When should I use the kitt-debug-like-expert skill?

Use this skill when standard troubleshooting fails, for complex logic errors, or when you need a methodical investigation rather than a quick fix.

How does this skill handle different programming languages?

It includes an automated context scan that detects project types like Python, JavaScript, Rust, or Swift and loads specific domain expertise if available in your environment.

What is the 'Verify, Don't Assume' principle?

It is a core rule requiring every hypothesis to be tested and every fix to be validated with evidence before the debugging task is marked as complete.

Does this skill help with my own code biases?

Yes, it specifically encourages skepticism toward code you designed or implemented to overcome cognitive biases about how the system 'should' work.

What is the standard output format for a debugging session?

The skill provides a structured markdown report including the specific issue description, observed evidence, investigation steps, root cause, and verification results.

Expert Debugging Protocol (KITT)

Name: Expert Debugging Protocol (KITT)
Author: utahdws

byutahdws

Herramientas para Desarrolladores

Activates a methodical, evidence-based investigation protocol to identify and resolve complex software bugs using scientific rigor.

Acerca de

This skill transforms Claude into a senior-level debugger, moving beyond quick fixes to perform deep systematic root cause analysis. It implements a rigorous framework involving evidence gathering, hypothesis testing, and multi-stage verification, while proactively identifying domain-specific expertise for platforms like React, Rust, or Swift. It is specifically designed for scenarios where standard troubleshooting has failed, requiring the AI to challenge its own implementation assumptions to find elusive 'heisenbugs' and architectural flaws.

Características Principales

Automated domain detection for context-aware debugging across multiple languages
0 GitHub stars
Cognitive bias mitigation to identify errors in recently written code
Scientific hypothesis testing to isolate root causes without guesswork
Methodical investigation protocol with systematic evidence gathering
Standardized output reports documenting the investigation and verification steps

Casos de Uso

Debugging inconsistent 'heisenbugs' that are difficult to reproduce or isolate
Verifying complex logic changes in large-scale legacy codebases
Conducting deep-dive root cause analysis on mission-critical system failures

Acerca de

Características Principales

Automated domain detection for context-aware debugging across multiple languages
0 GitHub stars
Cognitive bias mitigation to identify errors in recently written code
Scientific hypothesis testing to isolate root causes without guesswork
Methodical investigation protocol with systematic evidence gathering
Standardized output reports documenting the investigation and verification steps

Casos de Uso

Debugging inconsistent 'heisenbugs' that are difficult to reproduce or isolate
Verifying complex logic changes in large-scale legacy codebases
Conducting deep-dive root cause analysis on mission-critical system failures