Debug errors, trace bugs, find root causes. Investigate why code fails, trace execution paths, troubleshoot unexpected behavior, find why functions return wrong values. NOT for exploring unfamiliar code (use exploring-codebases) or planning changes (use change-planning).
Debug and eliminate fallback/generic-stub replies quickly. Use when you see empty assistant replies, “Thanks for your message…” stubs, or “no specific information available” messages. Produces a minimal reproduction (test or deterministic trace) and pinpoints the fallback source + trigger.
Debug Rust ownership, borrowing, and lifetime errors. Use when encountering borrow checker errors (E0382, E0502, E0597, etc.) or when code won't compile due to ownership issues.
Use before committing code or after major changes to verify performance. Catches expensive operations before they cause frame drops.
Use this skill when users need to collect, manage, or analyze forensic artifacts such as files, memory dumps, Windows Event Logs, Mac Unified Logs, or network packet captures (PCAP) from endpoints.
Use this skill when working with Chrome DevTools, browser debugging, performance profiling, network analysis, JavaScript debugging, CSS inspection, element inspection, console usage, or any web development debugging workflows.
Exploration symbolique du code avec Serena MCP. Utiliser pour naviguer dans le code par symboles (fonctions, classes, hooks), trouver des définitions, et comprendre l'architecture sans lire des fichiers entiers.
Debug Docker containers, images, and infrastructure with systematic diagnostic techniques. This skill provides comprehensive guidance for troubleshooting container exit codes, OOM kills, image build failures, networking issues, volume mount problems, and permission errors. Covers four-phase debugging methodology from quick assessment to deep analysis, essential Docker commands, debug container techniques for minimal images, and platform-specific troubleshooting for Windows, Mac, and Linux.
Analyze code complexity and maintainability including cyclomatic complexity, function length, nesting depth, and cognitive load. Use when reviewing code maintainability, refactoring candidates, or technical debt assessment.
Automatically applies when profiling Python performance. Ensures proper use of profiling tools, async profiling, benchmarking, memory analysis, and optimization strategies.
Systematic bug hunting combining Sherpa's debugging workflow, Julie's execution tracing, and Goldfish's investigation tracking. Activates for bug fixes with methodical reproduction, test capture, fix, and verification. Use when debugging errors or fixing bugs.
웹 애플리케이션의 소스 코드를 분석하고, 런타임 플로우를 시각화하여 인터랙티브 웹 리포트를 생성합니다.(routes → controllers → services → DB) 코드를 처음보는 사람이 이해하기 쉽게 요약과 클릭 가능한 다이어그램을 사용합니다.
Analyzes and optimizes p5.js game performance including FPS drops, particle limits, off-screen culling, object pooling, and memory usage. Use when checking performance, experiencing lag, frame stuttering, or when optimizing for late-game waves with hundreds of entities.
GDB debugging for ESP32-S3 firmware. Use when user asks to debug, gdb, breakpoint, step through, backtrace, info stack, inspect variables, or investigate crashes in ESP32 firmware.
Monitor JavaScript bundle size and execution performance. Use when tracking bundle size, identifying large chunks, or optimizing load performance.
Analyze llm-compact-logger test output and configure enhancements. Use when user shares debug-compact.json or debug-report.json files.
Shows inlining decisions during compilation with call tree and reasons for inline/don't-inline. Use to verify critical calls are inlined, understand why inlining failed (too large, recursive, boundary), and optimize method sizes. Good inlining = better compilation. Recursive functions show 'not inlined' - expected behavior for Bytecode DSL limitation.
Error handling with @julian-i/try-error. Wrap throwable code with tryPromise (async) or tryThrow (sync). Forces reflection on error types, handling location, audience, and recovery.
Python code security analysis, performance optimization, and maintainability assessment
MCP ツールを TypeScript コードとして実行し、トークン消費を最大 98.7% 削減します。Serena MCP ツール呼び出しが必要な場合、大規模データセット処理、コードベース全体への操作、バッチ処理、プライバシーに配慮したデータ処理が必要な場合に使用してください。
Launch the live watch debug monitor TUI to track integration events in real-time
