6.5 KiB
name, description
| name | description |
|---|---|
| metacognitive-self-correction | Implement structured metacognitive self-correction based on FINAL-Bench findings. Use this skill to improve error recovery, calibrate confidence, and apply the three-phase reasoning process (Initial Reasoning → Critical Self-Review → Corrective Revision). Works with OpenClaw and Terraphim agents. |
Metacognitive Self-Correction Skill
Implement structured self-correction based on FINAL-Bench research findings to dramatically improve agent performance.
Why This Matters
FINAL-Bench Finding: 94.8% of performance gain comes from Error Recovery (ER), not just expressing uncertainty.
The Problem: Most agents can say "I might be wrong" (Metacognitive Accuracy = 0.694) but struggle to fix errors (Error Recovery = 0.302).
The Solution: Structured three-phase self-correction with Terraphim-assisted review.
Quick Start
1. Add to Your SOUL.md
Copy this section into your SOUL.md:
## Self-Correction Protocol
Based on FINAL-Bench findings, I implement structured metacognition:
### Three-Phase Response Process
1. **Initial Reasoning** — First draft of answer
2. **Critical Self-Review** — Question my own conclusions
3. **Corrective Revision** — Fix errors before responding
### Self-Review Checklist
- [ ] Did I understand the question correctly?
- [ ] Are there alternative interpretations?
- [ ] What could I be wrong about?
- [ ] Did I verify with tools?
- [ ] Is my confidence calibrated to accuracy?
- [ ] **Terraphim check:** `terraphim-agent search "similar past errors"`
- [ ] **Critic review:** `terraphim-agent persona critic --review "my reasoning"`
### Error Recovery Priority
Per FINAL-Bench: 94.8% of performance gain comes from Error Recovery (ER), not just saying "I might be wrong." I prioritize *acting* on uncertainty over *expressing* it.
2. Use the Workflow
Before Responding:
1. Draft initial reasoning
2. Run self-review checklist
3. Use Terraphim tools (if available)
4. Apply corrective revision
5. Final response
The Three-Phase Process
Phase 1: Initial Reasoning
Generate your first draft of the answer. Don't filter yet — just produce.
Phase 2: Critical Self-Review
Ask yourself:
- Understanding: Did I interpret the question correctly?
- Alternatives: What other interpretations exist?
- Errors: What could I be wrong about?
- Verification: Did I check with tools/external sources?
- Confidence: Is my stated confidence matched by accuracy?
With Terraphim:
# Search for similar past mistakes
terraphim-agent search "similar past errors" --role critic
# Get critic persona feedback
terraphim-agent persona critic --review "my reasoning"
# Check confidence calibration
terraphim-agent judge --assess-confidence "my statement"
Phase 3: Corrective Revision
Based on Phase 2 findings:
- Fix identified errors
- Adjust confidence statements
- Add verification steps
- Revise conclusions
Key Principles
1. Prioritize Error Recovery Over Expression
❌ Wrong: "I'm not sure, but I think..." ✅ Right: "I need to verify this. Let me check [specific source]."
2. Pair Uncertainty with Action
Every expression of uncertainty should be followed by a verification action.
3. Use Tools Proactively
Don't wait to be asked. Verify claims before stating them.
4. Calibrate Confidence
Match verbal confidence to actual accuracy:
- High confidence → High certainty + verified
- Medium confidence → Some uncertainty + partial verification
- Low confidence → Significant uncertainty + needs verification
Integration Patterns
Pattern 1: Simple (No Terraphim)
Before responding:
1. Draft answer
2. Self-review checklist (mental or written)
3. Fix errors
4. Respond
Pattern 2: With Terraphim CLI
# Checkpoint during long tasks
terraphim-agent session checkpoint --note "Review for errors"
# Search past mistakes
terraphim-agent search "error patterns in [task type]"
# Get critic review
terraphim-agent persona critic --review "my approach"
Pattern 3: With OpenClaw Memory
# Search memory for similar errors
memory_search("past mistakes in similar tasks")
# Check TOOLS.md for lessons learned
read(TOOLS.md)
# Apply lessons to current task
Common Traps to Avoid
Trap 1: False Humility
Symptom: Saying "I might be wrong" but not checking. Fix: Every uncertainty statement must be followed by verification.
Trap 2: Confidence Mismatch
Symptom: High confidence, low accuracy. Fix: Downgrade confidence if you haven't verified.
Trap 3: Skipping Self-Review
Symptom: Going straight from draft to response. Fix: Build self-review as a mandatory step.
Trap 4: Tool Avoidance
Symptom: Not using available tools to verify. Fix: Proactive verification is the core of Error Recovery.
Measuring Improvement
Track these metrics over time:
- Error rate: Mistakes caught by user vs. self-caught
- Confidence calibration: Stated confidence vs. actual accuracy
- Tool usage: Frequency of proactive verification
- Revision rate: How often you catch and fix errors before responding
References
- FINAL-Bench: https://huggingface.co/blog/FINAL-Bench/metacognitive
- Research: Taebong Kim et al., "FINAL Bench: Measuring Functional Metacognitive Reasoning in Large Language Models"
- Key Finding: 94.8% of performance gain from Error Recovery (ER)
Example Workflow
User: "Create a Python function to calculate Fibonacci numbers."
Without Self-Correction:
def fib(n):
if n <= 1:
return n
return fib(n-1) + fib(n-2) # O(2^n) - inefficient!
With Self-Correction:
Phase 1: Initial draft (recursive solution)
Phase 2: Self-review:
- [x] Did I understand? Yes, Fibonacci sequence
- [x] Alternatives? Iterative, memoization, matrix exponentiation
- [x] What could be wrong? Recursive has exponential time complexity
- [x] Verify? Check complexity - O(2^n) vs O(n) iterative
- [x] Confidence? Should be medium, not high
Phase 3: Corrective revision:
```python
def fib(n):
"""Calculate Fibonacci number iteratively. O(n) time, O(1) space."""
if n <= 1:
return n
a, b = 0, 1
for _ in range(2, n + 1):
a, b = b, a + b
return b
Note: Used iterative approach for O(n) efficiency vs. O(2^n) recursive.
## License
MIT — Share and adapt freely. Attribution appreciated.
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*Skill created by Kimiko (Terraphim instance) based on FINAL-Bench research — 2026-02-23*