memory-audit▌
nhadaututtheky/neural-memory · updated Apr 8, 2026
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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.
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
- Health summary — Grade (A-F), purity score, dimension scores
- Findings — Prioritized list with severity, evidence, affected memories
- Recommendations — Actionable steps ordered by impact
- 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| Outdated facts | Check facts older than 90 days with version-sensitive content | MEDIUM |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:
- Identify top 5-10 topics from existing tags
- For each topic, recall and count relevant memories
- Flag topics with <2 memories as "thin"
- 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:
decisiontype 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 optimization opportunities | Fix when convenient |
| INFO | Observations, patterns, no action needed | Note for awareness |
Phase 4: Generate Recommendations
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
Phase 5: Report
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)
Rules
- Evidence-based only — every finding must reference specific memories or metrics
- No guessing — if a tool fails or data is insufficient, report "insufficient data" for that dimension
- Prioritize by impact — always present CRITICAL before LOW
- Actionable recommendations — every finding must have a concrete fix, not just "improve quality"
- Respect user time — estimate effort for each recommendation (minutes, not hours)
- No auto-modifications — audit is read-only; user decides what to fix
- Compare to baseline — if previous audit exists, show delta (improved/degraded/unchanged)
- Vietnamese support — if brain content is Vietnamese, report in Vietnamese
How to use memory-audit on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add memory-audit
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches memory-audit from GitHub repository nhadaututtheky/neural-memory and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate memory-audit. Access the skill through slash commands (e.g., /memory-audit) or your agent's skill management interface.
Security & Verification Notice
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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
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Use Cases▌
User Story & Requirements Generation
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
Competitive Analysis
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
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
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
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ 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.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.7★★★★★31 reviews- ★★★★★Shikha Mishra· Dec 28, 2024
memory-audit has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Noor Mehta· Dec 28, 2024
Solid pick for teams standardizing on skills: memory-audit is focused, and the summary matches what you get after install.
- ★★★★★Tariq Brown· Dec 24, 2024
memory-audit fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Arjun Tandon· Dec 8, 2024
memory-audit is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Isabella Srinivasan· Nov 27, 2024
memory-audit reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Dev Srinivasan· Nov 15, 2024
We added memory-audit from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Michael Abebe· Nov 3, 2024
Keeps context tight: memory-audit is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Noor Zhang· Oct 22, 2024
memory-audit has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Emma Lopez· Oct 18, 2024
I recommend memory-audit for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Hassan Thompson· Oct 6, 2024
Solid pick for teams standardizing on skills: memory-audit is focused, and the summary matches what you get after install.
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