আপনার এজেন্টের জন্য উপযুক্ত সক্ষমতা খুঁজে নিন।
Deep CSS analysis tool for understanding specificity, cascade order, inheritance chains, and computed styles. Visualizes CSS complexity and suggests simplifications. Use when debugging specificity issues or understanding CSS behavior.
This skill should be used when the user asks to "debug notebook", "inspect notebook outputs", "find notebook error", "read traceback from ipynb", "why did notebook fail", or needs to understand runtime errors in executed Jupyter notebooks from any source (marimo, jupytext, papermill).
[30] ANALYZE. Understand how changes impact the system — what's the core, what's affected, what depends on what. Use when planning changes, analyzing systems, debugging issues, or anytime you need to see the full picture of cause and effect. Triggers on "what's affected", "impact analysis", "dependencies", "scope mapping", or when you need to understand ripple effects.
Diagnoses JavaScript errors, asset loading failures, p5.js conflicts, game logic bugs, and performance issues. Use when encountering errors, crashes, console warnings, sprites not loading, enemies stuck, towers not working, or any unexpected behavior.
Use when working on the Miden compiler (`cargo miden`, `cargo-miden`) and its integration test suite, debugging compiler issues or failing tests, or implementing compiler changes and you need to inspect intermediate artifacts. Covers `MIDENC_EMIT` (the environment-variable equivalent of `--emit`) for emitting WAT/HIR/MASM (and related outputs), plus `MIDENC_EMIT_MACRO_EXPAND` for dumping macro-expanded Rust via `cargo expand` for integration-test fixtures.
Systematic debugging methodology that eliminates guessing and speculation. Add instrumentation to gather specific data that fully explains the problem. Evidence before hypothesis. Observation before solution.
Produce a post-incident analysis capturing timeline, root causes, impact, and corrective actions.
Evidence-based debugging with Iron Law discipline. Instrument before guessing, trace before theorizing. Use when encountering any bug, test failure, or unexpected behavior - before proposing fixes.
Deep analysis debugging mode for complex issues. Activates methodical investigation protocol with evidence gathering, hypothesis testing, and rigorous verification. Use when: standard troubleshooting fails, systematic root cause analysis needed, complex bugs, mysterious errors.
Use when profiling Python code, optimizing performance, implementing async patterns, or working with concurrent I/O - covers cProfile, line_profiler, asyncio patterns, and multiprocessing for CPU-bound tasks (plugin:dev-python@pproenca)
瞬作のWeb自動検証スキル。Chrome DevTools MCPを使用してWebアプリの画面キャプチャ、要素確認、操作テストを自動実行する。実装完了後、デザイン変更後、バグ修正後などに呼び出される。
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.
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.
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.
Python code security analysis, performance optimization, and maintainability assessment
