Debugging

This skill teaches a rigorous 4-phase debugging methodology: Investigation, Pattern Analysis, Hypothesis Testing, and Implementation. It includes practical utilities for condition-based test waiting and test pollution detection, along with documentation on root cause tracing and defense-in-depth validation strategies.

Superpowers is a complete software development workflow library for AI coding assistants. It provides structured skills for test-driven development, systematic debugging, brainstorming, implementation planning, code review, and subagent-driven development. The skills enforce disciplined practices through mandatory workflows and integrate with Claude Code, OpenCode, and Codex platforms.

This skill teaches systematic debugging techniques using the scientific method approach. It covers debugging tools and techniques for JavaScript/TypeScript, Python, and Go, including profiler usage, memory leak detection, binary search debugging with git bisect, and best practices for tracking down elusive bugs in any codebase.

This skill provides extensive patterns and best practices for authentication implementation (JWT, OAuth2, sessions), monorepo management (Turborepo, Nx, Bazel), debugging strategies, E2E testing with Playwright/Cypress, error handling, Git workflows, and code review excellence. It serves as a reference guide for building secure, scalable applications.

Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies

This skill provides technical knowledge about the DWARF debugging standard (versions 3-5) used in compiled binaries. It helps with parsing DWARF debug information using tools like dwarfdump and readelf, answering questions about the DWARF specification, and writing code that interacts with DWARF data using libraries like pyelftools (Python), gimli (Rust), or libdwarf (C/C++).

Claudeception enables Claude Code to build persistent knowledge by extracting reusable skills from debugging sessions and problem-solving. When you discover a non-obvious solution, workaround, or debugging technique, it captures that knowledge as a new skill that can be automatically retrieved in future sessions facing similar problems.

Deep analysis debugging mode for complex issues. Activates methodical investigation protocol with evidence gathering, hypothesis testing, and rigorous verification. Use when standard troubleshooting fails or when issues require systematic root cause analysis.

This skill provides structured templates and best practices for iterative test-driven development using the Ralph Wiggum loop plugin. It guides Claude through self-referential development cycles where the AI iterates on code until tests pass, with clear completion criteria and error classification to distinguish between fixable code issues and environment blockers.

This skill should be used when user asks about "GCloud logs", "Cloud Logging queries", "Google Cloud metrics", "GCP observability", "trace analysis", or "debugging production issues on GCP".

A debugging methodology skill that applies scientific investigation principles through natural conversation. It guides users through systematic bug diagnosis including hypothesis formation, evidence gathering, root cause analysis, and fix verification while maintaining user control over the investigation direction.

Official Sentry plugin that integrates Sentry's debugging and monitoring tools into Claude Code. It provides natural language queries for Sentry issues, automatic analysis of PR comments from Sentry bot, and guided setup for AI monitoring, logging, metrics, and tracing across JavaScript, Python, and Ruby projects.