memesh-review

name	memesh-review
description	Review and optimize the MeMesh memory database. Analyzes health score, finds stale/conflicting/redundant memories, shows work patterns, and suggests cleanup actions. Use when asked to "review memories", "check memory health", "clean up knowledge", or "what's in my memory".
user-invocable	true

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

# 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:

user_patterns: {}

Step 2: Analyze and report

From the recalled data, compute and present:

## 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 any more. If the noise is spread across many episodic entries,
  suggest `memesh dream run` instead.

**Potential conflicts**
- "entity-A" vs "entity-B" — contradicting decisions

**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 `memesh forget --name "old-design"` (superseded)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. `memesh dream run --project myapp` (propose digests for the noisy clusters, then review)
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memesh forget --name "outdated-entity"
memesh dream run                  # then: memesh dream list / accept <id> / reject <id>
memesh remember --name "missing-knowledge" --type decision --obs "..."

Step 4: Verify

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

Documentation & Code Quality Checks

For comprehensive documentation synchronization and lint checks, use:

@sa:comprehensive-code-review

This skill includes:

• Dim 17: Documentation Synchronization — version consistency, API docs, architecture docs, feature docs, deprecated terms, breaking changes
• Dim 18: Code Style & Lint — lint errors/warnings, security rules, disabled rules, style consistency

MeMesh-specific automation:

# Quick verification (memesh-specific checks)
./scripts/verify-docs-sync.sh
# Exit code 0 = all checks pass

# Lint check
npm run lint  # 0 errors expected, ~83 warnings (technical debt)

---

Tips

• Run every 1-2 weeks to keep memory healthy
• Health score < 50 → too many stale or low-quality memories
• Noise > 80% → encourage deliberate memesh remember for decisions
• Dashboard available at: http://localhost:3737/dashboard (run memesh serve first)
• Before merge: Run documentation sync checklist above to prevent drift