktemp -d) && git clone --depth=1 https://github.com/openyak/openyak "$T" && mkdir -p ~/.claude/skills && cp -r "$T/backend/app/data/skills/github-deep-research" ~/.claude/skills/openyak-desktop-github-deep-research && rm -rf "$T"
backend/app/data/skills/github-deep-research/SKILL.mdGitHub 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
Execute the bundled
scripts/github_api.py using the bash tool:
python scripts/github_api.py <owner> <repo> summary python scripts/github_api.py <owner> <repo> readme python scripts/github_api.py <owner> <repo> tree
The script path is relative to this skill's base directory (shown in the skill output).
Available commands (the last argument of
):github_api.py
- summary — comprehensive overview (stars, forks, languages, latest release)
- info — basic repository metadata
- readme — repository README content
- tree — directory structure (depth 3)
- languages — language breakdown by bytes
- contributors — top contributors
- commits — recent commit history
- issues — open/closed issues
- prs — pull requests
- releases — release history
Environment: Set
GITHUB_TOKEN for higher API rate limits (optional but recommended).
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
- Always include inline citations - Use
format immediately after each claim from external sources[citation:Title](URL) - 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 a multi-agent workflow [citation:Official Docs](https://docs.example.com).
Bad - Without citations:
The project gained 10,000 stars within 3 months of launch. The architecture uses a multi-agent workflow.