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BytesAgain β€Ί Skills β€Ί Metacognition
πŸ¦€ ClawHub

Metacognition

by @meimakes

Self-reflection engine for AI agents. Extracts patterns from session transcripts into a weighted graph with Hebbian learning and time decay. Compiles a token...

Versionv1.1.2
Downloads811
Installs3
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TERMINAL
clawhub install metacognition

πŸ“– About This Skill

name: metacognition description: Self-reflection engine for AI agents. Extracts patterns from session transcripts into a weighted graph with Hebbian learning and time decay. Compiles a token-budgeted lens of active self-knowledge. metadata: {"openclaw":{"requires":{"bins":["python3"]},"writablePaths":["memory/metacognition.json","scripts/metacognition-lens.md"],"readablePaths":["memory/"],"env":{"EMBEDDINGS_URL":"optional, localhost-only embeddings endpoint (defaults to http://localhost:4821/v1/embeddings, remote URLs rejected at startup)","WORKSPACE":"optional, workspace root path"},"security":"localhost-only network (EMBEDDINGS_URL validated to 127.0.0.1/localhost/::1 at import time, remote URLs disable embeddings entirely), no curl/subprocess β€” uses Python stdlib urllib only, extract command limited to 1MB file reads","homepage":"https://github.com/meimakes/metacognition","author":"Mei Park (@meimakes)"}}

Metacognition Skill

A self-reflection engine for AI agents. Extracts patterns from session transcripts into a weighted graph with Hebbian learning and time decay.

What It Does

  • Maintains a store of categorized insights (perceptions, overrides, protections, self-observations, decisions, curiosities)
  • Uses Hebbian reinforcement: repeated insights get stronger, unused ones decay
  • Builds a graph of connections between related insights
  • Finds clusters of related knowledge that may represent higher-level principles
  • Compiles a "metacognition lens" β€” a token-budgeted summary of active self-knowledge

    • Setup

    1. Place metacognition.py in your workspace scripts/ directory 2. The script stores data in memory/metacognition.json (relative to workspace) 3. The compiled lens outputs to scripts/metacognition-lens.md 4. Optionally configure a local embeddings endpoint for semantic similarity (falls back to string matching)

    Cron Integration

    Set up a cron job to run periodically (e.g., every 4 hours):

    METACOGNITION INTEGRATION. You are the self-reflection engine.
    1. Run cd  && python3 scripts/metacognition.py decay to prune weak entries.
    2. Use sessions_list + sessions_history to read the main session's recent conversation.
    3. Analyze the conversation for DEEPER patterns:
   - PATTERNS: Am I repeating the same kind of mistake? What does that reveal?
   - ANTICIPATION: What did the human need that I could have predicted?
   - RELATIONSHIP: What did I learn about how the user communicates or what they value?
   - CONFIDENCE: Where was I certain and wrong? Where was I uncertain but right?
   - GROWTH: What's a higher-level principle behind today's specific events?
    4. For each genuine insight (1-3, quality over quantity), add it:
   python3 scripts/metacognition.py add  ""
   Types: perceptions, overrides, protections, self-observations, decisions, curiosities
   Write insights as PRINCIPLES, not incident reports.
    5. Run python3 scripts/metacognition.py reweave to build graph connections.
    6. Run python3 scripts/metacognition.py compile to rebuild the lens.
    7. Report only if something genuinely interesting was extracted.

    CLI Commands

    python3 metacognition.py add         # Add or merge an entry
python3 metacognition.py list [type]              # List entries
python3 metacognition.py feedback    # Reinforce or weaken
python3 metacognition.py decay                    # Apply time-based decay
python3 metacognition.py compile                  # Compile the lens
python3 metacognition.py extract            # Extract from a daily note
python3 metacognition.py resolve              # Mark curiosity resolved
python3 metacognition.py reweave                  # Build graph connections
python3 metacognition.py graph                    # Show graph stats
python3 metacognition.py integrate                # Full cycle

    Configuration

    Key constants in the script:

    | Constant | Default | Description | |----------|---------|-------------| | HALF_LIFE_DAYS | 7.0 | How quickly unreinforced entries decay | | STRENGTH_CAP | 3.0 | Maximum strength an entry can reach | | LENS_TOKEN_BUDGET | 500 | Token budget for compiled lens | | EMBEDDING_SIM_THRESHOLD | 0.85 | Similarity threshold for merging (embeddings) | | FALLBACK_SIM_THRESHOLD | 0.72 | Similarity threshold for merging (string matching) | | EDGE_SIM_THRESHOLD | 0.35 | Threshold for creating graph edges |

    Entry Types

  • perceptions β€” Things learned from experience
  • overrides β€” Corrections to previous beliefs
  • protections β€” Rules to prevent known failure modes
  • self-observations β€” Patterns in own behavior
  • decisions β€” Policy decisions for future behavior
  • curiosities β€” Open questions with lifecycle (born β†’ active β†’ evolving β†’ resolved)

    • βš™οΈ Configuration

    Key constants in the script:

    | Constant | Default | Description | |----------|---------|-------------| | HALF_LIFE_DAYS | 7.0 | How quickly unreinforced entries decay | | STRENGTH_CAP | 3.0 | Maximum strength an entry can reach | | LENS_TOKEN_BUDGET | 500 | Token budget for compiled lens | | EMBEDDING_SIM_THRESHOLD | 0.85 | Similarity threshold for merging (embeddings) | | FALLBACK_SIM_THRESHOLD | 0.72 | Similarity threshold for merging (string matching) | | EDGE_SIM_THRESHOLD | 0.35 | Threshold for creating graph edges |