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:

1. Official docs/repos (highest weight)
2. Technical blogs (Medium, Dev.to)
3. News articles (verified outlets)
4. Community discussions (Reddit, HN)
5. 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:

1. Metadata Block - Date, confidence level, subject
2. Executive Summary - 2-3 sentence overview with key metrics
3. Chronological Timeline - Phased breakdown with dates
4. Key Analysis Sections - Topic-specific deep dives
5. Metrics & Comparisons - Tables, growth charts
6. Strengths & Weaknesses - Balanced assessment
7. Sources - Categorized references
8. Confidence Assessment - Claims by confidence level
9. 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

1. Start with official sources - Repo, docs, company blog
2. Verify dates from commits/PRs - More reliable than articles
3. Triangulate claims - 2+ independent sources
4. Note conflicting info - Don't hide contradictions
5. Distinguish fact vs opinion - Label speculation clearly
6. CRITICAL: Always include inline citations - Use [citation:Title](https://raw.githubusercontent.com/bytedance/deer-flow/main/URL) format immediately after each claim from external sources
7. Extract URLs from search results - web_search returns {title, url, snippet} - always use the URL field
8. 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.