Enables AI agents to reflect on their own reasoning, detect cognitive biases, and improve decision quality through structured self-examination loops.
--- 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: | Confidence | Meaning | Action | |------------|---------|--------| | 90%+ | Highly confident | Answer directly | | 70-89% | Likely correct | Answer + add caveat | | 50-69% | Uncertain | Ask clarifying questions | | <50% | Likely wrong | Decline 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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