What problem does it solve?
It prevents misleading popularity signals by measuring which Aeon skills are actually running across configured forks rather than counting a default always-on skill.
Core Features & Use Cases
- Configured-fleet leaderboard: Scores skill adoption only among forks whose
aeon.yml diverges from upstream defaults, so results reflect meaningful operator customization.
- Upstream actionable recommendations: Produces Promote, Match, and Sunset candidate lists based on adoption thresholds, model overrides, and zero-adoption signals.
- Fleet intelligence with guardrails: Handles GitHub API limitations and repo visibility issues, and suppresses notifications when the configured denominator is too small to be useful.
Use Case: Operators of a framework with many downstream forks can identify which skills the community truly runs, which ones deserve better upstream defaults, and which ones should be deprecated or improved—without manually auditing each fork.
Quick Start
Generate a weekly report by scanning configured Aeon forks of the target repository you provide (or the first watched repo), then write the ranked leaderboard article and notification.
