「node」の検索結果

Capture what someone actually said without contaminating it with your own inference. Invoke when eliciting intent in conversation (design discussion, requirement interview, decision surfacing) and when turning a conversation into a durable record (ADR, decision node, memory entry, spec `## Decisions` row). Enforces quote-before-critique, keeps illustration separate from commitment, and holds interpretation subordinate to verbatim. Prevents the failure where unspecified space is filled by the recorder and then evaluated as if the speaker had specified it.

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Generate or repair a project's .gitignore. Composes the baseline must-ignore set (the single source of truth in baseline-ignores.json) with optional gitignore.io enrichment, and merges add-only so existing entries are never lost. Use when a repo has no .gitignore, when the commit-leak guard reports a gap, or when onboarding a new stack. Trigger: "set up gitignore", "fix my gitignore", "ignore node_modules/secrets", "gitignore for <stack>".

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Expert patterns for Azure Functions development including isolated worker model, Durable Functions orchestration, cold start optimization, and production patterns. Covers .NET, Python, and Node.js programming models.

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Azure Batch SDK for Java. Run large-scale parallel and HPC batch jobs with pools, jobs, tasks, and compute nodes.

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Use when a user wants a standalone browser-based visual companion for interactive mockups, diagrams, wireframes, layout comparisons, visual brainstorming, or clickable visual option selection. Starts a local zero-dependency Node server, writes HTML screens, and reads browser click events.

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Generate images, video, and audio with ComfyUI — install, launch, manage nodes/models, run workflows with parameter injection. Uses the official comfy-cli for lifecycle and direct REST/WebSocket API for execution.

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Guides AI agents through Senzing entity resolution workflows using the Senzing MCP server. Covers data mapping to Senzing format, SDK code generation (Python, Java, C#, Rust, TypeScript/Node.js), documentation search, error troubleshooting, sample data access, reporting and visualization, SDK setup guides, and V3-to-V4 migration. Use when working with entity resolution, record matching, record linkage, deduplication, Senzing SDK integration, data mapping for Senzing ingestion, or troubleshooting Senzing error codes. Also use when the user mentions matching records across data sources, finding duplicate entities, identity resolution, master data management, or needs to resolve who is who across datasets.

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

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Expert-level VLSI design covering CMOS logic, custom layout, standard cell design, physical design flow, DRC, LVS, and advanced process nodes.

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Use for any question about a codebase, its architecture, file relationships, or project content — especially when graphify-out/ exists, where the question should be treated as a graphify query first. Turns any input (code, docs, papers, images, videos) into a persistent knowledge graph with god nodes, community detection, and query/path/explain tools.

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Systematically find and fix performance problems in applications, frontend and backend, by measuring first. Use this skill whenever the user reports slow endpoints, high latency, poor throughput, memory leaks, large bundles, or slow page loads; wants to profile with cProfile, py-spy, Chrome DevTools, Node --cpu-prof, or perf; run load tests with locust or k6; set performance budgets or SLOs; optimize database queries (EXPLAIN, indexes, N+1); add caching (Redis); improve frontend performance (code splitting, lazy loading, image optimization, memoization, virtualization); or tune the network (CDNs, gzip/br, HTTP/2/3). Also trigger for p50/p95/p99 latency, throughput, error rate, connection pooling, async vs threads, GIL, and memory leak investigations.

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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 when building an MCP server in Python (FastMCP) or Node/TypeScript (MCP SDK) — agent-centric tool design, input schemas, error handling, and the 10-question evaluation harness.

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When working within any module under `modules.root_paths` from `.agent-project-settings.yml` — Laravel HMVC, Symfony DDD-lite, Node monorepo, Python src/, Go internal/, or a custom path.

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Padrões de arquitetura backend, design de API, otimização de banco de dados e melhores práticas server-side para Node.js, Express e rotas de API Next.js.

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Padrões de codificação universais, melhores práticas e padrões para desenvolvimento em TypeScript, JavaScript, React e Node.js.

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Padrões especialistas para desenvolvimento com Azure Functions, incluindo modelo isolated worker, orquestração de Durable Functions, otimização de cold start e padrões de produção. Cobre modelos de programação .NET, Python e Node.js. Use quando: azure function, azure functions, durable functions, azure serverless, function app.

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Guia completo de desenvolvimento backend para microserviços Node.js/Express/TypeScript. Use ao criar routes, controllers, services, repositories, middleware ou trabalhar com Express APIs, acesso a banco de dados com Prisma, rastreamento de erros com Sentry, validação com Zod, unifiedConfig, injeção de dependências ou padrões assíncronos. Cobre arquitetura em camadas (routes → controllers → services → repositories), padrão BaseController, tratamento de erros, monitoramento de performance, estratégias de testes e migração de padrões legados.

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Especialista em BullMQ para filas de jobs com Redis, processamento em background e execução confiável de operações assíncronas em aplicações Node.js/TypeScript. Use quando: bullmq, bull queue, redis queue, background job, job queue.

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Instâncias GPU em nuvem reservadas e sob demanda para treinamento e inferência de ML. Use quando você precisar de instâncias GPU dedicadas com acesso SSH simples, sistemas de arquivos persistentes ou clusters multi-node de alto desempenho para treinamento em larga escala.

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Orquestração de treinamento distribuído em clusters. Escala PyTorch/TensorFlow/HuggingFace do laptop para milhares de nós. Ajuste de hiperparâmetros integrado com Ray Tune, tolerância a falhas, escalabilidade elástica. Use ao treinar modelos massivos em múltiplas máquinas ou executar varreduras distribuídas de hiperparâmetros.

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Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

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Safely clean regenerable clutter from a project folder by scanning for build artifacts and caches, backing up every removed path into a timestamped archive with a BACKUP.md manifest, zipping the backup to save space, and only deleting originals after explicit confirmation. Use when the user asks to clean a project directory, remove node_modules, clear build output, reclaim disk space, or tidy a repo before archiving.

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GodoMaster — Godot 4.x game development intelligence: GDScript, nodes/scenes, 2D/3D rendering, physics, animation, UI, audio, input, shaders, networking, performance, export, file I/O, testing, localization, AI behavior, asset pipeline, and architecture tooling. Complete workflow for indie and professional game development based on official Godot documentation.

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。