fork-skill-digest

Compare fork aeon.yml configurations against upstream AEON skill defaults.

6|2|Updated May 21, 2026
One-click install
npx skills add https://github.com/anajuliabit/aeon --skill fork-skill-digest
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: fork-skill-digest
Source: https://github.com/anajuliabit/aeon/tree/main/skills/fork-skill-digest
Command: npx skills add https://github.com/anajuliabit/aeon --skill fork-skill-digest

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill identifies where your configured fork fleet systematically differs from upstream defaults, so you can learn from collective operator choices and reduce configuration drift.

Core Features & Use Cases

  • Weekly divergence digest across forks: Scans active forks of a target repo and compares each fork’s aeon.yml against the running instance’s upstream skill defaults.
  • Default-change recommendations (aggregate): Flags skills likely worth flipping upstream based on how often forks diverge (enable upward / disable downward), plus consensus for model/var/schedule.
  • Actionable fleet signals: Produces a verdict, tiered bucket results (flip candidates, consensus, hotspots, emerging watchlist), and a list of heaviest customizers and fork-only experiments.
  • Week-over-week change tracking: Persists a structured snapshot in memory/topics/fork-skill-digest-state.json and computes deltas like new flips and new fork-only skills.

Quick Start

Run the skill with an optional target repo override by setting var to a GitHub repo like "owner/aeon" to generate the weekly divergence digest for that fork fleet.

Frequently Asked Questions about fork-skill-digest

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I detect configuration drift across GitHub forks?

Detect configuration drift by parsing each fork's YAML configuration file and comparing it against a snapshotted upstream baseline to compute systematic divergence buckets and operator consensus.

What is fleet divergence analysis for YAML configurations?

Fleet divergence analysis scans active repository forks to identify where enabled, var, model, and schedule settings systematically differ from upstream defaults, producing actionable alignment recommendations.

How do I track week-over-week changes in fork configurations?

Track week-over-week fork configuration changes by persisting a structured JSON snapshot of divergence states and computing deltas like new default flips and newly detected fork-only skills on a weekly audit schedule.

Can I automate a weekly audit of forked repository configurations?

Yes, you can automate a weekly audit of forked repository configurations by resolving a target repo, fetching active forks via the GitHub API, enumerating file trees, and safely parsing YAML to generate a recurring digest.

What's the best way to find model consensus across active forks?

Find model consensus across active forks by aggregating and parsing YAML configurations from each forked repository, then computing tiered bucket results to identify which model settings the majority of operators adopt.

Do I need GitHub API access to analyze fork divergence?

Yes, you need GitHub API access to analyze fork divergence, as the process requires fetching active forks, enumerating fork file trees, and resolving the target repository to extract and compare YAML configurations.