memesh-review

Imported from PCIRCLE-AI/memesh (skills/memesh-review/SKILL.md). Install upstream with npx skills add PCIRCLE-AI/memesh --skill memesh-review. Copyright stays with the author.

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

# Get system health
memesh status

# Get all recent memories (structured output for analysis)
memesh recall --limit 50 --json

# `--tag` filters actual tags; inspect each returned entity's `type` to group decisions, lessons, and session records.

Recall is a bounded window, not a whole-database count. For an exact health score and factor totals, use the Dashboard Analytics view after memesh serve or its local GET /v1/analytics endpoint. If it is unavailable, omit the score and label any counts from recall as sample counts.

If MCP user_patterns tool is available, also run it for work pattern analysis:

user_patterns: {}

Step 2: Analyze and report

Use the analytics result for graph-wide totals and health factors; use recalled memories to illustrate findings. Present only fields you actually obtained:

## 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/30 points (active entities accessed in the last 30 days / all active entities)
- Quality: N/30 points (active entities with confidence > 0.7 / all active entities)
- Freshness: N/20 points (entities created in the last 7 days / all active entities, capped at 1)
- Lessons: N/20 points (`lesson_learned` entity count / 5, capped at 1)

### 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 `work_package` for human review.

**Potential conflicts**
- "entity-A" vs "entity-B" — inspect the text and recommend an explicit
  `contradicts` or `supersedes` relation; MeMesh does not judge it automatically

**Noise ratio**
- N% auto-tracked (session_keypoint, commit) vs N% intentional knowledge
- If noise > 80%: recommend more deliberate `memesh remember` usage

### Recommended Actions
1. `memesh forget --name "old-design"` (superseded)
2. Ask the current agent to prepare one MCP `work_package`; then review the staged proposal
3. `memesh remember ...` (knowledge gap in [area])

Step 3: Execute approved actions

Present the report first. Ask which actions to execute. Then run the commands:

memesh forget --name "outdated-entity"
# MCP work_package submits one proposal; then use dream show / accept / reject
memesh remember --name "missing-knowledge" --type decision --obs "..."

Step 4: Verify

memesh recall --limit 5 --json    # confirm changes took effect

Tips

• Run every 1-2 weeks to keep memory healthy
• Health score < 50 → inspect the four factor scores before recommending a cause
• Noise > 80% → encourage deliberate memesh remember for decisions
• Dashboard available at: http://localhost:3737/dashboard (run memesh serve first)