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

The issues command returns repository issues only, excluding pull requests. It follows additional pages when needed to fill the requested issue limit; use prs to research pull requests separately.

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