「debug」の検索結果

Automatically generate clear, actionable issue reports from failing tests and repository analysis. Analyze test failures to understand expected vs. actual behavior, identify affected code components, and produce well-structured Markdown reports suitable for GitHub Issues or similar trackers. Use when a test fails, when debugging issues, or when the user asks to create an issue report, generate a bug report, or document a test failure.

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。

Explains test failures and provides actionable debugging guidance. Use when tests fail (unit, integration, E2E), builds fail, or code throws errors. Analyzes error messages, stack traces, and test output to identify root causes and suggest concrete fixes. Handles pytest, jest, junit, mocha, vitest, selenium, cypress, playwright, and other testing frameworks across Python, JavaScript/TypeScript, Java, Go, and other languages.

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 performs git bisect to identify the first bad commit that introduced a bug or failure. Use when debugging regressions, tracking down when a test started failing, or identifying which commit broke functionality. Handles flaky tests with retry logic and provides comprehensive reports with bisect logs and confidence levels.

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 code to support efficient git bisect by producing deterministic pass/fail signals and concise runtime summaries for each tested commit. Use when debugging regressions with git bisect, automating bisect workflows, creating bisect test scripts, handling flaky tests during bisection, or needing clear exit codes and logging for automated bisect runs. Helps identify the exact commit that introduced a bug through automated testing.

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。

Generate concrete counterexamples when formal verification, assertions, or specifications fail. Use this skill when debugging failed proofs, understanding why verification fails, creating minimal reproducing examples, analyzing assertion violations, investigating invariant breaks, or diagnosing specification mismatches. Produces concrete input values, execution traces, and state information that demonstrate the failure.

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 instruments source code to collect runtime information such as function calls, branch decisions, variable values, and execution traces while preserving original program semantics. Use when users need to: (1) Add logging or tracing to code for debugging, (2) Collect runtime execution data for analysis, (3) Monitor function calls and control flow, (4) Track variable values during execution, (5) Generate execution traces for testing or profiling. Supports Python, Java, JavaScript, and C/C++ with configurable instrumentation levels.

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。

Generate code fixes and patches from bug reports, failing test cases, error messages, and stack traces. Use this skill when debugging code, fixing test failures, addressing GitHub issues, resolving runtime errors, or patching security vulnerabilities. Analyzes the bug context, identifies root causes, and generates precise code patches with explanations and validation steps.

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。

Identify the precise location of bugs in source code, modules, and systems. Use this skill when debugging applications, investigating test failures, analyzing error reports, tracing runtime issues, or performing root cause analysis. Analyzes stack traces, error messages, failing tests, and code patterns to pinpoint buggy functions, classes, files, or modules with confidence rankings and supporting evidence.

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 repair buggy code and generate comprehensive tests for Python, Java, and C++ programs. Use when users need to fix logic errors or runtime errors in functions, modules, or repositories. Accepts specifications via natural language descriptions, existing test cases, or input/output examples. Generates corrected code, creates or updates tests to verify correctness and prevent regressions, and produces a detailed report explaining the bug, fix, and testing strategy. Triggers on requests like "fix this bug", "repair this code", "debug this function", or "this code is broken".

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 and fix issues in a GRACE 4 project using .grace semantic navigation, assertions, and verification evidence.

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。

Production-grade TypeScript reference for React & Next.js frontend development. Covers type narrowing, component Props, generic hooks, discriminated unions, as const, satisfies, Zod validation, TanStack Query, server/client boundaries, forms, state management, performance, accessibility, debugging, and code review. Use when the user works with TypeScript in React or Next.js: type errors, Props design, generics, API typing, SSR/CSR boundaries, hydration issues, form validation, state management, performance, or code review. Also use for "how should I type this?", "why does this type error happen?", or any architectural decision involving TypeScript in a frontend context.

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 for writing, reviewing, debugging, or testing Pandera schemas and runtime validation for Polars DataFrame or LazyFrame pipelines installed with pandera[polars]. Trigger on pandera.polars DataFrameSchema, DataFrameModel, Column, Field, Check, PolarsData, decorators, coercion, strictness, lazy error collection, and validation-depth decisions. Do not use for pandas-backed Pandera, Pydantic object models, Polars transformations without Pandera, static dataframe typing alone, or generic data-quality platforms.

