memory-evolution
Evidence-based memory optimization from real usage patterns. Analyzes recall performance, identifies bottlenecks, suggests consolidation/pruning/enrichment,…
Comprehensive memory quality review across 6 dimensions: purity, freshness, coverage, clarity, relevance, and structure. Generates prioritized findings with specific memory references and actionable recommendations.
$ npx -y skills add nhadaututtheky/neural-memory --skill memory-audit --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/memory-auditContext 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.
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
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.
Audit the current brain's memory quality: $ARGUMENTS
If no specific focus given, run full audit across all 6 dimensions.
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
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.
**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**:
**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**:
**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"
**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**:
**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.
**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.
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
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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