ai-self-evolution

To prevent repeating development mistakes across sessions, you capture command failures and user corrections into structured `.learnings/` markdown logs, enabling continuous improvement and pattern detection for future agent workflows.

A continuous improvement loop for agent workflows is a process that captures development experience from errors and knowledge gaps, runs session start recaps and end summaries, and hardens recurring patterns for reuse in interactive coding.

You log development errors and knowledge gaps into markdown files by writing learning and error entries to the `.learnings/` directory, using consistent IDs, priorities, and statuses to support archival and pattern promotion.

Yes, this error logging approach works for interactive coding sessions by applying learning loops where failures occur, fixes need tracking, and recurring patterns should be hardened for reuse across projects.

You detect recurring patterns from tooling failures and API errors by logging triggers when tools fail, tracking recurrence counts using a `Pattern-Key`, and guiding promotion or skill extraction for repeated issues.

The best way to organize project learnings without external dependencies is maintaining structured markdown logs in the `.learnings/` directory with automatic session start scans and session end summaries to mark resolutions.