What is the primary goal of the Learn From Mistakes skill?

The primary goal is to identify the fundamental root cause of AI agent failures and implement high-level prevention strategies, such as code fixes or automated hooks, that make future errors impossible.

How does this skill detect context-related errors?

It integrates token tracking and session history analysis to monitor context window usage, identifying when high token counts or recent compactions lead to logic degradation.

Why does the skill emphasize a prevention hierarchy?

Because documentation is the weakest form of prevention. The hierarchy forces developers to consider stronger fixes like API redesigns, validation hooks, and automated checks first.

What are the three RCA methods used in this skill?

The skill uses 5-Whys for deep logic investigation, Modular Error Taxonomy for classifying agent-specific capability failures, and Causal Barrier Analysis for identifying missing safety mechanisms.

AI Root Cause Analysis & Learning

Name: AI Root Cause Analysis & Learning
Author: cowwoc

bycowwoc

•

Developer Tools

Analyzes AI agent failures using multi-method root cause analysis and context tracking to implement robust, high-level prevention strategies.

The Learn From Mistakes skill provides a structured framework for diagnosing failures in AI agent workflows, specifically optimized for the CAT ecosystem. It moves beyond superficial error reporting by utilizing advanced methodologies like 5-Whys, Modular Error Taxonomy, and Causal Barrier Analysis. By integrating real-time token tracking and context degradation metrics, the skill helps developers distinguish between logical errors and context-limit failures. It enforces a rigorous prevention hierarchy, guiding users to implement high-level fixes like automated hooks and code-level enforcement rather than relying on weak documentation updates.

Key Features

01Prevention quality checks to identify and fix fragile verification patterns

0256 GitHub stars

03Multi-method RCA including 5-Whys, Taxonomy, and Causal Barrier Analysis

04Automated token usage and context degradation tracking

05Mandatory history verification to eliminate AI memory bias

06Seven-level prevention hierarchy for robust system hardening

Use Cases

01Diagnosing why subagents are producing inconsistent or incomplete results

02Identifying context window saturation as a root cause for logic degradation

03Escalating failed documentation-based rules into automated pre-tool hooks

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add cowwoc/cat learn-from-mistakes

For use in Claude.ai and ChatGPT

Download Skill