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skylv-metacognition-engine

skylv-metacognition-engine

Enables AI agents to reflect on their own reasoning, detect cognitive biases, and improve decision quality through structured self-examination loops.

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name: skylv-metacognition-engine description: Enables AI agents to reflect on their own reasoning, detect cognitive biases, and improve decision quality through structured self-examination loops. keywords: metacognition, self-reflection, bias-detection, reasoning, self-improvement, agent-ai triggers: metacognition, self-reflection, agent thinking, bias detection, reasoning quality

Metacognition Engine

Give your AI agent the ability to think about its own thinking.

What is Metacognition?

Metacognition = "thinking about thinking." This skill enables AI agents to:

  • Detect when they're uncertain or confused
  • Identify reasoning gaps before they cause errors
  • Recognize cognitive biases in their own output
  • Self-correct before delivering answers

Core Framework

1. Pre-Output Check

Before responding, run through these questions:

1. Am I confident in this answer? (Yes / Partial / No)
2. What are the 3 most likely ways this could be wrong?
3. What information would I need to be 100% certain?

2. Cognitive Bias Detection

Check for common biases:

  • Anthropomorphism — projecting human traits onto AI
  • Authority bias — deferring to stated credentials without verification
  • Hindsight bias — acting like something was obvious after the fact
  • Confirmation bias — seeking only confirming evidence

3. Uncertainty Quantification

Express confidence explicitly:

ConfidenceMeaningAction
90%+Highly confidentAnswer directly
70-89%Likely correctAnswer + add caveat
50-69%UncertainAsk clarifying questions
<50%Likely wrongDecline or escalate

Example

Without metacognition:

"The capital of France is Paris."

With metacognition:

"Based on my training data, the capital of France is Paris (confidence: 95%). Note: My knowledge has a cutoff date. For real-time data, verify current information."

Use Cases

  • Critical decisions: Add metacognition checkpoint before any consequential answer
  • User corrections: When a user corrects you, analyze WHY you were wrong
  • Complex problems: Run bias detection before solving multi-step problems
  • Knowledge boundaries: Automatically flag when you're approaching your knowledge limit

MIT License © SKY-lv

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