Imported from tiangong-ai/agent-skills (github-contribution-period-analysis/SKILL.md). Install upstream with npx skills add tiangong-ai/agent-skills --skill github-contribution-period-analysis. Copyright stays with the author.

GitHub Contribution Period Analysis

Core Goal

• Build an evidence-based Markdown report for one target GitHub user in a UTC time window.
• Focus on practical code contribution signals: merged PR and commit activity.
• Cover both own repositories and other repositories, limited by credential access.

Workflow

1. Prepare authentication for GitHub API access.
2. Run scripts/github_contribution_report.py with target user and time window.
3. Review the generated Markdown report and inspect evidence links.

Command

python3 scripts/github_contribution_report.py \
  --user <github-login> \
  --start 2026-03-01 \
  --end 2026-03-08 \
  --output /tmp/github-contribution-report.md

Output Semantics

• Included contribution types:
  • Authored merged PRs.
  • PRs merged by the target user (maintainer merge work).
  • Authored commits.
• Excluded activity types:
  • Comments, reviews, stars, watches, forks.
• The report contains:
  • Scope and coverage notes.
  • Summary metrics.
  • Substantive work categories inferred from PR/commit evidence.
  • Work content highlights with representative evidence links.
  • Repository breakdown.
  • Per-repository concrete change details (for every changed repository).
  • Contribution timeline.
  • Top contribution tables.
  • Evidence appendix with PR/commit URLs.

Important Constraints

• SSH key alone is for git transport and does not directly authorize GitHub REST API calls.
• Private repository coverage needs authenticated API access, typically via GITHUB_TOKEN/GH_TOKEN or gh auth token.
• GitHub Search API has a 1000-item cap per query. For very active users, split the time range and run multiple reports.

References

• references/env.md
• references/data-model.md
• references/markdown-template.md

Scripts

• scripts/github_contribution_report.py