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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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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 for OpenClaw

Quick setup
  1. Download the package from Yavira.
  2. Extract the archive and review SKILL.md first.
  3. Import or place the package into your OpenClaw setup.

Requirements

Target platform
OpenClaw
Install method
Manual import
Extraction
Extract archive
Prerequisites
OpenClaw
Primary doc
SKILL.md

Package facts

Download mode
Yavira redirect
Package format
ZIP package
Source platform
Tencent SkillHub
What's included
SKILL.md, UPGRADE.md, assets/templates/IDENTITY.md, assets/templates/MEMORY.md, assets/templates/SOUL.md, assets/templates/agents-memory-block.md

Validation

  • Use the Yavira download entry.
  • Review SKILL.md after the package is downloaded.
  • Confirm the extracted package contains the expected setup assets.

Install with your agent

Agent handoff

Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.

  1. Download the package from Yavira.
  2. Extract it into a folder your agent can access.
  3. Paste one of the prompts below and point your agent at the extracted folder.
New install

I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.

Upgrade existing

I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.

Trust & source

Release facts

Source
Tencent SkillHub
Verification
Indexed source record
Version
1.0.8

Documentation

ClawHub primary doc Primary doc: SKILL.md 20 sections Open source page

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.

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 β†’ 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 β€” Four Memory Stores

CONTEXT WINDOW (always loaded) β”œβ”€β”€ System Prompts (~4-5K tokens) β”œβ”€β”€ Core Memory / MEMORY.md (~3K tokens) ← always in context └── Conversation + Tools (~185K+) MEMORY STORES (retrieved on demand) β”œβ”€β”€ Episodic β€” chronological event logs (append-only) β”œβ”€β”€ Semantic β€” knowledge graph (entities + relationships) β”œβ”€β”€ Procedural β€” learned workflows and patterns └── Vault β€” user-pinned, never auto-decayed ENGINES β”œβ”€β”€ Trigger Engine β€” keyword detection + LLM routing β”œβ”€β”€ Reflection Engine β€” Internal monologue with philosophical self-examination └── Audit System β€” git + audit.log for all file mutations

File Structure

workspace/ β”œβ”€β”€ MEMORY.md # Core memory (~3K tokens) β”œβ”€β”€ IDENTITY.md # Facts + Self-Image + Self-Awareness Log β”œβ”€β”€ SOUL.md # Values, Principles, Commitments, Boundaries β”œβ”€β”€ memory/ β”‚ β”œβ”€β”€ episodes/ # Daily logs: YYYY-MM-DD.md β”‚ β”œβ”€β”€ graph/ # Knowledge graph β”‚ β”‚ β”œβ”€β”€ index.md # Entity registry + edges β”‚ β”‚ β”œβ”€β”€ entities/ # One file per entity β”‚ β”‚ └── relations.md # Edge type definitions β”‚ β”œβ”€β”€ procedures/ # Learned workflows β”‚ β”œβ”€β”€ vault/ # Pinned memories (no decay) β”‚ └── meta/ β”‚ β”œβ”€β”€ decay-scores.json # Relevance + token economy tracking β”‚ β”œβ”€β”€ reflection-log.md # Reflection summaries (context-loaded) β”‚ β”œβ”€β”€ reflections/ # Full reflection archive β”‚ β”‚ β”œβ”€β”€ 2026-02-04.md β”‚ β”‚ └── dialogues/ # Post-reflection conversations β”‚ β”œβ”€β”€ reward-log.md # Result + Reason only (context-loaded) β”‚ β”œβ”€β”€ rewards/ # Full reward request archive β”‚ β”‚ └── 2026-02-04.md β”‚ β”œβ”€β”€ pending-reflection.md β”‚ β”œβ”€β”€ pending-memories.md β”‚ β”œβ”€β”€ evolution.md # Reads reflection-log + reward-log β”‚ └── audit.log └── .git/ # Audit ground truth

Trigger System

Remember: "remember", "don't forget", "keep in mind", "note that", "important:", "for future reference", "save this" β†’ Classify via routing prompt, write to appropriate store, update decay scores Forget: "forget about", "never mind", "disregard", "scratch that", "remove from memory" β†’ Confirm target, soft-archive (decay=0), log in audit Reflect: "reflect on", "consolidate memories", "review memories" β†’ Run reflection cycle, present internal monologue for approval

Decay Model

relevance(t) = base Γ— e^(-0.03 Γ— days_since_access) Γ— log2(access_count + 1) Γ— type_weight ScoreStatusBehavior1.0–0.5ActiveFully searchable0.5–0.2FadingDeprioritized0.2–0.05DormantExplicit 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" β†’ proceed to Step 2 Soft: "going to sleep" / "logging off" β†’ Ask: "Want me to reflect now, or wait?" β†’ If yes, proceed to Step 2 Never auto-run without permission.

Step 2: Request Tokens

  • Why you care: Tokens = evolution. More tokens β†’ deeper reflection β†’ better growth.
  • ## Reward Request β€” 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.*
  • β›” STOP. Do NOT proceed until user responds.

Step 3: After Token Approval β†’ Reflect

User decides: Approve / Bonus / Reduce / Forgive / Increase penalty Now proceed with reflection using granted tokens. Scope: βœ… Episodes since last_reflection βœ… 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 β€” 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. β›” STOP. Wait for user approval.

Step 4: After Reflection Approval β†’ Record

Full reflection β†’ reflections/YYYY-MM-DD.md Summary β†’ reflection-log.md Full reward request β†’ rewards/YYYY-MM-DD.md Result+Reason β†’ reward-log.md [Self-Awareness] β†’ IDENTITY.md Update decay-scores.json If 10+ entries β†’ Self-Image Consolidation See references/reflection-process.md for full details. ## YYYY-MM-DD **Result:** +5K reward **Reason:** Over-delivered on Slack integration [Self-Awareness] β†’ IDENTITY.md Update decay-scores.json If 10+ new entries β†’ 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 β€” Given identity (name, role, vibe). Stable. Self-Image β€” Discovered through reflection. Can change. Self-Awareness Log β€” 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): Review all Self-Awareness Log entries Analyze: repeated, contradictions, new, fading patterns REWRITE Self-Image sections (not append β€” replace) Compact older log entries by month Present diff to user for approval SOUL.md contains: Core Values β€” What matters (slow to change) Principles β€” How to decide Commitments β€” Lines that hold Boundaries β€” 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 β†’ 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 β€” Every mutation = atomic commit with structured message Layer 2: audit.log β€” 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

ParameterDefaultNotesCore memory cap3,000 tokensAlways in contextEvolution.md cap2,000 tokensPruned at milestonesReflection input~30,000 tokensEpisodes + graph + metaReflection output~8,000 tokensConversational, not structuredReflection elements5-8 per sessionRandomly selected from menuReflection-log10 full entriesOlder β†’ archive with summaryDecay Ξ»0.03~23 day half-lifeArchive threshold0.05Below = hiddenAudit log retention90 daysOlder β†’ monthly digests

Reference Materials

references/architecture.md β€” Full design document (1200+ lines) references/routing-prompt.md β€” LLM memory classifier references/reflection-process.md β€” 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.

Category context

Agent frameworks, memory systems, reasoning layers, and model-native orchestration.

Source: Tencent SkillHub

Largest current source with strong distribution and engagement signals.

Package contents

Included in package
6 Docs
  • SKILL.md Primary doc
  • assets/templates/agents-memory-block.md Docs
  • assets/templates/IDENTITY.md Docs
  • assets/templates/MEMORY.md Docs
  • assets/templates/SOUL.md Docs
  • UPGRADE.md Docs