- 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 — 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" — a token-budgeted summary of active self-knowledge
Setup
- Place
metacognition.pyin your workspacescripts/directory - The script stores data in
memory/metacognition.json(relative to workspace) - The compiled lens outputs to
scripts/metacognition-lens.md - 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 <WORKSPACE> && 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 <type> "<insight>"`
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 <type> <text> # Add or merge an entry
python3 metacognition.py list [type] # List entries
python3 metacognition.py feedback <id> <pos|neg> # Reinforce or weaken
python3 metacognition.py decay # Apply time-based decay
python3 metacognition.py compile # Compile the lens
python3 metacognition.py extract <path> # Extract from a daily note
python3 metacognition.py resolve <id> # Mark curiosity resolved
python3 metacognition.py reweave # Build graph connections
python3 metacognition.py graph # Show graph stats
python3 metacognition.py integrate # Full cycleConfiguration
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 — Things learned from experience
- overrides — Corrections to previous beliefs
- protections — Rules to prevent known failure modes
- self-observations — Patterns in own behavior
- decisions — Policy decisions for future behavior
- curiosities — Open questions with lifecycle (born → active → evolving → resolved)