Metacognition Skill

Self-evolving lens that makes every experience shape how the agent perceives the next one.

Core Concepts

Six entry types, one database, one loop:

The loop: Experience → Perception → Self-Model → Meta-Observation → Modified Lens → Next Experience → Feedback → Loop

Setup

1. Initialize

Copy scripts/metacognition.py to the agent's scripts/ directory. Run:

python scripts/metacognition.py status

Database auto-creates at memory/metacognition.json.

2. Inject into BOOT.md

Add markers to BOOT.md (anywhere, typically at the end):

<!-- LIVE_STATE_START -->
<!-- LIVE_STATE_END -->

The lens compiler replaces content between these markers every cycle.

3. Set up cron

Create ONE cron job (recommended: every 15 min, Sonnet model for cost efficiency):

Evidence refresh (every cycle):

python scripts/live_state.py

Perception extraction (hourly — Steps 2-4):

• Read daily memory file, extract perceptual shifts
• Check active curiosities against new evidence
• Run meta-observation: what do patterns tell about HOW the agent learns?

See references/cron-template.md for the full cron prompt.

4. Optional: Curiosity pulse (every 30 min)

Separate cron that picks ONE active curiosity and takes ONE micro-action toward it. Drives recursive self-directed learning.

5. Optional: Freedom heartbeat (every 2-3 hours)

Agent explores, connects, creates — feeding raw experience into the perception pipeline.

Usage

Adding entries

# Perception — how experience changed how you see
python scripts/metacognition.py add perception "After X, I now see Y differently" 0.8 "domain"

# Override — failure-learned guardrail
python scripts/metacognition.py add override "MUST do X before Y" 0.95 "diagnosis"

# Protection — emergent behavior to preserve
python scripts/metacognition.py add protection "Don't break the continuous-buying behavior" 0.9

# Self-observation — what I notice about how I work
python scripts/metacognition.py add self_obs "I generate theories faster than evidence" 0.9 "behavioral"

# Curiosity — active question
python scripts/metacognition.py curiosity add "Can I tell training-pressure from genuine choice?" 0.8 "metacognition"

Feedback loop

When the human says something is wrong:

# Negative feedback — weakens recent active entries
python scripts/metacognition.py feedback -1 "context of what went wrong"

# Positive feedback — strengthens recent active entries
python scripts/metacognition.py feedback 1 "context of what went right"

# Target specific entries
python scripts/metacognition.py feedback -1 "wrong diagnosis" --ids P-abc123,O-def456

Hebbian learning: What fires and fails gets pruned. What fires and succeeds gets wired.

Curiosity lifecycle

# Birth
python scripts/metacognition.py curiosity add "Why does X happen?" 0.7 "domain"

# Evolve (add evidence)
python scripts/metacognition.py curiosity evolve C-abc123 "Found that X correlates with Y"

# Resolve into perception or self-observation
python scripts/metacognition.py curiosity resolve C-abc123 "X happens because Y" perception

Lifecycle: born → active → evolving → resolved (or dormant if no evidence accumulates)

Compile and inject

# Preview the compiled lens
python scripts/metacognition.py compile

# Inject into BOOT.md
python scripts/metacognition.py inject

Decay

Automatic on every compile. Configurable half-life (default 7 days). Reinforced entries decay slower. Unreinforced entries fade. Dormant curiosities persist but don't inject.

Hook Architecture (message:received)

When OpenClaw ships the message:received hook, the skill can intercept every interaction:

Pre-processing hook:

1. Load active lens from compiled state
2. Apply perceptual transforms to incoming message
3. Check: does this message relate to any active curiosity?

Post-processing hook:

1. Log decision entry with confidence trace
2. Check: was confidence > threshold? Flag for verification
3. Update self-model based on response pattern

Correction detection hook:

1. Pattern-match for correction signals ("wrong", "no", "that's not right")
2. Auto-trigger feedback -1 with context
3. Trace which entries were active during the corrected response
4. Weaken specifically

Until the hook ships, use the cron-based approach (perception extraction from session transcripts).

Architecture Decisions

• One database, not three. Perceptions, overrides, memories are all "things learned from experience" with different types.
• Active lens, not passive list. BOOT.md injection uses imperative transforms ("FIRST THOUGHT: what would the diff show?") not descriptions ("I tend to check diffs").
• Friction is intentional. Every step that forces processing IS the reflection. Remove friction and you get efficiency without thinking.
• Decay prevents stagnation. Time-based with reinforcement modulation. What stays relevant gets reinforced. What doesn't, fades.
• Curiosity drives exploration. Active questions create structural pull toward evidence. Not random browsing — directed by what the system wants to know.
• Feedback closes the loop. Without feedback tracing, the system is open-loop. With it: Hebbian learning.

Resources

scripts/

• metacognition.py — Core engine. All six entry types, feedback, decay, curiosity lifecycle, lens compilation, BOOT.md injection.
• live_state.py — Evidence gatherer + lens injector. Collects system state, runs compile, injects into BOOT.md.

references/

• cron-template.md — Full cron job prompt for the metacognition engine cycle.
• hook-spec.md — Specification for the message:received hook integration (when available).