Root Cause Tracing
About
This skill systematically traces bugs backward through the call stack to identify their original triggers rather than just fixing symptoms. It's designed for use when errors occur deep in execution with unclear data origins or long call chains. The approach involves observing symptoms, finding immediate causes, and repeatedly asking "what called this" until reaching the source.
Quick Install
Claude Code
Recommendednpx skills add bobmatnyc/claude-mpm -a claude-code/plugin add https://github.com/bobmatnyc/claude-mpmgit clone https://github.com/bobmatnyc/claude-mpm.git ~/.claude/skills/Root Cause TracingCopy and paste this command in Claude Code to install this skill
GitHub Repository
Related Skills
smart-bug-fix
TestingThis skill provides an automated bug-fixing workflow that performs root cause analysis, generates fixes with Codex, and validates changes with testing. It's designed for developers to systematically debug issues by combining multi-model reasoning with iterative validation. Use it when you need a structured, AI-assisted approach to diagnose and resolve complex bugs.
sherlock-review
OtherSherlock Review performs evidence-based code investigation using deductive reasoning to verify implementation claims and find root causes. It systematically analyzes code, tests, and history to determine what actually happened versus what was reported. Use this skill for validating fixes, investigating bugs, or conducting rigorous code reviews.
when-debugging-ml-training-use-ml-training-debugger
OtherThis skill helps developers diagnose and fix common machine learning training issues like loss divergence, overfitting, and slow convergence. It provides systematic debugging to identify root causes and generate fixes for training problems. Use it when you encounter poor validation performance or training instability to restore model convergence.
systematic-debugging
OtherThis skill provides a structured four-phase debugging framework to replace random code changes with systematic problem diagnosis. It helps developers methodically investigate bugs, errors, and unexpected behavior by forming specific hypotheses and testing single changes. Use it when under time pressure or when quick fixes seem obvious to ensure reliable problem resolution.
