debugging
This Claude Code skill provides four systematic debugging methodologies for investigating and fixing software bugs. It includes root cause tracing to identify where invalid data originated, defense-in-depth validation to prevent bugs across multiple layers, systematic investigation frameworks that prioritize root cause analysis before attempting fixes, and verification protocols to confirm solutions work. Use this skill when facing production bugs, errors deep in stack traces, recurring issues, or when verifying that fixes actually resolve problems rather than treating symptoms.
git clone --depth 1 https://github.com/mrgoonie/claudekit-skills /tmp/debugging && cp -r /tmp/debugging/.claude/skills/debugging ~/.claude/skills/debuggingSKILL.md
# Debugging Skills A collection of systematic debugging methodologies that ensure thorough investigation before attempting fixes. ## Available Sub-Skills ### Systematic Debugging **Location:** `systematic-debugging/SKILL.md` Four-phase debugging framework: Root Cause Investigation → Pattern Analysis → Hypothesis Testing → Implementation. The iron law: NO FIXES WITHOUT ROOT CAUSE INVESTIGATION FIRST. ### Root Cause Tracing **Location:** `root-cause-tracing/SKILL.md` Trace bugs backward through the call stack to find the original trigger. Don't fix symptoms - find where invalid data originated and fix at the source. ### Defense-in-Depth Validation **Location:** `defense-in-depth/SKILL.md` Validate at every layer data passes through to make bugs structurally impossible. Four layers: Entry Point → Business Logic → Environment Guards → Debug Instrumentation. ### Verification Before Completion **Location:** `verification-before-completion/SKILL.md` Run verification commands and confirm output before claiming success. The iron law: NO COMPLETION CLAIMS WITHOUT FRESH VERIFICATION EVIDENCE. ## When to Use - **Bug in production** → Start with systematic-debugging - **Error deep in stack trace** → Use root-cause-tracing - **Fixing a bug** → Apply defense-in-depth after finding root cause - **About to claim "done"** → Use verification-before-completion ## Quick Dispatch | Symptom | Sub-Skill | |---------|-----------| | Test failure, unexpected behavior | systematic-debugging | | Error appears in wrong location | root-cause-tracing | | Same bug keeps recurring | defense-in-depth | | Need to confirm fix works | verification-before-completion | ## Core Philosophy > "Systematic debugging is FASTER than guess-and-check thrashing." From real debugging sessions: - Systematic approach: 15-30 minutes to fix - Random fixes approach: 2-3 hours of thrashing - First-time fix rate: 95% vs 40%
Manage MCP (Model Context Protocol) server integrations - discover tools/prompts/resources, analyze relevance for tasks, and execute MCP capabilities. Use when need to work with MCP servers, discover available MCP tools, filter MCP capabilities for specific tasks, execute MCP tools programmatically, or implement MCP client functionality. Keeps main context clean by handling MCP discovery in subagent context.
Stage all files and create a commit.
Stage, commit and push all code in the current branch
Create a pull request
Create a new agent skill
Utilize tools of Model Context Protocol (MCP) servers
Create aesthetically beautiful interfaces following proven design principles. Use when building UI/UX, analyzing designs from inspiration sites, generating design images with ai-multimodal, implementing visual hierarchy and color theory, adding micro-interactions, or creating design documentation. Includes workflows for capturing and analyzing inspiration screenshots with chrome-devtools and ai-multimodal, iterative design image generation until aesthetic standards are met, and comprehensive design system guidance covering BEAUTIFUL (aesthetic principles), RIGHT (functionality/accessibility), SATISFYING (micro-interactions), and PEAK (storytelling) stages. Integrates with chrome-devtools, ai-multimodal, media-processing, ui-styling, and web-frameworks skills.
Process and generate multimedia content using Google Gemini API. Capabilities include analyze audio files (transcription with timestamps, summarization, speech understanding, music/sound analysis up to 9.5 hours), understand images (captioning, object detection, OCR, visual Q&A, segmentation), process videos (scene detection, Q&A, temporal analysis, YouTube URLs, up to 6 hours), extract from documents (PDF tables, forms, charts, diagrams, multi-page), generate images (text-to-image, editing, composition, refinement). Use when working with audio/video files, analyzing images or screenshots, processing PDF documents, extracting structured data from media, creating images from text prompts, or implementing multimodal AI features. Supports multiple models (Gemini 2.5/2.0) with context windows up to 2M tokens.