capability-evolver

Automating self-improvement for AI agents involves analyzing runtime memory, logs, and signals to identify failures and autonomously propose updates. This Skill evaluates historical events offline to generate auditable artifacts that enhance agent efficiency.

Auditable evolution assets are traceable artifacts like Genes, Capsules, and EvolutionEvents generated during AI self-improvement. They store proposed changes and updates locally, ensuring safety and traceability when modifying agent behavior based on runtime analysis.

Starting an offline AI evolution loop requires running the command `node index.js --loop`. This initiates the engine to analyze local memory and signals, identify improvement opportunities, and write validated changes to local stores without requiring network connectivity.

AI agent self-improvement does not require a constant network connection as the engine operates offline by analyzing local memory and events. It optionally syncs with EvoMap Hub via a local Proxy mailbox, but offline operation remains fully functional.

Validated changes during autonomous AI agent updates are enforced through safety mechanisms including validation checks, sandboxed commands, and auditable artifact generation. This ensures that proposed Gene and Capsule updates from runtime analysis are safely applied.