fork-intelligence_skill

Overview

This skill discovers valuable, non-obvious work in GitHub fork ecosystems by looking beyond stars. It combines branch-level divergence analysis, upstream PR cross-referencing, tags/releases, timestamp clustering, and domain heuristics to surface forks worth attention. The output is a tiered, actionable report with cherry-pick candidates and maintenance signals.

How this skill works

Start by taking an upstream baseline (stars, forks, default branch, last push) and listing forks with pagination. Filter out bulk mirrors using timestamp clustering, then compare default and non-default branches to detect ahead/behind/diverged status. Enrich findings with commit content checks, tag/release detection, open-issue counts, repo rename detection, and upstream PR cross-references to recover force-pushed or rebased work. Rank forks into tiers and produce a concise recommendations report.

When to use it

• You want to find feature work that stars-only filters miss.
• Triage a new repository to identify maintenance or feature candidates quickly.
• Evaluate a specific fork for cherry-pick or upstream merge readiness.
• Prioritize work for backporting, packaging, or platform enablement.
• Detect cross-fork convergence indicating unmet upstream demand.

Best practices

• Always load the appropriate template (Full Analysis, Quick Scan, or Targeted Evaluation) before running analysis.
• Use timestamp clustering to remove mirror noise — it typically eliminates 85%+ of forks.
• Check non-default branches for any fork with recent pushes or multiple branches — many substantive changes live there.
• Cross-reference upstream PR history to recover force-pushed or rebased contributions hidden from compare APIs.
• Prioritize signals in this order: branch divergence, upstream PRs, tags/releases, commit email domains, then stars.

Example use cases

• 5‑minute triage of a new open-source repo to produce a short list of divergent forks.
• Deep analysis to find Tier 1 major extensions suitable for cherry-pick into upstream.
• Targeted evaluation of a single fork to produce a patch/merge plan and list of relevant commits.
• Identify infrastructure and packaging work (CI/CD, Cargo.toml, CMakeLists) useful for releases and distribution.
• Detect multiple forks solving the same problem to inform product decisions or feature prioritization.