memesh-reviewlisted

# MeMesh Memory Review Review the memory database and provide actionable cleanup recommendations. ## How to Access Use CLI (works everywhere) or MCP tools (if available). See the `memesh` skill for auto-detect instructions. ## Process ### Step 1: Gather data ```bash # Get system health memesh status # Get all recent memories (structured output for analysis) memesh recall --limit 50 --json # Get memories by type for quality analysis memesh recall --tag "type:decision" --json memesh recall --tag "type:lesson_learned" --json memesh recall --tag "type:session_keypoint" --json ``` If MCP `user_patterns` tool is available, also run it for work pattern analysis: ```json user_patterns: {} ``` ### Step 2: Analyze and report From the recalled data, compute and present: ```markdown ## Memory Health Report ### Overview - Total entities: N - Last 30 days active: N (N%) - Knowledge types: N decisions, N patterns, N lessons, N auto-tracked ### Health Score: N/100 - Activity: N% (accessed in last 30 days) - Quality: N% (high confidence, well-tagged) - Freshness: N% (new this week) - Self-Improvement: N% (lessons learned ratio) ### Quality Issues Found **Stale (not accessed 30+ days, low confidence)** - "entity-name" — confidence: N% — Suggest: archive? **Verbose (5+ observations)** - "entity-name" (N observations) — note it; there is no one-entity compression command. If useful knowledge is spread across episodic entries, an already running agent can prepare one MCP `wor