「debug」の検索結果

Report the current development environment: Node, Python, OS, git state. Use when debugging environment issues, onboarding, or checking system compatibility.

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。

Expert Clay platform consultant for B2B data enrichment and workflow automation. Use when the user asks about Clay tables, waterfall enrichment, Clay credits, Clay AI columns, Claygent, enrichment workflows, Clay integrations, Clay exports, Clay table design, lead scoring in Clay, or building data pipelines in Clay. Also triggers on "Clay table", "enrichment waterfall", "Clay credits", "Claygent", "Clay AI column", "Clay export", "Clay providers", "enrich in Clay", "Clay column", "Clay formulas", "find emails", "email waterfall", "phone waterfall", "lead scoring", "Clay debugging", "Clay troubleshoot". Do NOT use for general CRM questions without Clay context, cold email copy (use cold-email-copy-playbook), or signal routing (use buying-window-signal-workflow).

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Use when writing, debugging, or configuring an omp hook or extension — blocking or rewriting tool calls, redacting tool output, reshaping LLM context, registering tools/commands, or wiring modules through config.yml. Covers ExtensionAPI (current) vs HookAPI (legacy), the event catalog, discovery and load order, and disable controls. Not for Claude Code hooks (~/.claude/settings.json) or the shared harness safety guard (~/.agents/harness/hooks/).

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Retrieve current documentation, API references, and code examples for any library, framework, SDK, CLI tool, or cloud service — including well-known ones like React, Next.js, Prisma, Django, or Spring Boot. Use for API syntax, configuration options, version migrations, setup instructions, and library-specific debugging, even when you already think you know the answer: training data on API details and signatures is frequently outdated. Prefer this over web search for library documentation.

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Diagnosis loop for hard bugs and performance regressions. Use when the user says "diagnose"/"debug this", or reports something broken/throwing/failing/slow.

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Systematic testing methodology for exhaustive branch coverage, edge case identification, and production bug prevention. Use when PR review reveals incomplete test coverage, when tests pass but users report bugs, when code changes break existing features, when verifying all branches and edge cases before merge, when analyzing "it works on my machine" issues, when planning test strategy for new features, or when debugging flaky tests and race conditions.

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TypeScript-only skill for generating and debugging high-resolution AI images through sub2api OpenAI-compatible APIs. Use this whenever the user wants a TS/Node sub2api image generator, 4K image generation, stream=true /v1/images/generations or /v1/responses image generation, Cloudflare 524 timeout avoidance, partial image capture, or SUB2API_BASE_URL/SUB2API_API_KEY based image generation.

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Use on a bug, test failure, crash, or unexpected behavior — enforce reproduce → isolate → hypothesize → verify before any fix; fires even on 'this is broken' / 'quick fix'.

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Use when working with Docker — Dockerfile edits, docker-compose services, containers, or the dual-container (fast + Xdebug) setup — even when the user just says 'my container won't start'.

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Use when implementing, debugging, refactoring, or reviewing code — enforces the think → analyze → verify → execute workflow — even when the user just says 'implement X' without naming it.

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Use when adding, running, or debugging proctmux end-to-end integration tests, especially PTY/TUI behavior, terminal snapshots, unified/client modes, timing flakes, VT100 emulator limits, or files under tests/e2e and internal/testharness/e2e.

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Plataforma de observabilidade para LLM com rastreamento, avaliação e monitoramento. Use ao depurar aplicações LLM, avaliar saídas de modelos em datasets, monitorar sistemas em produção ou construir pipelines de testes sistemáticos para aplicações de IA.

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Visualize métricas de treinamento, depure modelos com histogramas, compare experimentos, visualize grafos de modelos e perfil de desempenho com TensorBoard - kit de visualização de ML do Google

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Plataforma de observabilidade de IA de código aberto para rastreamento de LLM, avaliação e monitoramento. Use ao debugar aplicações de LLM com rastreamentos detalhados, executar avaliações em datasets ou monitorar sistemas de IA em produção com insights em tempo real.

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Guia completo para usar Claude Code de forma eficaz. Inclui templates de configuração, estratégias de prompting com keywords "Thinking", técnicas de debugging e melhores práticas para interagir com o agente.

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Modos operacionais de IA (brainstorm, implement, debug, review, teach, ship, orchestrate). Use para adaptar o comportamento com base no tipo de tarefa.

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Use this Canvas-owned clean-room skill when working with Remotion or React-based video generation. Trigger for Remotion projects, compositions, sequences, frame-based animation, rendering, captions, audio, video assets, templates, and debugging programmatic video output.

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[Workflow] Use when activating the Integration Test Green workflow — drive an integration-test suite to fully green with a bounded convergence loop: verify the whole system (or the named target), adjudicate every failure with /debug-investigate + /integration-test-review before any edit, fix at the owning layer, /changes-review each round's fix diff, re-verify from scratch, then sync spec TCs, the integration-test reference doc, and feature docs.

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[Workflow] Use when activating the Bug Fix workflow for systematic debugging with root cause investigation, fix, and verification.

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[Testing] Use when you need to drive an integration-test suite to fully green — each round runs /integration-test-verify (whole system by default, or the target named in the prompt), and on ANY failure combines /debug-investigate + /integration-test-review (report-only) to adjudicate the fault (test wrong · test not optimal · source wrong), then /fix to resolve it at the owning layer, then /changes-review on that round's fix diff, then re-runs a FRESH full verify — looping until the whole suite passes its 2-consecutive-green-runs gate with zero failures.

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[Fix & Debug] Use when you need to investigate and explain how existing features or logic work. Flag: --mode=explain produces a one-way developer-narrative explanation (Purpose → How → Why → Impact) tuned by coding level; use /understand for the standalone prompt-driven explainer.

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[Debugging] Use when analyzing or optimizing performance bottlenecks: database queries, N+1 fan-out, indexing, API latency, memory/GC, concurrency and pool saturation, algorithmic complexity (O(n²)), network/protocol round trips, frontend rendering and Core Web Vitals, caching, and distributed/resilience paths. Calibration constants and domain laws (latency ladder, Little's Law, utilization knee, CWV thresholds, symptom→cause triage) live in references/performance-knowledge.md.

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Use when building, refactoring, or debugging a macOS menu bar app with SwiftUI, or when a MenuBarExtra misbehaves — the icon renders wrong or vanishes, the Settings window opens behind other apps, the popover cannot be closed, controls inside a .menu style do not render, the Dock icon comes back, or the app quits when the item is dragged off the menu bar. Triggers include menu bar app, status bar app, tray app, MenuBarExtra, NSStatusItem, LSUIElement, hide Dock icon.

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Use when adding, reviewing, or debugging Launch at Login in a macOS app — SMAppService registration, the requiresApproval state, a Settings toggle that reflects reality, and migration off deprecated login-item APIs.

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。