Skill: meimakes/metacognition (version 1.1.2)

--- 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 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 <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 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** — 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)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)