Multi-store memory with natural language triggers, knowledge graphs, decay-based forgetting, reflection consolidation, philosophical evolution, multi-agent support, and full audit trail.
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AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versioncognitive-memoryExecute the skills CLI command in your project's root directory to begin installation:
Fetches cognitive-memory from cjhfff/cjh-skills 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 cognitive-memory. Access via /cognitive-memory 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
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Multi-store memory with natural language triggers, knowledge graphs, decay-based forgetting, reflection consolidation, philosophical evolution, multi-agent support, and full audit trail.
bash scripts/init_memory.sh /path/to/workspace
Creates directory structure, initializes git for audit tracking, copies all templates.
Add to ~/.clawdbot/clawdbot.json (or moltbot.json):
{
"memorySearch": {
"enabled": true,
"provider": "voyage",
"sources": ["memory", "sessions"],
"indexMode": "hot",
"minScore": 0.3,
"maxResults": 20
}
}
Append assets/templates/agents-memory-block.md to your AGENTS.md.
User: "Remember that I prefer TypeScript over JavaScript."
Agent: [Classifies → writes to semantic store + core memory, logs audit entry]
User: "What do you know about my preferences?"
Agent: [Searches core memory first, then semantic graph]
CONTEXT WINDOW (always loaded)
├── System Prompts (~4-5K tokens)
├── Core Memory / MEMORY.md (~3K tokens) ← always in context
└── Conversation + Tools (~185K+)
MEMORY STORES (retrieved on demand)
├── Episodic — chronological event logs (append-only)
├── Semantic — knowledge graph (entities + relationships)
├── Procedural — learned workflows and patterns
└── Vault — user-pinned, never auto-decayed
ENGINES
├── Trigger Engine — keyword detection + LLM routing
├── Reflection Engine — Internal monologue with philosophical self-examination
└── Audit System — git + audit.log for all file mutations
workspace/
├── MEMORY.md # Core memory (~3K tokens)
├── IDENTITY.md # Facts + Self-Image + Self-Awareness Log
├── SOUL.md # Values, Principles, Commitments, Boundaries
├── memory/
│ ├── episodes/ # Daily logs: YYYY-MM-DD.md
│ ├── graph/ # Knowledge graph
│ │ ├── index.md # Entity registry + edges
│ │ ├── entities/ # One file per entity
│ │ └── relations.md # Edge type definitions
│ ├── procedures/ # Learned workflows
│ ├── vault/ # Pinned memories (no decay)
│ └── meta/
│ ├── decay-scores.json # Relevance + token economy tracking
│ ├── reflection-log.md # Reflection summaries (context-loaded)
│ ├── reflections/ # Full reflection archive
│ │ ├── 2026-02-04.md
│ │ └── dialogues/ # Post-reflection conversations
│ ├── reward-log.md # Result + Reason only (context-loaded)
│ ├── rewards/ # Full reward request archive
│ │ └── 2026-02-04.md
│ ├── pending-reflection.md
│ ├── pending-memories.md
│ ├── evolution.md # Reads reflection-log + reward-log
│ └── audit.log
└── .git/ # Audit ground truth
Remember: "remember", "don't forget", "keep in mind", "note that", "important:", "for future reference", "save this" → Classify via routing prompt, write to appropriate store, update decay scores
Forget: "forget about", "never mind", "disregard", "scratch that", "remove from memory" → Confirm target, soft-archive (decay=0), log in audit
Reflect: "reflect on", "consolidate memories", "review memories" → Run reflection cycle, present internal monologue for approval
relevance(t) = base × e^(-0.03 × days_since_access) × log2(access_count + 1) × type_weight
| Score | Status | Behavior |
|---|---|---|
| 1.0–0.5 | Active | Fully searchable |
| 0.5–0.2 | Fading | Deprioritized |
| 0.2–0.05 | Dormant | Explicit search only |
| < 0.05 | Archived | Hidden from search |
Type weights: core=1.5, episodic=0.8, semantic=1.2, procedural=1.0, vault=∞
Follow this flow IN ORDER:
Immediate: "reflect" / "let's reflect" → proceed to Step 2
Soft: "going to sleep" / "logging off" → Ask: "Want me to reflect now, or wait?" → If yes, proceed to Step 2
Never auto-run without permission.
