Discover lifestyle debugging skills, compare source repositories and read the instructions for each workflow.
This skill should be used when the user wants to run code quality checks (linting, formatting, type checking, tests) on the lazyopencode project. Use this skill when asked to "run quality gates", "check the cod...
Fast codebase search via WarpGrep (20x faster than grep)
Use when encountering any bug, test failure, or unexpected behavior (including race conditions, deadlocks, concurrency issues) - four-phase framework (root cause investigation, pattern analysis, hypothesis test...
Debug memory leaks, profile memory usage, optimize allocations. Use when heap grows unexpectedly, OOM errors occur, or profiling shows memory bottleneck. Covers C++ (Valgrind, ASAN, RAII), Python (tracemalloc,...
Automatically invoked when analyzing code quality, refactoring, or maintainability. Triggers on mentions of "code smell", "refactor", "code quality", "technical debt", "complexity", "maintainability", "clean co...
Stimulus and Turbo patterns for Rails frontend development. Automatically invoked when working with Hotwire, Stimulus controllers, Turbo frames/streams, progressive enhancement, or modern Rails JavaScript. Trig...
Comprehensive distributed tracing with Jaeger, Zipkin, OpenTelemetry, correlation IDs, and span design.
Performance testing patterns including load testing, stress testing, benchmarking, and profiling for optimal application performance
Applies AI-first development patterns for 10-100x productivity through specification-driven generation and leverage multiplication. Use when developing features with AI assistance or optimizing development work...
AI agent performs systematic root cause analysis using 5 Whys, Fishbone diagrams, and evidence-based investigation. Use when debugging, conducting post-mortems, or investigating incidents.
AI agent follows a 5-phase debugging process with reproduction, isolation, hypothesis testing, and root cause resolution. Use when investigating bugs, troubleshooting issues, or hunting errors.
Production observability with structured logging, metrics collection, distributed tracing, and alerting
Performs browser performance profiling with Lighthouse, Core Web Vitals, and DevTools analysis. Use when auditing page performance, optimizing Core Web Vitals, analyzing bundle sizes, or implementing performanc...
AI agent identifies and fixes common testing anti-patterns that lead to flaky, slow, or unmaintainable test suites. Use when reviewing tests, debugging test failures, or improving test quality.
1600+ lines of performance optimization mastery - profiling, rendering, memory, network, battery, APK size with production-ready code examples.
Complete sorting algorithm implementations including quick sort, merge sort, and binary search with complexity analysis and production-ready code.
Expert in Python development with focus on best practices, testing, and modern Python features
Aggressive session accountability via Obsidian timeline. PROACTIVELY log decisions, commits, ideas, and blockers AS THEY HAPPEN. Triggers on any significant action.
Python debugging techniques, pdb, and IDE debugging tools
Systematic debugging methodology emphasizing root cause analysis over quick fixes. Use when debugging complex issues, investigating production failures, or avoiding symptom-based patches in favor of understandi...
Test quality inspection framework for reviewing test coverage, identifying gaps, and ensuring comprehensive validation. Use when reviewing test suites, performing QA inspections, or establishing testing quality...
Rust code review patterns from corrode.dev (16 articles)
Full-stack performance analysis, optimization patterns, monitoring strategies, React 19 concurrent features, and bundle analysis for 2025+ development
React 19 best practices, hooks, state management and data fetching. Use when writing components, hooks, managing state, handling async operations or reviewing React code.
The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.