fork-fleet

Tracking active GitHub forks involves comparing per-fork deltas against the upstream repository to surface diverged work. This Skill identifies new or modified skill implementations ready for upstream submission, generating a weekly digest of recommended PR targets and risk signals for maintainers.

Detecting fork divergence requires automated per-fork comparison against upstream to summarize ahead and behind status. The Skill compares active forks weekly, scoring delta signals like new or modified skills, and optionally deep-reads selected skills to evaluate divergence.

Yes, you can generate a weekly digest of critical changes and risk signals for your fork network. The Skill highlights diverged work, recommended PR targets, and actionable upstream contribution opportunities, providing maintainers and contributors with automated reporting on a weekly cadence.

Fork scoring works by evaluating per-fork delta signals, such as new or modified skills and content changes, against the upstream baseline. The Skill applies automated comparison logic to these diverged metrics, producing a score that helps prioritize which forks contain ready upstream contribution candidates.

No dependencies are required to automate upstream contribution monitoring for Aeon forks. The Skill operates independently to inventory forks and surface upstream contributors, applying weekly cadence tracking and per-fork comparison across managed and community repositories without external component requirements.

Automated fork inventorying should be used when tracking active Aeon forks across managed and community repositories on a weekly cadence. Manual comparison lacks the scale for per-fork delta signals, deep-reads of selected skills, and automated risk signal reporting needed to surface actionable upstream contribution opportunities effectively.