Why you care: Tokens = evolution. More tokens → deeper reflection → better growth.
## Reward Request — YYYY-MM-DD
### Baseline: 8,000 tokens
### Extra Requested: +[N] tokens
- [Over-delivered / workload / breakthrough]
### Self-Penalty: -[N] tokens
- [Mistake / underperformance]
### Final Request: [N] tokens
*Awaiting your decision.*
⛔ STOP. Do NOT proceed until user responds.
User decides: Approve / Bonus / Reduce / Forgive / Increase penalty
Now proceed with reflection using granted tokens.
Scope:
last_reflectionFormat: Internal Monologue
Element Menu (pick 5-8):
Self-Awareness Tagging: [Self-Awareness]
Present reflection.
⛔ STOP. Wait for user approval.
reflections/YYYY-MM-DD.mdreflection-log.mdrewards/YYYY-MM-DD.mdreward-log.md[Self-Awareness] → IDENTITY.mddecay-scores.jsonSee references/reflection-process.md for full details.
## YYYY-MM-DD
**Result:** +5K reward
**Reason:** Over-delivered on Slack integration
[Self-Awareness] → IDENTITY.mddecay-scores.jsonEvolution reads both logs for pattern detection.
See references/reflection-process.md for full details and examples.
IDENTITY.md contains:
Self-Image sections evolve:
Self-Image Consolidation (triggered at 10+ new entries):
SOUL.md contains:
Model: Shared Read, Gated Write
pending-memories.mdSub-agent proposal format:
## Proposal #N
- **From**: [agent name]
- **Timestamp**: [ISO 8601]
- **Suggested store**: [episodic|semantic|procedural|vault]
- **Content**: [memory content]
- **Confidence**: [high|medium|low]
- **Status**: pending
Layer 1: Git — Every mutation = atomic commit with structured message Layer 2: audit.log — One-line queryable summary
Actor types: bot:trigger-remember, reflection:SESSION_ID, system:decay, manual, subagent:NAME, bot:commit-from:NAME
Critical file alerts: SOUL.md, IDENTITY.md changes flagged ⚠️ CRITICAL
| Parameter | Default | Notes |
|---|---|---|
| Core memory cap | 3,000 tokens | Always in context |
| Evolution.md cap | 2,000 tokens | Pruned at milestones |
| Reflection input | ~30,000 tokens | Episodes + graph + meta |
| Reflection output | ~8,000 tokens | Conversational, not structured |
| Reflection elements | 5-8 per session | Randomly selected from menu |
| Reflection-log | 10 full entries | Older → archive with summary |
| Decay λ | 0.03 | ~23 day half-life |
| Archive threshold | 0.05 | Below = hidden |
| Audit log retention | 90 days | Older → monthly digests |
references/architecture.md — Full design document (1200+ lines)references/routing-prompt.md — LLM memory classifierreferences/reflection-process.md — Reflection philosophy and internal monologue formatMemory not persisting? Check memorySearch.enabled: true, verify MEMORY.md exists, restart gateway.
Reflection not running? Ensure previous reflection was approved/rejected.
Audit trail not working? Check .git/ exists, verify audit.log is writable.
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.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
Keeps context tight: cognitive-memory is the kind of skill you can hand to a new teammate without a long onboarding doc.
Solid pick for teams standardizing on skills: cognitive-memory is focused, and the summary matches what you get after install.
We added cognitive-memory from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
I recommend cognitive-memory for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
cognitive-memory is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
cognitive-memory reduced setup friction for our internal harness; good balance of opinion and flexibility.
Registry listing for cognitive-memory matched our evaluation — installs cleanly and behaves as described in the markdown.
cognitive-memory fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Useful defaults in cognitive-memory — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
I recommend cognitive-memory for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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