/memesh-review

name: memesh-review
description: Review and optimize the MeMesh memory database. Analyzes health score, finds stale or noisy memories, shows work patterns, and suggests human-reviewed 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. 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 `memesh forget --name "old-design"` (superseded)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

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)
node scripts/check-doc-claims.mjs
# 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