Nottmemory Review

Note (public nott plugin): task()/doc()/status calls below map to local .nott/memory/ files when SSOT MCP is not registered. See docs/MEMORY-PROTOCOL.md for the field-by-field mapping.

/nott:memory-review — memory telemetry dashboard

context

When to use:

• Validate hit rate before tuning memory-trigger keyword patterns
• Spot ignored memories (injected but never touched) — candidates for archive/supersede
• Diagnose latency regressions — separate Python+RPC overhead from DB time
• Gather evidence for Phase 2 followups (memory-pgvector-phase2, memory-fsrs6-pluggable, memory-stdio-transport)
• Periodic dogfood self-check

When NOT to use:

• Reading a single memory → memory(action="search", id=...) directly
• Mutating memory state — this skill is read-only by design
• Cross-guild aggregation — scope is current guild only

Anti-patterns:

1. Treating output as ground truth without checking sample sizes (samples<10 = noise)
2. Comparing latency without normalizing windows (default 7d vs --last all)
3. Drawing FSRS-6 / pgvector conclusions from <100 search samples

role

Read-only aggregator over audit_log (memory.*.timing entries) + memories table. Never writes. Two data sources surfaced separately so overhead is visible:

hook_ms − db_ms = overhead. If sustained >50ms p50 → open task memory-rpc-overhead-investigate.

task

1. Parse args (all optional):

   • --last 7d|30d|all|<seconds> (default 7d)
   • --scope project|user|feedback|reference|<custom>|all (default all)
   • --top N (default 10, max 100)

2. Call the review action:

   mcp__ssot__memory(
       action="review",
       last=<parsed>,
       scope=<parsed_or_omitted>,
       top=<parsed>
   )
   

   Returns shape:

   {
     "window_secs": 604800,
     "cutoff_epoch": 1747094400,
     "project_id": "...",
     "scope": null,
     "totals": { "total": N, "active": N, "archived": N, "pinned": N },
     "latency": [ { "action": "memory.search.timing", "samples": N, "p50_ms": ..., "p95_ms": ..., "p99_ms": ..., "min_ms": ..., "max_ms": ... }, ... ],
     "hit_rate": { "total_searches": N, "hits": N, "misses": N, "hit_rate": 0.XX },
     "top_queries": [ { "query": "...", "count": N, "hits": N, "misses": N }, ... ],
     "top_injected": [ { "id": "mem-...", "scope": "...", "insight": "...", "last_used": epoch, "strength": F, "retention": F, "tier": "..." }, ... ],
     "top_ignored": [ /* same shape as top_injected */ ],
     "scope_breakdown": [ { "scope": "...", "total": N, "active": N, "essential": N, "active_tier": N, "dormant": N, "archive_candidate": N }, ... ],
     "latency_ms": N
   }
   

3. Read .nott/hooks/audit/hooks.jsonl (last 10k lines tail) and aggregate memory-trigger / memory-inject / memory-touch entries by hook. Compute hook_ms p50/p95/p99 per hook within the same window. Tail with:

   tail -10000 .nott/hooks/audit/hooks.jsonl 2>/dev/null | jq -c 'select(.hook | startswith("memory-"))'
   

   Process those entries inside mcp__plugin_context-mode_context-mode__ctx_execute (don't pull raw lines into context). Filter to ts >= cutoff_epoch.

4. Format a Markdown report. Sections (in this order):

   Overview

   • Window: last 7d (or whatever was parsed)
   • Totals: total / active / archived / pinned
   • Scope filter (if any)

   Latency

   Table with columns: action | samples | db p50 | db p95 | db p99 | hook p50 | hook p95 | hook p99. Add a footnote line: overhead p50 = hook_p50 - db_p50. Flag rows where samples < 10 with ⚠ low sample.

   Hit rate

   • Total searches in window
   • Hits / Misses / Hit-rate %
   • Flag if hit_rate < 0.5 AND total_searches >= 50 → "Phase 2 trigger candidate (memory-fsrs6-pluggable evidence)"

   Top queries

   Table: query | count | hits | misses. Truncate query to 80 chars.

   Top injected (high-value memories)

   Table: id (8 chars) | scope | tier | retention | insight (60 chars).

   Top ignored (never touched in window)

   Table: same shape as injected, sorted by last_used ascending. Suggest action: "Candidates for memory(action='archive') or memory(action='supersede')."

   Scope breakdown

   Table: scope | total | active | essential | active_tier | dormant | archive_candidate.

5. End with a 1-line EVIDENCE GATES summary based on observed metrics:

   • pgvector_phase2: ✅ ready / ⏸ insufficient (need corpus >1k, current = N)
   • fsrs6_pluggable: ✅ ready / ⏸ insufficient (need hit_rate <50% over >100 searches, current = X% over Y)
   • stdio_transport: ✅ ready / ⏸ insufficient (need p99 hook_ms >100ms over >100 samples, current = Xms)

   Each gate's "ready" emits a follow-on suggestion: task(action="update", id="<task_id>", status="doing") for the corresponding SSOT followup.

output

A Markdown report rendered as the final message. Never write the report to disk (no side effects). Never call mcp__ssot__memory with any action other than review. If the user asks to fix something based on the report, present concrete commands they (or a follow-up agent) can run — don't execute them from this skill.

Source: menot-you/claude — distributed by TomeVault.