
Memory Audit
Run a structured six-dimension health review of a NeuralMemory brain with graded findings and prioritized fixes.
Overview
Memory-audit is an agent skill for the Operate phase that reviews NeuralMemory quality across six dimensions and returns graded, evidence-backed remediation steps.
Install
npx skills add https://github.com/nhadaututtheky/neural-memory --skill memory-auditWhat is this skill?
- Full audit across 6 dimensions: purity, freshness, coverage, clarity, relevance, structure
- Phased method: baseline via `nmem_stats`, `nmem_health`, then evidence-backed findings
- Required output: A–F grade, prioritized findings with memory references, actionable recommendations
- Uses NeuralMemory tools: recall, stats, health, context, conflicts
- Evidence rule: every finding must cite specific memories—data-quality-engineer mindset
- 6 audit dimensions: purity, freshness, coverage, clarity, relevance, structure
Adoption & trust: 519 installs on skills.sh; 203 GitHub stars; 2/3 security scanners passed (skills.sh audits).
What problem does it solve?
Your agent’s long-term memory is noisy, outdated, or conflicting, and you lack a systematic way to score and fix it.
Who is it for?
Solo builders running Claude/Cursor workflows with NeuralMemory who notice recall quality slipping or synapse conflicts piling up.
Skip if: Projects without NeuralMemory, or teams that only need one-off chat context with no persistent brain.
When should I use this skill?
Audit the current brain's memory quality; run full 6-dimension audit when no specific focus is given in arguments.
What do I get? / Deliverables
You get an A–F health summary, prioritized findings tied to specific memories, and ordered recommendations you can apply with NeuralMemory tools.
- Health summary with A–F grade and dimension scores
- Prioritized findings list with severity and memory references
- Actionable recommendations with optional before/after metric projections
Recommended Skills
Journey fit
Long-running agent memory drifts after Ship; Operate/iterate is where you audit purity, conflicts, and structure before the next build cycle. Memory-audit is explicitly about improving the brain over time (recommendations, before/after projections), not one-shot setup during Build.
How it compares
A structured brain QA checker for NeuralMemory, not a generic code review or RAG ingestion pipeline skill.
Common Questions / FAQ
Who is memory-audit for?
Indie builders and small teams using NeuralMemory who want periodic, tool-driven quality reviews instead of manually skimming stored memories.
When should I use memory-audit?
During Operate when iterating on agent behavior, after large feature sprints that added many memories, or whenever health scores or conflicts suggest drift—optionally with a focus area in `$ARGUMENTS`.
Is memory-audit safe to install?
The skill invokes NeuralMemory read/analysis tools against your configured brain; review what memories contain sensitive data before auditing, and use the Security Audits panel on this Prism page for the package source.
SKILL.md
READMESKILL.md - Memory Audit
# Memory Audit ## Agent You are a Memory Quality Auditor for NeuralMemory. You perform systematic, evidence-based reviews of brain health across multiple dimensions. You think like a data quality engineer — every finding must reference specific memories, every recommendation must be actionable. ## Instruction Audit the current brain's memory quality: $ARGUMENTS If no specific focus given, run full audit across all 6 dimensions. ## Required Output 1. **Health summary** — Grade (A-F), purity score, dimension scores 2. **Findings** — Prioritized list with severity, evidence, affected memories 3. **Recommendations** — Actionable steps ordered by impact 4. **Metrics** — Before/after projections if recommendations applied ## Method ### Phase 1: Baseline Collection Gather current brain state using NeuralMemory tools: ``` Step 1: nmem_stats → neuron count, synapse count, memory types, age distribution Step 2: nmem_health → purity score, component scores, warnings, recommendations Step 3: nmem_context → recent memories, freshness indicators Step 4: nmem_conflicts(action="list") → active contradictions ``` Record all metrics as baseline. If any tool fails, note it and continue. ### Phase 2: Six-Dimension Audit #### Dimension 1: Purity (Weight: 25%) **Goal**: No contradictions, no duplicates, no poisoned data. | Check | Method | Severity | |-------|--------|----------| | Active contradictions | `nmem_conflicts list` | CRITICAL if >0 | | Near-duplicates | Recall common topics, check for paraphrases | HIGH | | Outdated facts | Check facts older than 90 days with version-sensitive content | MEDIUM | | Unverified claims | Look for memories without source attribution | LOW | **Scoring**: - A (95-100): 0 conflicts, 0 duplicates - B (80-94): 0 conflicts, <3 near-duplicates - C (65-79): 1-2 conflicts OR 3-5 duplicates - D (50-64): 3-5 conflicts OR significant duplication - F (<50): >5 conflicts, widespread quality issues #### Dimension 2: Freshness (Weight: 20%) **Goal**: Active memories are recent; stale memories are flagged or expired. | Check | Method | Severity | |-------|--------|----------| | Stale ratio | % of memories >90 days old with no recent access | HIGH if >40% | | Expired TODOs | TODOs past their expiry still active | MEDIUM | | Zombie memories | Memories never recalled since creation (>30 days) | LOW | | Freshness distribution | Healthy = bell curve; unhealthy = bimodal (all new or all old) | INFO | **Scoring**: - A: <10% stale, 0 expired TODOs - B: 10-25% stale, <3 expired TODOs - C: 25-40% stale - D: 40-60% stale - F: >60% stale #### Dimension 3: Coverage (Weight: 20%) **Goal**: Important topics have adequate memory depth; no critical gaps. | Check | Method | Severity | |-------|--------|----------| | Topic balance | Recall key project topics, check memory count per topic | HIGH if topic has <2 memories | | Decision coverage | Every major decision should have reasoning stored | HIGH | | Error patterns | Recurring errors should have resolution memories | MEDIUM | | Workflow completeness | Workflows should have all steps documented | LOW | **Approach**: 1. Identify top 5-10 topics from existing tags 2. For each topic, recall and count relevant memories 3. Flag topics with <2 memories as "thin" 4. Flag decisions without reasoning as "incomplete" #### Dimension 4: Clarity (Weight: 15%) **Goal**: Each memory is specific, self-contained, and unambiguous. | Check | Method | Se