Diagnose AI agent faults and apply verifiable patches with safety gates.
npx skills add https://github.com/Kingxiao/agentforge --skill agentforge-evolution
Please help me install this Agent Skill. Skill: agentforge-evolution Source: https://github.com/Kingxiao/agentforge/tree/main/agentforge-evolution Command: npx skills add https://github.com/Kingxiao/agentforge --skill agentforge-evolution
Diagnoses faults in AI agents and applies safe, verifiable self-improvement patches.
Run a diagnosis on the agent’s current metrics, generate a safe patch, apply it, verify with tests, and review the Evolution Log.
High-intent search queries and answers about installing and using this skill.
To safely apply self-improvement patches to AI agents, diagnose faults against defined metrics, generate a patch, apply it with safety guards like a circuit breaker, verify with test gates, and review the evolution log.
A circuit breaker mechanism for AI agent self-evolution halts patch application automatically if blast-radius checks or test gates detect critical faults, ensuring changes remain traceable and reversible through evolution logging.
The best way to trace and rollback AI agent evolution changes is by maintaining an Evolution Log that records every patch application, enabling verifiable reversals when safety gates or test verifications fail.
Yes, you can use automated patch management for AI agents in Platform-Harness environments across the full lifecycle from specification to deployment, utilizing blast-radius checks and evolution logs for safe changes.
Relying on self-diagnosis for AI agent patch generation requires defined performance metrics to detect faults accurately; without strict test gates and blast-radius checks, generated patches risk unverified runtime failures.
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