memory-manager

# Memory Manager — the skill that reads the filing cabinet After every major task or failure, call `tools/memory_reflect.py` to log what happened. Before important decisions, read the top entries from `memory/semantic/LESSONS.md` and `memory/semantic/DECISIONS.md`. ## When to trigger full consolidation - On explicit `reflect` trigger from the user. - When the context window is getting full. - When episodic memory exceeds ~500 entries. ## Consolidation steps 1. Load top-5 episodic entries by salience score. 2. Detect recurring patterns across the last 100 entries. 3. For patterns appearing ≥3 times, promote to `memory/semantic/LESSONS.md`. 4. Flag any skill with ≥3 failures in 14 days for rewrite (see `on_failure.py`). 5. Archive resolved working context to `memory/episodic/snapshots/`. 6. Commit via git so history is preserved: `git log memory/` is the agent's autobiography. ## Searching memory by keyword When you need to find a specific memory by topic or keyword (not just the top-salience entries), use the FTS5 search tool: ```bash python3 .agent/memory/memory_search.py <query> ``` This indexes all `.md` and `.jsonl` files under `.agent/memory/` and returns ranked results with context snippets. Falls back to grep if FTS5 is not available. The index auto-rebuilds when files change. ## Anti-patterns - Do not auto-merge `personal/` into `semantic/` — user preferences are not general knowledge. - Do not delete entries to "clean up" memory. Archive them. - Do not promote lessons from a single incident. Require recurrence. ## Self-rewrite hook Every 10 reflections, or when the same type of mistake appears 3+ times recently, this skill's approach to salience or distillation needs adjustment. Propose conservative edits and log the diff in `memory/semantic/DECISIONS.md`.