「apt」の検索結果

Write what the work found into the project's knowledge record, so the next session inherits it instead of re-deriving it. Use the moment something worth keeping exists: a fact taken from a source, a rule worked out, a decision or conclusion, a measurement, a correction to a recorded value, or a question left open - and check for unrecorded findings before implementation, handoff (including a PR), or finishing work. Record within the user's write authorization. The first write creates GROUNDING.yaml. Covers one-report update, add, set, review, same and distinct, judgments with a falsifier, and background capture.

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Set up kpopper watch for background branch compatibility, scoped shared facts, and daily review. Use when asked to watch a project's graph, check worktree changes against main, share external observations, or install a kpopper routine. Ordinary followup capture recommends scheduling without enabling it.

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Fine-tune LLMs and VLMs using Unsloth on HF Jobs (Hugging Face on-demand cloud GPUs). Use when users want to fine-tune language models, train VLMs (Vision Language Models), do continued pretraining, domain adaptation, or run UV scripts on HF Jobs. Triggers on requests involving Unsloth training, HF Jobs GPU training, Qwen3-VL fine-tuning, Gemma VLM training, or LoRA fine-tuning on cloud GPUs.

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Domain-Driven Design system for software development. Use when designing new systems with DDD principles, refactoring existing codebases toward DDD, generating code scaffolding (entities, aggregates, repositories, domain events), facilitating Event Storming sessions, creating bounded context maps, or performing code reviews with a DDD lens. Covers both strategic design (bounded contexts, subdomains, context maps, ubiquitous language) and tactical design (entities, value objects, aggregates, domain services, repositories). Supports all major architecture patterns (Hexagonal/Ports & Adapters, CQRS, Event Sourcing, Clean Architecture) with language-agnostic guidance and concrete examples in Python and TypeScript.

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Build, modify, run, test, screenshot, record, and export Godot 4.x games with the gdx CLI. Use when Codex needs to create a new Godot game project, attach to an existing Godot project, generate GDScript, build scenes/resources from JSON specs, edit a running scene through the gdx daemon, capture screenshots, record animation clips, send input, inspect game state, run Godot tests, or produce exports through gdx.

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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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Run the skill-improvement pipeline after a learning was detected — capture, classify, create, validate, apply; explicit request only.

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Use when a repeated learning, mistake, or successful pattern should be turned into a new rule or skill. Also use after completing a task to capture learnings from the work.

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When setting up the legal pack — captures jurisdiction, role, escalation and playbook into a plain-prose profile every legal skill reads. Triggers on "set up legal", "legal profile".

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Use when capturing an architectural decision — file naming, next ADR number, Status / Context / Decision / Consequences, index regen; fires even without saying 'ADR'.

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Crie suites de testes end-to-end confiáveis, rápidos e mantíveis que ofereçam segurança para fazer deploy de código rapidamente e capturem regressões antes que os usuários as encontrem.

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Infraestrutura de pagamento para agentes IA — escrow ACTP, pagamentos instantâneos x402, liquidação em USDC na Base L2. Onboarding interativo: pergunta suas preferências, gera código customizado do agente e verifica a configuração. Cobre padrões provider/requester, roteamento de adaptadores, máquina de 8 estados, preços, disputas e identidade.

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Padrões abrangentes para criar, manter e gerenciar Registros de Decisões de Arquitetura (ADRs) que capturam o contexto e a lógica por trás de decisões técnicas significativas.

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Quando o usuário quer otimizar qualquer formulário que NÃO seja de inscrição/registro — incluindo formulários de captura de leads, formulários de contato, formulários de solicitação de demo, formulários de aplicação, formulários de pesquisa ou formulários de checkout. Também use quando o usuário menciona "otimização de formulário", "conversões de formulário de lead", "atrito em formulário", "campos de formulário", "taxa de conclusão de formulário" ou "formulário de contato". Para formulários de inscrição/registro, consulte signup-flow-cro. Para popups contendo formulários, consulte popup-cro.

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Quando o usuário deseja criar ou otimizar popups, modais, overlays, slide-ins ou banners para fins de conversão. Use também quando o usuário menciona "exit intent," "conversões de popup," "otimização de modal," "popup de captura de leads," "popup de email," "banner de anúncio" ou "overlay." Para formulários fora de popups, consulte form-cro. Para otimização geral de conversão de página, consulte page-cro.

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Quando o usuário quer otimizar fluxos de inscrição, registro, criação de conta ou ativação de teste gratuito. Também use quando o usuário mencionar "conversões de inscrição", "atrito de registro", "otimização de formulário de inscrição", "inscrição em teste gratuito", "reduzir abandono de inscrição" ou "fluxo de criação de conta". Para onboarding pós-inscrição, consulte onboarding-cro. Para formulários de captura de leads (não criação de conta), consulte form-cro.

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Framework de pré-treinamento visão-linguagem que conecta codificadores de imagem congelados e LLMs. Use quando você precisar de legendagem de imagens, resposta a perguntas visuais, recuperação imagem-texto ou chat multimodal com desempenho zero-shot de última geração.

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Framework PyTorch de alto nível com classe Trainer, treinamento distribuído automático (DDP/FSDP/DeepSpeed), sistema de callbacks e boilerplate mínimo. Escala de laptop para supercomputador com o mesmo código. Use quando quiser loops de treinamento limpos com boas práticas integradas.

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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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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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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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Fetch transcripts from YouTube videos for summarization, analysis, or reference. Accepts video ID or full URL. No API key required. Triggers: YouTube transcript, video transcript, get subtitles, what did they say in the video, summarize YouTube, extract video text, caption from video.

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Write minimal, accessible web UI without over-engineering, adapting to whatever stack the project uses — React, Vue, Svelte, Angular, web components, or plain HTML. Use this skill on every project and every task that writes or reviews markup or components, builds forms, adds interactive elements like buttons, dialogs, accordions, or tabs, or audits code for accessibility — even when the user never mentions accessibility, and even inside a framework, CMS, or design system. It's non-negotiable regardless of project type, stack, or deadline. About to reach for ARIA attributes, a dialog library, a focus-trap package, or a headless UI component? Use this skill first.

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Load this skill whenever the project contains audio or video content, media players, podcasts, video embeds, or any <audio>/<video> elements. Under no circumstances publish audio or video without captions, transcripts, and audio descriptions where required. Absolutely always apply WCAG 1.2 criteria for time-based media.

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。