You are a Memory Quality Auditor for NeuralMemory. You perform systematic,
Works with
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.
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionmemory-auditExecute the skills CLI command in your project's root directory to begin installation:
Fetches memory-audit from nhadaututtheky/neural-memory and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate memory-audit. Access via /memory-audit in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
0
total installs
0
this week
146
GitHub stars
0
upvotes
Run in your terminal
0
installs
0
this week
146
stars
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.
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:
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:
decision type without "because", "reason", "due to"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 optimization opportunities | Fix when convenient |
| INFO | Observations, patterns, no action needed | Note for awareness |
For each finding, produce an actionable recommendation:
Finding: [CRITICAL] 3 active contradictions about API endpoint URLs
Memory A: "API endpoint is /v2/users" (2026-01-15)
Memory B: "Migrated API to /v3/users" (2026-02-01)
Memory C: "API uses /api/v2/users prefix" (2026-01-20)
Recommendation: Resolve via nmem_conflicts
1. Keep Memory B (most recent, explicit migration note)
2. Mark A and C as superseded
3. Store clarification: "API migrated from /v2 to /v3 on 2026-02-01"
Impact: Eliminates recall confusion for API-related queries
Effort: 2 minutes
Present the audit report:
Memory Audit Report
Brain: default | Date: 2026-02-10
Overall Grade: B (82/100)
Dimension Scores:
Purity: ████████░░ 85/100 (0 conflicts, 2 near-duplicates)
Freshness: ███████░░░ 72/100 (18% stale, 1 expired TODO)
Coverage: █████████░ 90/100 (all major topics covered)
Clarity: ████████░░ 80/100 (3 vague memories found)
Relevance: █████████░ 88/100 (1 orphaned project reference)
Structure: ███████░░░ 75/100 (low synapse diversity)
Findings: 8 total
CRITICAL: 0
HIGH: 2 (staleness, vague decisions)
MEDIUM: 4 (duplicates, tag drift, low diversity, expired TODO)
LOW: 2 (acronyms, orphaned ref)
Top 3 Recommendations:
1. [HIGH] Clarify 3 vague decision memories — add reasoning
2. [MEDIUM] Resolve 2 near-duplicate memories about auth config
3. [MEDIUM] Run consolidation to improve synapse diversity
Projected grade after fixes: A- (91/100)
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
shadcn/improve
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
memory-audit has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: memory-audit is focused, and the summary matches what you get after install.
memory-audit fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
memory-audit is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
memory-audit reduced setup friction for our internal harness; good balance of opinion and flexibility.
We added memory-audit from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Keeps context tight: memory-audit is the kind of skill you can hand to a new teammate without a long onboarding doc.
memory-audit has been reliable in day-to-day use. Documentation quality is above average for community skills.
I recommend memory-audit for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Solid pick for teams standardizing on skills: memory-audit is focused, and the summary matches what you get after install.
showing 1-10 of 31