What is the 'Scientific Method' of debugging included in this skill?

It is a five-step process: Observe actual behavior, Hypothesize the cause, Experiment with a test, Analyze the results, and Repeat until the root cause is verified.

How does this skill help with intermittent or flaky bugs?

It provides a structured strategy for 'flaky' issues, focusing on extensive logging, race condition analysis, and timing dependencies to capture elusive behaviors that are hard to reproduce.

Which programming languages are supported by this skill?

While the core debugging principles are language-agnostic, the skill includes specific implementations, tool configurations, and snippets for JavaScript, TypeScript, Python, and Go.

Does it provide guidance for debugging in production environments?

Absolutely. It outlines safe investigation practices for production, including evidence gathering through error tracking (like Sentry), log analysis, and local reproduction using anonymized data.

Can this skill help with application performance issues?

Yes, it includes specialized patterns for performance debugging, including instructions for profiling tools, measuring bottlenecks, and identifying common culprits like N+1 queries or unnecessary re-renders.

Debugging Strategies Claude Code Skill

概要

The Debugging Strategies skill transforms the often-frustrating process of bug hunting into a structured, scientific workflow. It provides Claude with a comprehensive framework for reproducing elusive issues, gathering critical environmental data, and forming testable hypotheses. By leveraging language-specific tools for JavaScript, Python, and Go, alongside advanced techniques like binary search debugging and memory leak detection, this skill ensures that root causes are addressed rather than just symptoms, making it indispensable for investigating performance bottlenecks, production crashes, and intermittent failures.

主な機能

110 GitHub stars
Systematic four-phase troubleshooting workflow: Reproduce, Gather, Hypothesize, and Test
Specialized strategy patterns for handling intermittent race conditions and production-only bugs
Memory leak detection and performance profiling methodologies for modern runtimes
Multi-language debugging support for JavaScript, TypeScript, Python, and Go
Advanced root cause analysis techniques including binary search (git bisect) and differential debugging

ユースケース

Identifying and fixing memory leaks or performance bottlenecks in high-traffic applications
Analyzing production crash dumps and stack traces to perform rapid root cause analysis
Investigating and resolving intermittent race conditions in distributed systems or async code

概要

主な機能

110 GitHub stars
Systematic four-phase troubleshooting workflow: Reproduce, Gather, Hypothesize, and Test
Specialized strategy patterns for handling intermittent race conditions and production-only bugs
Memory leak detection and performance profiling methodologies for modern runtimes
Multi-language debugging support for JavaScript, TypeScript, Python, and Go
Advanced root cause analysis techniques including binary search (git bisect) and differential debugging

ユースケース

Identifying and fixing memory leaks or performance bottlenecks in high-traffic applications
Analyzing production crash dumps and stack traces to perform rapid root cause analysis
Investigating and resolving intermittent race conditions in distributed systems or async code

Systematic Debugging Strategies

Systematic Debugging Strategies

概要

主な機能

ユースケース

概要

主な機能

ユースケース