capability-evolver

Autonomously evolving AI agents involves analyzing runtime history for failures and applying protocol-constrained patches to memory and behavior. This Skill triggers an automated evolution cycle to apply deterministic, trackable upgrades via a local GEP asset store.

The GEP protocol enables auditable AI evolution using a local asset store containing genes, capsules, and evolution events. It ensures deterministic upgrades by satisfying strict validation, solidification, and safety loops for bounded improvements.

Triggering an automated self-repair cycle requires running a single command such as /evolve or node index.js. This initiates the process of scanning runtime history, detecting crashes, and patching memory or code to improve resilience.

The AI self-improvement process requires no external dependencies. It is environment-agnostic and supports local skill injections and environment overrides to apply protocol-constrained patches using a local GEP asset store.

The limitations of autonomous AI agent evolution are bounded by strict validation, solidification, and safety loops. These constraints ensure trackable improvements and prevent uncontrolled behavioral changes during the patch application process.

You can use environment overrides and local skill injections during AI evolution. The system is environment-agnostic, applying protocol-constrained patches from a local GEP asset store to ensure deterministic upgrades across different setups.