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概要と使いどころ
Battle-tested Playwright patterns for writing and debugging reliable E2E, API, component, visual, accessibility, and security tests. Use when you need locator strategy, assertions, fixtures, network mocking, auth flows, trace debugging, or framework recipes for React, Next.js, Vue, and Angular. TypeScript and JavaScript.
日本語の概要は準備中です。原文の説明を表示しています。
Battle-tested Playwright patterns for writing and debugging reliable E2E, API, component, visual, accessibility, and security tests. Use when you need locator strategy, assertions, fixtures, network mocking, auth flows, trace debugging, or framework recipes for React, Next.js, Vue, and Angular. TypeScript and JavaScript.
日本語の概要は準備中です。原文の説明を表示しています。
Bringing up a complete ROS2 system on a robot's onboard computer: systemd services, launch file composition, ordered startup, and production monitoring. Use this skill when configuring a robot to start ROS2 nodes on boot, writing systemd unit files for ROS2 launch, composing layered launch files for full robot stacks, setting up watchdog monitoring, configuring udev rules for deterministic device naming, or debugging boot-time race conditions. Trigger whenever the user mentions robot bringup, robot startup, systemd for ROS2, ROS2 on boot, launch file composition, robot boot sequence, udev rules for cameras or serial ports, automatic restart for ROS2 nodes, network configuration for multi-machine ROS2, log rotation for robots, graceful shutdown of robot stacks, or SSH-based remote debugging. Also trigger for sourcing workspaces in systemd, ordered startup with health checks, or running ROS2 systems as long-running production services. Covers systemd on Ubuntu 22.04/24.04 with ROS2 Humble, Iron, and Jazzy.
日本語の概要は準備中です。原文の説明を表示しています。
Debug a failing test or build error in WooCommerce iOS
日本語の概要は準備中です。原文の説明を表示しています。
Systematic debugging with MCP integration, auto-invoke from qa-commit, Phase 7 Harden
日本語の概要は準備中です。原文の説明を表示しています。
Provides error classification, recovery, and graceful-degradation patterns. Use when implementing error handling or debugging resilience failures in any skill.
日本語の概要は準備中です。原文の説明を表示しています。
Debug regex patterns with visual breakdowns, plain English explanations, test case generation, and flavor conversion. Use when user needs help with regular expressions or pattern matching.
日本語の概要は準備中です。原文の説明を表示しています。
Debug Docker containers, fix Dockerfile issues, optimize images, and troubleshoot docker-compose. Use when having Docker problems, container issues, or optimizing Docker builds.
日本語の概要は準備中です。原文の説明を表示しています。
Use XcodeBuildMCP to build, run, launch, and debug the current iOS project on a booted simulator. Trigger when asked to run an iOS app, interact with the simulator UI, inspect on-screen state, capture logs/console output, or diagnose runtime behavior using XcodeBuildMCP tools.
日本語の概要は準備中です。原文の説明を表示しています。
Design, implement, debug, and optimize API integrations with expert-level patterns for REST, GraphQL, webhooks, and authentication flows.
日本語の概要は準備中です。原文の説明を表示しています。
Debug LangChain and LangGraph agents by fetching execution traces from LangSmith Studio. Use when debugging agent behavior, investigating errors, analyzing tool calls, checking memory operations, or examining agent performance. Automatically fetches recent traces and analyzes execution patterns. Requires langsmith-fetch CLI installed.
日本語の概要は準備中です。原文の説明を表示しています。
Four-phase debugging framework for any technical issue. Use when encountering bugs, errors, or unexpected behavior. Prevents random fix attempts.
日本語の概要は準備中です。原文の説明を表示しています。
Debug Fastly Compute deployments that appear successful but return stale/wrong responses. Use when: (1) fastly compute publish succeeds but version check shows old version, (2) new endpoints return 404 after deployment, (3) cache-busted requests work but regular requests fail, (4) fastly domain list doesn't show your custom domain. Covers edge propagation timing, cached error responses, and domain management API differences.
日本語の概要は準備中です。原文の説明を表示しています。
Debug Fastly Compute services where code changes don't affect behavior due to multi-service routing architecture. Use when: (1) deployed changes have no effect, (2) unexpected redirects persist after code updates, (3) DNS resolves to Fastly but responses don't match deployed code, (4) `fastly service list` shows multiple related services. The root cause is often a routing/gateway service intercepting requests before they reach your content service.
日本語の概要は準備中です。原文の説明を表示しています。
Run and debug Flutter E2E integration tests that exercise the real app against a local Docker backend (no mocks). Use when running E2E tests, debugging failures, or working on the local harness.
