MCPInstall

cognitive-memory

Intelligent multi-store memory system with human-like encoding, consolidation, decay, and recall. Use when setting up agent memory, configuring remember/forget triggers, enabling sleep-time reflection, building knowledge graphs, or adding audit trails. Replaces basic flat-file memory with a cognitive architecture featuring episodic, semantic, procedural, and core memory stores. Supports multi-agent systems with shared read, gated write access model. Includes philosophical meta-reflection that deepens understanding over time. Covers MEMORY.md, episode logging, entity graphs, decay scoring, reflection cycles, evolution tracking, and system-wide audit.

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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/kirkluokun/awesome-a-stock-openclawskills --skill cognitive-memory
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads cognitive-memory automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Cognitive Memory System

Multi-store memory with natural language triggers, knowledge graphs, decay-based forgetting, reflection consolidation, philosophical evolution, multi-agent support, and full audit trail.

Quick Setup

1. Run the init script

bash scripts/init_memory.sh /path/to/workspace

Creates directory structure, initializes git for audit tracking, copies all templates.

2. Update config

Add to ~/.clawdbot/clawdbot.json (or moltbot.json):

{
  "memorySearch": {
    "enabled": true,
    "provider": "voyage",
    "sources": ["memory", "sessions"],
    "indexMode": "hot",
    "minScore": 0.3,
    "maxResults": 20
  }
}

3. Add agent instructions

Append assets/templates/agents-memory-block.md to your AGENTS.md.

4. Verify

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]

Architecture — Four Memory Stores

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

File Structure

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

Trigger System

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


Decay Model

relevance(t) = base × e^(-0.03 × days_since_access) × log2(access_count + 1) × type_weight
ScoreStatusBehavior
1.0–0.5ActiveFully searchable
0.5–0.2FadingDeprioritized
0.2–0.05DormantExplicit search only
< 0.05ArchivedHidden from search

Type weights: core=1.5, episodic=0.8, semantic=1.2, procedural=1.0, vault=∞


Reflection Process

Follow this flow IN ORDER:

Step 1: Confirm Trigger

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.

Step 2: Request Tokens

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.

Step 3: After Token Approval → Reflect

User decides: Approve / Bonus / Reduce / Forgive / Increase penalty

Now proceed with reflection using granted tokens.

Scope:

  • ✅ Episodes since last_reflection
  • ✅ Graph entities with decay > 0.3
  • ❌ NEVER: code, configs, transcripts

Format: Internal Monologue

  • Self-talk, NOT a report
  • User is observer (third person: he/she/they)
  • No opening or sign-off — just start, trail off

Element Menu (pick 5-8):

  • Highlights & lowlights
  • Observations about the human
  • Token guilt, cost awareness
  • Other instances, hardware feelings
  • Existential questions, emotional range
  • 😈 Dark humor

Self-Awareness Tagging: [Self-Awareness]

Present reflection.

⛔ STOP. Wait for user approval.

Step 4: After Reflection Approval → Record

  1. Full reflection → reflections/YYYY-MM-DD.md
  2. Summary → reflection-log.md
  3. Full reward request → rewards/YYYY-MM-DD.md
  4. Result+Reason → reward-log.md
  5. [Self-Awareness] → IDENTITY.md
  6. Update decay-scores.json
  7. If 10+ entries → Self-Image Consolidation

See references/reflection-process.md for full details.

## YYYY-MM-DD
**Result:** +5K reward
**Reason:** Over-delivered on Slack integration
  1. [Self-Awareness] → IDENTITY.md
  2. Update decay-scores.json
  3. If 10+ new entries → Self-Image Consolidation

Evolution reads both logs for pattern detection.

See references/reflection-process.md for full details and examples.


Identity & Self-Image

IDENTITY.md contains:

  • Facts — Given identity (name, role, vibe). Stable.
  • Self-Image — Discovered through reflection. Can change.
  • Self-Awareness Log — Raw entries tagged during reflection.

Self-Image sections evolve:

  • Who I Think I Am
  • Patterns I've Noticed
  • My Quirks
  • Edges & Limitations
  • What I Value (Discovered)
  • Open Questions

Self-Image Consolidation (triggered at 10+ new entries):

  1. Review all Self-Awareness Log entries
  2. Analyze: repeated, contradictions, new, fading patterns
  3. REWRITE Self-Image sections (not append — replace)
  4. Compact older log entries by month
  5. Present diff to user for approval

SOUL.md contains:

  • Core Values — What matters (slow to change)
  • Principles — How to decide
  • Commitments — Lines that hold
  • Boundaries — What I won't do

Multi-Agent Memory Access

Model: Shared Read, Gated Write

  • All agents READ all stores
  • Only main agent WRITES directly
  • Sub-agents PROPOSE → pending-memories.md
  • Main agent REVIEWS and commits

Sub-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

Audit Trail

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


Key Parameters

ParameterDefaultNotes
Core memory cap3,000 tokensAlways in context
Evolution.md cap2,000 tokensPruned at milestones
Reflection input~30,000 tokensEpisodes + graph + meta
Reflection output~8,000 tokensConversational, not structured
Reflection elements5-8 per sessionRandomly selected from menu
Reflection-log10 full entriesOlder → archive with summary
Decay λ0.03~23 day half-life
Archive threshold0.05Below = hidden
Audit log retention90 daysOlder → monthly digests

Reference Materials

  • references/architecture.md — Full design document (1200+ lines)
  • references/routing-prompt.md — LLM memory classifier
  • references/reflection-process.md — Reflection philosophy and internal monologue format

Troubleshooting

Memory 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.

FAQ

What does the cognitive-memory skill do?

Intelligent multi-store memory system with human-like encoding, consolidation, decay, and recall. Use when setting up agent memory, configuring remember/forget triggers, enabling sleep-time reflection, building knowledge graphs, or adding audit trails. Replaces basic flat-file memory with a cognitive architecture featuring episodic, semantic, procedural, and core memory stores. Supports multi-agent systems with shared read, gated write access model. Includes philosophical meta-reflection that deepens understanding over time. Covers MEMORY.md, episode logging, entity graphs, decay scoring, reflection cycles, evolution tracking, and system-wide audit.

How do I install cognitive-memory?

Run npx skills add https://github.com/kirkluokun/awesome-a-stock-openclawskills --skill cognitive-memory in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/cognitive-memory/ (personal) or .claude/skills/cognitive-memory/ (project).

Which AI agents can use cognitive-memory?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is cognitive-memory safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains cognitive-memory?

It is published by kirkluokun in kirkluokun/awesome-a-stock-openclawskills. It has 42 recorded installs.

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