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 for creating, reviewing, or debugging GitHub Copilot reusable prompt files and slash commands in Visual Studio Code, including .github/prompts/*.prompt.md, inputs, context links, agent selection, and tool selection. Do not use for always-on rules, multi-step Agent Skills, custom personas, or VS Code extension commands.

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。

Kaggle-first end-to-end competition workflow for scored submissions. Use when Codex must run Kaggle or competitive ML workflows through scored submission, including code competitions, validation, metrics, policy-safe public notebook/discussion intel, tabular/text/image modeling, tuning, ensembling/stacking, proactive multi-notebook architectures, producer notebooks that train models and export private Kaggle artifact datasets, downstream consumer notebooks, Kaggle GPU offload, kagglehub access, hidden-test debugging, and public score retrieval.

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 for writing, reviewing, debugging, testing, or optimizing Python code that uses PyArrow arrays, schemas, Tables, RecordBatches, RecordBatchReaders, compute kernels, Dataset scans, Parquet, or Arrow IPC and C data interfaces. Trigger on schema drift, null/type semantics, chunking, batch streaming, predicate projection pushdown, conversion, and memory ownership. Do not use for pandas-only transformations, Polars-only expressions, generic SQL, Arrow Flight service design, C++/Rust Arrow, or Parquet work that does not use PyArrow.

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 reverse engineering techniques. Use when the main job is to understand how a compiled, obfuscated, packed, or virtualized target works before exploiting or solving it, including binaries, APKs, WASM, firmware, custom VMs, bytecode, malware-like loaders, and anti-debug or anti-analysis logic. Do not use it when the vulnerability is already understood and the remaining task is exploitation; use pwn instead. Do not use it for pure web workflows, log or disk forensics, or standalone crypto problems unless reversing the implementation is the real blocker.

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。

Production-grade Python and AI engineering standards. Use for ANY Python work — FastAPI services, LangChain/LangGraph agents, RAG pipelines, LLM API integrations, document processing, or data scripts — whether writing new code, reviewing, refactoring, or debugging. Enforces typed Pydantic boundaries, structured errors and logging, LLM call timeouts/retries/output validation, secret management, async correctness, and tested code.

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。

AI-powered Claude Code skill that performs deep code review using formal logic and reasoning frameworks to detect bugs, anti-patterns, and security risks beyond what linters catch.

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。

| Production-tested setup for Tailwind CSS v4 with shadcn/ui, Vite, and React. Use when: initializing React projects with Tailwind v4, setting up shadcn/ui, implementing dark mode, debugging CSS variable issues, fixing theme switching, migrating from Tailwind v3, or encountering color/theming problems. Covers: @theme inline pattern, CSS variable architecture, dark mode with ThemeProvider, component composition, vite.config setup, common v4 gotchas, and production-tested patterns.

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。

| Turborepo monorepo build system guidance. Triggers on: turbo.json, task pipelines, dependsOn, caching, remote cache, the "turbo" CLI, --filter, --affected, CI optimization, environment variables, internal packages, monorepo structure/best practices, and boundaries. Use when user: configures tasks/workflows/pipelines, creates packages, sets up monorepo, shares code between apps, runs changed/affected packages, debugs cache, or has apps/packages directories.

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。

| Nuxt 4 data management: composables, data fetching with useFetch/useAsyncData, and state management with useState and Pinia. Use when: creating custom composables, fetching data with useFetch or useAsyncData, managing global state with useState, integrating Pinia, debugging reactive data issues, or implementing SSR-safe state patterns.

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。

| Nuxt 5 production optimization: hydration, performance, testing with Vitest, deployment to Cloudflare/Vercel/Netlify, and migration from Nuxt 4. Use when: debugging hydration mismatches, optimizing performance and Core Web Vitals, writing tests with Vitest, deploying to Cloudflare Pages/Workers/Vercel/Netlify, or migrating from Nuxt 4 to Nuxt 5.

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。

Nuxt 5 data management with useFetch, useAsyncData, useState, and Pinia. Use when creating composables, fetching data, managing state, or debugging reactive/SSR data issues.

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。

Consult external AIs (Gemini 2.5 Pro, OpenAI Codex, Claude) for second opinions. Use for debugging failures, architectural decisions, security validation, or need fresh perspective with synthesis.

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。