self-improving-agent-v2

To stop an agent from repeating mistakes, you need a persistent learning mechanism that records failures and corrections. This skill captures past errors and discoveries in a local `.learnings/` journal, automatically retrieving them to inform future solutions and prevent duplicate errors.

Agent reflection is the process of reviewing past actions to identify optimizations and failures. This skill implements reflection by storing high-signal learnings after tasks are completed, allowing the agent to apply these insights to future automation workflows and continuously improve performance.

You can persist agent memory locally by writing learnings to a private `.learnings/` directory. This skill enforces strict local storage with explicit safety rules, ensuring that recorded failures and optimizations remain private and preventing any leakage of sensitive data.

Building a self-improving agent requires recording discoveries and failures after they occur. This skill automatically consults past learnings before solving similar tasks, applying previous optimizations to reduce repeat errors and enabling the assistant to improve over time.

Yes, this agent memory skill operates without external dependencies. It uses strictly local storage to maintain the learning notebook, requiring no additional frameworks or APIs to record failures, retrieve past insights, and apply safety rules against data leakage.

You should use a local learning journal when your assistant frequently misses opportunities to improve or repeats errors. It is especially valuable after corrections, failed tasks, or when a better approach emerges, allowing you to capture and reuse high-signal optimizations.