cognito-auditor

Analyze AI outputs post-mortem and extract actionable lessons.

20|15|Updated May 7, 2026

Search Tags:#lessons learned #systematic analysis #AI review #AI quality assurance #post-mortem analysis

Ready to run

npx skills add https://github.com/iamasters-academy/iamasters-os --skill cognito-auditor

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: cognito-auditor
Source: https://github.com/iamasters-academy/iamasters-os/tree/main/vendor/cognito/modes/auditor
Command: npx skills add https://github.com/iamasters-academy/iamasters-os --skill cognito-auditor

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires Sinapsis, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps in post-mortem analysis, QA of AI outputs, extracting lessons learned, and conducting systematic reviews, enhancing the quality assurance process.

Core Features & Use Cases

Post-mortem Retrospective: Analyze what went well, what didn't, and what was missing post-deployment.
Lessons Learned Extraction: Accurately identify actionable lessons learned for continuous improvement.
Pattern Identification: Detect recurring patterns and anti-patterns in AI outputs.
Integration with Sinapsis: Optionally integrate with Sinapsis to codify lessons as instincts.
Use Case: Ideal for ensuring high-quality AI outputs by systematically reviewing project outputs, identifying issues, and improving future processes.

Quick Start

Run the auditor mode to review the outputs of your project and generate a comprehensive audit report.

Frequently Asked Questions about cognito-auditor

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

FAQPage Schema

How do I conduct a post-mortem analysis on AI outputs?▼

To perform QA for AI outputs, you run the auditor mode to systematically review project outputs against expected quality standards. This process identifies recurring patterns and anti-patterns, generating a comprehensive audit report for continuous improvement.

What is the best way to extract lessons learned from AI project reviews?▼

The best way to extract lessons learned from AI project reviews is through systematic pattern identification on past outputs. This Skill accurately identifies actionable lessons from deployment post-mortems to drive continuous improvement of AI processes.

How does post-mortem retrospective analysis identify anti-patterns in AI outputs?▼

Post-mortem retrospective analysis identifies anti-patterns by systematically reviewing what went wrong and what was missing in AI outputs. This Skill detects recurring failure patterns to ensure high-quality future deployments and process corrections.

Do I need Sinapsis to run AI output quality assurance and lesson extraction?▼

You do not need Sinapsis to run AI output quality assurance, as the Skill works standalone for lesson extraction. Sinapsis is an optional dependency used to codify the extracted lessons as instincts for deeper workflow integration.

Can I integrate extracted lessons learned from AI reviews into my development workflow?▼

Yes, you can integrate extracted lessons learned into your workflow by using Sinapsis. This optional integration allows you to codify the lessons identified during post-mortem analysis as actionable instincts for your AI processes.