Découvrez les skills data ai debugging, comparez leurs dépôts et consultez leurs instructions.
Use when asked to find code smells, audit TODOs, remove dead code, clean up unused imports, delete debug statements, or assess code quality. Triggers on: "use tech-debt mode", "tech debt", "code smells", "clean...
Use when asked to document architecture, understand system structure, create diagrams, or analyze component relationships. Focuses on interfaces and high-level design. Triggers on: "use architecture mode", "arc...
Systematic debugging with hypothesis-driven investigation. Use when something is broken, tests are failing, unexpected behavior occurs, or errors need investigation. Triggers on: "this is broken", "debug", "why...
Interactive planning skill for Claude Code. Generates specs, implementation plans, and loop infrastructure through clarifying questions.
Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs. [MAND...
Search through specific provided logs to find specific information.
Break down technical implementation tasks into actionable steps with codebase analysis. Use when the user says "breakdown the task", "plan the implementation" or when you need to decompose complex features, ref...
주간 Claude Code 사용 분석 리포트 생성. 이번 주 모든 세션 로그를 분석하여 프로젝트별 시간 분포, 작업 유형, Jira 이슈 진행 현황 제공. "주간 분석", "Claude 사용 통계", "weekly analytics" 등의 요청 시 자동 적용.
Scan for security vulnerabilities in dependencies, code patterns, and secrets. Uses project-documented tooling where available.
Detect technical debt patterns in code including duplication, complexity, and maintainability issues. Use at phase checkpoints or on-demand to assess code quality.
Agent logs, reasoning, and token tracking. Use when logging interactions, saving reasoning, tracking tokens, or managing events.
Generate comprehensive test coverage reports when reviewing code. Identifies untested code paths and low-coverage areas. Supports Python (pytest-cov), JavaScript (jest), Go (go test -cover), Java (JaCoCo). Use...
Write and execute Python scripts using the IDA Domain API for reverse engineering. Analyze binaries, extract functions, strings, cross-references, decompile code, work with IDA Pro databases (.i64/.idb). Use wh...
Integrate MemBrowse memory tracking into an embedded firmware project. Use when the user wants to set up MemBrowse, add memory analysis GitHub workflows, create membrowse-targets.json for tracking RAM/Flash usa...
A test skill to validate that SessionStart hooks can create symlinks before skill discovery. If you can see this skill, the hook timing works correctly.
AI-powered code review tool that analyzes code for bugs, style issues, and improvements
Unified debugging workflow enforcing root cause protocol. Investigate → Understand → Reason → Fix. Use when debugging bugs, errors, or unexpected behavior.
Capture and review lessons learned from coding sessions. Use to record insights, read past lessons, and improve over time.
Performance profiling and optimization. Use for benchmarking code, analyzing performance, running Lighthouse audits, and finding hotspots.
Debug and investigate code issues using search and AI analysis. Use when stuck on bugs, tracing execution flow, or understanding complex code.
Contextual learning system that remembers patterns and rules from past sessions. Use to get relevant context before tasks, record outcomes, and build a personal playbook of coding patterns.
Generate tests using AI and run test suites. Use for generating unit tests, running coverage reports, and mutation testing.
Ultimate Bug Scanner - scan code for bugs across 7 languages (JS/TS, Python, Go, Rust, Java, C++, Ruby). Use before commits to catch null safety issues, security holes, async bugs, and memory leaks.
Expert guide for WebGL API development including 3D graphics, shaders (GLSL), rendering pipeline, textures, buffers, performance optimization, and canvas rendering. Use when working with WebGL, 3D graphics, can...
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.