How does the Discover Debugging skill activate?

The skill activates automatically whenever Claude Code detects you are working on debugging, profiling, memory management, or using tools like GDB and LLDB.

What is progressive loading in this context?

Progressive loading is a strategy where a gateway skill loads first to save context, and more detailed skill indices or specific debugging files are loaded only as needed.

Is this skill suitable for production debugging?

Yes, it includes specialized logic for production-specific debugging and distributed systems to help resolve issues in live environments safely.

Can I manually view the full list of available debugging workflows?

Yes, you can run 'cat ~/.claude/skills/debugging/INDEX.md' within Claude Code to see detailed descriptions and usage triggers for every sub-skill.

What specific debugging areas are covered?

It covers 14 distinct areas including browser devtools, concurrency, containers, core dumps, network debugging, Python-specific debugging, and remote systems.

Debugging Discovery & Diagnostics

Name: Debugging Discovery & Diagnostics
Author: rand

byrand

•

Seguridad y Pruebas

Streamlines the identification and resolution of software bugs by automatically loading specialized debugging and profiling skills.

Acerca de

The Discover Debugging skill acts as an intelligent gateway for Claude Code, automatically activating when it detects tasks involving troubleshooting, performance analysis, or memory management. It provides a structured, progressive loading system for 14 specialized sub-skills, ranging from GDB and LLDB fundamentals to advanced production-level distributed systems debugging. By serving as a context-efficient entry point, it ensures that Claude utilizes the most relevant domain-specific knowledge for complex diagnostics while keeping the conversation focused and performant.

Características Principales

Access to 14 specialized sub-skills for diverse development environments
Deep integration with GDB, LLDB, and performance profiling tools
57 GitHub stars
Automatic activation based on debugging keywords and task context
Progressive loading architecture to optimize context window usage
Standardized workflows for memory leaks, core dumps, and concurrency issues

Casos de Uso

Analyzing and resolving race conditions in distributed systems
Identifying and fixing memory leaks in complex C++ or Python applications
Optimizing application performance using advanced profiling and browser devtools

Acerca de

Características Principales

Access to 14 specialized sub-skills for diverse development environments
Deep integration with GDB, LLDB, and performance profiling tools
57 GitHub stars
Automatic activation based on debugging keywords and task context
Progressive loading architecture to optimize context window usage
Standardized workflows for memory leaks, core dumps, and concurrency issues

Casos de Uso

Analyzing and resolving race conditions in distributed systems
Identifying and fixing memory leaks in complex C++ or Python applications
Optimizing application performance using advanced profiling and browser devtools