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

• 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.