proactive-self-improving-agent

To make an AI agent learn from mistakes automatically, this Skill captures errors, corrections, and new insights during operation, logging them into structured files within your workspace for continuous self-improvement.

An autonomous agent structures its knowledge base by organizing operational experiences into distinct files for errors, improvements, and feature requests, with mechanisms for promoting valuable learnings into permanent knowledge or new skills.

Safe AI evolution is maintained by implementing guardrails that prevent undesirable changes and adhere to safety and value-first principles, ensuring the agent prioritizes valuable knowledge promotion without risky autonomous modifications.

No specific dependencies are required to enable agent error handling and knowledge capture. You only need to ensure the `.learnings/` directory exists in your workspace before the agent begins logging its operational experiences.

An agent updates its internal knowledge by capturing user feedback during tasks like processing research papers, automatically adjusting its categorization or search strategies and logging these new insights into structured files.

You should not use an autonomous self-improving agent if your workflow lacks clear safety and value-first principles, as the agent relies on these guardrails to prevent undesirable changes while promoting new permanent knowledge.