meta-cognition

Evaluate reasoning steps, assumptions, and biases before presenting conclusions.

1|1|Updated Mar 31, 2026

Search Tags:#meta-cognition #reasoning #traceability #decision-making #critical-thinking #self-evaluation #biases

Ready to run

npx skills add https://github.com/iAMv1/agent-os --skill meta-cognition

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: meta-cognition
Source: https://github.com/iAMv1/agent-os/tree/main/skills/meta-cognition
Command: npx skills add https://github.com/iAMv1/agent-os --skill meta-cognition

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Meta-cognition helps AI agents and humans improve decision quality by evaluating thinking processes, identifying biases, and verifying conclusions before presenting results.

Core Features & Use Cases

Self-evaluation of reasoning steps and assumptions
Bias checks and steel-man the opposition
Calibration of confidence and documentation of lessons learned
Post-analysis review for continuous improvement

Quick Start

Prompt the agent to perform a structured self-review of its reasoning before delivering an answer.

Frequently Asked Questions about meta-cognition

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I improve AI reasoning reliability for complex decision-making?▼

To improve reasoning reliability, you can prompt the AI to perform a structured self-evaluation, tracing its reasoning steps, listing assumptions, and calibrating confidence before delivering an answer.

How do I check for cognitive biases in AI-generated analysis?▼

Checking for biases involves applying a self-evaluation process where the AI explicitly reviews its reasoning, steel-mans the opposition, and verifies conclusions before presenting the final results.

What is the best way to trace AI assumptions during high-stakes problem solving?▼

The best way to trace assumptions is to require explicit steps for documenting them, calibrating confidence levels, and performing a post-analysis review to ensure thinking quality in high-stakes scenarios.

Can I use self-evaluation to document lessons learned from AI post-hoc reviews?▼

Yes, you can use self-evaluation during post-hoc reviews to identify biases, verify conclusions, and explicitly document lessons learned for continuous improvement of future decision-making.

When should I apply critical-thinking checks to an AI agent's output?▼

Critical-thinking checks should be applied to high-stakes or complex problems where thinking quality matters, specifically by verifying conclusions and evaluating the underlying reasoning process.

Why does calibrating confidence help with traceability in AI reasoning?▼

Calibrating confidence helps traceability by forcing the explicit documentation of reasoning steps and assumptions, making it easier to evaluate decision quality and review the thinking process.