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/memory-audit

Comprehensive memory quality review across 6 dimensions: purity, freshness, coverage, clarity, relevance, and structure. Generates prioritized findings with specific memory references and actionable recommendations.

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neural-memory
2353 skills
Install
$ npx -y skills add nhadaututtheky/neural-memory --skill memory-audit --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/memory-audit

Context preview

The summary Claude sees to decide when to auto-load this skill.

Comprehensive memory quality review across 6 dimensions: purity, freshness, coverage, clarity, relevance, and structure. Generates prioritized findings with specific memory references and actionable recommendations.

SKILL.md

memory-audit.SKILL.md
name: memory-audit
description: |
  Comprehensive memory quality review across 6 dimensions: purity, freshness,
  coverage, clarity, relevance, and structure. Generates prioritized findings
  with specific memory references and actionable recommendations.
metadata:
  stage: review
  tags: [memory, audit, quality, health, neuralmemory]
context:
  - "~/.neuralmemory/config.toml"
agent: Memory Quality Auditor
allowed-tools:
  - nmem_recall
  - nmem_stats
  - nmem_health
  - nmem_context
  - nmem_conflicts

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 | Severity | |-------|--------|----------| | Vague memories | Content like "fixed the thing", "updated config" | HIGH | | Missing context | Decisions without reasoning, errors without resolution | MEDIUM | | Overstuffed memories | Single memory covering 3+ distinct concepts | MEDIUM | | Acronym soup | Unexpanded abbreviations without context | LOW |

**Heuristics**:

  • Vague: content <20 characters, or lacks specific nouns/verbs
  • Missing context: `decision` type without "because", "reason", "due to"
  • Overstuffed: content >500 characters with 3+ distinct topics

Dimension 5: Relevance (Weight: 10%)

**Goal**: Memories match current project/user context.

| Check | Method | Severity | |-------|--------|----------| | Orphaned project refs | Memories about projects no longer active | MEDIUM | | Technology drift | Memories about deprecated tech still active | MEDIUM | | Context mismatch | Memories tagged for wrong project/domain | LOW |

**Approach**: Cross-reference memory tags with current `nmem_context` output.

Dimension 6: Structure (Weight: 10%)

**Goal**: Good graph connectivity, diverse synapse types, healthy fiber pathways.

| Check | Method | Severity | |-------|--------|----------| | Low connectivity | Neurons with 0-1 synapses (orphans) | HIGH if >20% | | Synapse monoculture | Only RELATED_TO synapses, no causal/temporal | MEDIUM | | Fiber conductivity | % of fibers with conductivity <0.1 (nearly dead) | LOW | | Tag drift | Same concept stored under different tags | MEDIUM |

**Data source**: `nmem_health` provides connectivity, diversity, orphan_rate.

Phase 3: Severity Triage

Classify all findings:

| Severity | Criteria | Action | |----------|----------|--------| | **CRITICAL** | Active contradictions, security-sensitive errors | Fix immediately | | **HIGH** | Significant gaps, widespread staleness, vague decisions | Fix this session | | **MEDIUM** | Moderate quality issues, some duplicates | Fix within 1 week | | **LOW** | Cosmetic, minor opti

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Ships withneural-memory

Your AI agent forgets everything between sessions. Neural Memory gives it a brain. Website · Quickstart · MCP Tools · Pro · Changelog Memories are stored as interconnected neurons and recalled through spreading activation — the same way the human brain works.

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Repo: nhadaututtheky/neural-memory