What is the 'scientific method' approach in this skill?

It involves documenting the current state, mapping the system path, forming falsifiable hypotheses, and testing each variable individually to ensure the solution is evidence-backed.

How does it help with my own cognitive biases?

The skill follows a core principle of treating your own code with more skepticism than unfamiliar code, forcing a re-examination of assumptions about how the implementation 'should' work versus how it actually behaves.

How does it handle different programming languages?

The skill performs a context scan upon invocation to detect project files like Cargo.toml, package.json, or xcodeproj, loading specific expertise for frameworks like Node.js, Rust, Swift, and more.

When should I activate the Expert Debugger skill?

This skill is designed for complex scenarios where standard troubleshooting has failed, or for issues that require a systematic, evidence-based deep dive into the root cause.

Expert Debugger

Name: Expert Debugger
Author: shishiv

byshishiv

Security & Testing

Activates a methodical investigation protocol for complex issues using systematic hypothesis testing and rigorous evidence gathering.

About

The Expert Debugger skill transforms Claude into a senior systems engineer specialized in resolving elusive software bugs that defy standard troubleshooting. It implements a scientific investigation framework that prioritizes evidence gathering, domain-specific context scanning, and formal hypothesis testing. By explicitly addressing cognitive biases—treating authored code with higher skepticism than unfamiliar code—it ensures that root causes are accurately identified and verified through a rigorous checklist before any permanent changes are applied.

Key Features

0 GitHub stars
Rigorous verification patterns to prevent regressions and side effects
Cognitive bias mitigation for self-authored code analysis
Systematic scientific protocol for root cause analysis
Hypothesis-driven investigation to eliminate 'drive-by' fixes
Automatic domain detection for specialized stacks like React, Rust, and Swift

Use Cases

Ensuring architectural changes are validated against a strict evidence-based checklist
Performing deep-dive investigations into complex legacy codebases or unfamiliar logic
Troubleshooting intermittent or 'impossible' production bugs that lack clear error messages

About

Key Features

0 GitHub stars
Rigorous verification patterns to prevent regressions and side effects
Cognitive bias mitigation for self-authored code analysis
Systematic scientific protocol for root cause analysis
Hypothesis-driven investigation to eliminate 'drive-by' fixes
Automatic domain detection for specialized stacks like React, Rust, and Swift

Use Cases

Ensuring architectural changes are validated against a strict evidence-based checklist
Performing deep-dive investigations into complex legacy codebases or unfamiliar logic
Troubleshooting intermittent or 'impossible' production bugs that lack clear error messages