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Found 5 Skills
Graduate a proven pattern from auto-memory (MEMORY.md) to CLAUDE.md or .claude/rules/ for permanent enforcement.
Patterns for building AI agents that learn from their own execution, detect failure modes, and improve autonomously. Covers feedback loops, performance regression detection, memory curation, skill extraction, and meta-learning architectures. Use when building agents that need to get better over time, managing auto-memory, or designing self-correcting systems.
Audit, restructure, and maintain the full Claude Code memory hierarchy: CLAUDE.md files, .claude/rules/ topic files, auto-memory, and project documentation. Detects project type and suggests appropriate docs. Use when CLAUDE.md needs updating, memory needs restructuring, or a project needs its docs audited. Trigger with 'audit memory', 'update CLAUDE.md', 'restructure memory', 'session capture', 'memory cleanup', 'check project docs', or 'what docs does this project need'.
Explicitly save important knowledge to auto-memory with timestamp and context. Use when a discovery is too important to rely on auto-capture.
Capture and persist lessons learned from a session to compound knowledge over time. Triggers on "/lessons-learned", "what did we learn", "save lessons", "update skills with what we learned", or at the end of a complex multi-session task. PROACTIVE USE: This skill should also be suggested or invoked (1) when resuming from context compaction (the previous context likely contained unrecorded lessons), (2) after resolving a non-trivial bug or debugging session, (3) after significant friction or failed approaches that yielded insight, (4) after a council-of-bots review that surfaced fixes. Identifies reusable patterns, bug fixes, workflow insights, and tool quirks, then persists them to the right places: auto-memory (project-specific), skill files (reusable across projects), or both.