日本語の概要は準備中です。原文の説明を表示しています。
Fix misleading HTTP response header values when debugging with curl -I or curl -sI. Use when: (1) Response headers differ between curl testing and actual browser/client behavior, (2) Cache-Control or other headers show unexpected values despite correct middleware code, (3) Server-side middleware that only applies to GET requests appears to not work when testing with curl -I. The -I flag sends HEAD requests, and middleware that checks for GET method will skip processing, returning handler-level headers instead of middleware-overridden ones.
日本語の概要は準備中です。原文の説明を表示しています。
Automatically reduces bug-triggering test cases to minimal form while preserving the failure. Use when debugging with large, complex test cases that need simplification, reproducing bugs with minimal examples, or creating regression tests from verbose failure scenarios. Takes a failing test and systematically removes unnecessary inputs, steps, assertions, and code using delta debugging and other reduction algorithms. Supports unit tests, integration tests, input files, and multiple programming languages (Python, JavaScript, Java, C/C++).
日本語の概要は準備中です。原文の説明を表示しています。
Instrument programs (Python, C/C++, Java) to capture snapshots of key program states at runtime, including variables, memory, and call stacks. Use when you need to debug complex issues, reproduce test cases, prepare traces for formal verification, or analyze program execution. Supports manual instrumentation points, automatic function/method instrumentation, and conditional triggers. Outputs structured JSON snapshots for debugging, replay, and verification workflows.
日本語の概要は準備中です。原文の説明を表示しています。
Collect, normalize, and structure execution traces from instrumented programs (strace, ltrace) into JSON format for downstream analysis. Use when working with system call traces, library call traces, or execution logs that need to be analyzed for debugging, test case reproduction, or verification. Supports parsing strace/ltrace output, filtering noise, extracting debug information, and preparing traces for bug analysis or reproduction workflows.
日本語の概要は準備中です。原文の説明を表示しています。
Instruments programs to capture execution traces specifically for reproducing reported bugs, enabling consistent replay and diagnosis of failures. Use this skill when you need to reproduce a bug, capture execution traces for debugging, instrument code to record program behavior, generate replay scripts for bug reproduction, diagnose hard-to-reproduce failures, or perform deterministic replay of program execution. Triggers when users ask to instrument code for tracing, capture execution traces, reproduce bugs, generate replay scripts, or enable deterministic debugging.
日本語の概要は準備中です。原文の説明を表示しています。
Locate root causes of failing regression tests by analyzing code changes, error messages, and test dependencies. Use when regression tests start failing after code changes, investigating test failures in CI/CD, debugging flaky tests, or understanding why previously passing tests now fail. Analyzes git diffs, stack traces, test output, and dependency changes to produce structured markdown reports ranking likely causes. Triggers when users ask to find why tests are failing, debug regression failures, investigate test breakage, or analyze failing test suites.
日本語の概要は準備中です。原文の説明を表示しています。
Instruments programs to record execution information for deterministic replay debugging. Use when debugging hard-to-reproduce bugs (race conditions, timing issues, intermittent failures, heisenbugs), reproducing production failures, or analyzing complex execution sequences. Records non-deterministic events (I/O, threading, randomness, time) to enable exact replay of program executions. Supports Python, JavaScript, Java, and C/C++ with both custom instrumentation and existing replay tools.
日本語の概要は準備中です。原文の説明を表示しています。
Debug proof failures using counterexamples from Nitpick (Isabelle) or QuickChick (Coq) to identify specification errors, missing preconditions, and proof strategy issues. Use when: (1) A proof attempt fails and you need to understand why, (2) Counterexamples are generated by Nitpick or QuickChick, (3) Specifications may be incorrect or incomplete, (4) Theorems need validation before proving, (5) Missing preconditions or lemmas need identification, or (6) Proof failures need explanation and correction suggestions. Supports both Isabelle/HOL and Coq equally.
日本語の概要は準備中です。原文の説明を表示しています。
Explain why counterexamples violate specifications by analyzing formal specifications (temporal logic, invariants, pre/postconditions, code contracts), informal requirements (user stories, acceptance criteria), test specifications (assertions, property-based tests), and providing step-by-step traces showing state changes, comparing expected vs actual behavior, identifying root causes, and assessing violation impact. Use when debugging test failures, understanding model checker output, explaining runtime assertion violations, analyzing static analysis warnings, or teaching specification concepts. Produces structured markdown explanations with traces, comparisons, state diagrams, and cause chains. Triggers when users ask why something failed, explain a violation, understand a counterexample, debug a specification, or analyze why a test fails.
日本語の概要は準備中です。原文の説明を表示しています。