Self-Improve

Performance audit automation analyzes memory health, thoughts, and strategy logs to identify weaknesses with the largest potential impact during build-mode, selecting one concrete improvement per cycle.

Autonomous performance improvement works by reading memory health, thoughts, and strategy logs to identify the highest-impact weakness, applying one concrete change, and recording results to ensure traceability and reversibility.

An autonomous performance audit requires JSON and JSONL files from memory directories including skill-health data, thoughts logs, performance metrics, strategy files, cron-state data, and improvement history to determine changes.

Yes, build-mode optimization cycles ensure performance changes are reversible by committing each implemented update to a historical improvement log, making every modification traceable and accountable across runs.

The best way to track self-improvement changes is by appending applied updates to an improvement log file, creating a traceable history of performance modifications made during each build-mode optimization cycle.

Implementing one performance improvement per run ensures focused, high-impact changes during build-mode optimization, preventing conflicting modifications and maintaining stability while accelerating self-improvement cycles.