「implementation」の検索結果

Use when a task explicitly creates or improves an MCP server and needs a safe implementation and evaluation workflow

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Pixel-faithful clone of any web UI into the user's existing stack, using whatever sources are available — a screenshot alone, a live URL, raw HTML/CSS, or any combination. Use this skill whenever the user wants to recreate, match, replicate, or "clone" a design from a screenshot, image, URL, Figma export, or HTML dump. Trigger on phrases like "clone this", "match this design", "build this from screenshot", "recreate this page", "make it look like this", "rebuild this UI", "copy this layout", or any time the user provides a visual reference and asks for a faithful implementation. Do not undertrigger — even if the user just drops a screenshot without explicit phrasing, this skill applies.

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Catalog APIs, persistence models, and message/event payloads into DATA_MODEL.md at the project root. Use after implementation when a diff touches request/response types, schemas, ORM models, queue payloads, or webhook shapes. Triggers on "document data model", "update DATA_MODEL", "catalog API shapes".

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。

Structures a vague engineering request into a well-scoped task brief before any implementation begins. Use when the user has an engineering goal but the ask is missing which repos are in scope, what "done" means, constraints, or open questions. Triggers on "shape this", "scope this out", "frame this work", "write a brief for", "I want to build" (with unclear scope), or the /shape slash command. Produces a filled task template (multi-repo feature, single-repo change, investigation, or bugfix). Not for already-scoped work — go straight to execution. If the domain is unclear, ask one qualifying question before routing — for marketing intake see marketing-shaper, game-design intake see game-design-shaper.

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Design, analyze, and optimize cold outbound email campaigns on Instantly. Handles end-to-end ICP definition, expert panel scoring (recursive to 90+), sequence copywriting, infrastructure audit, capacity planning, and an implementation/strategy doc for human review. Use when asked to build cold outbound sequences, optimize cold email, audit an outbound motion, write sales sequences, or design cold email campaigns. Supports "start from scratch" and "optimize existing" modes. Execution skill — it produces copy, math, and a doc; it does not auto-send. Requires Instantly for audit/send features.

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Use when designing the systems of a game from a locked concept — core loops, meta loops, player verbs, progression, content systems, level structure, and narrative integration. Triggers on "game design doc", "GDD", "system spec", "core loop design", "progression design", "level design", "MDA", or "design the systems". Produces a design doc plus per-system specs that game-balancer numbers, iap-manager stocks, and godot-engineer builds. Stops at design — no number tuning, pricing, or engine code. For balance numbers see game-balancer; for the engine implementation see godot-engineer.

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Scan projects for credential leaks, secrets in code, insecure patterns, LLM API key exposure, PII leakage to external AI services, and .env/.gitignore misconfigurations. Especially useful for data and API integrations, regardless of implementation language.

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。

User asks for design, planning, or approach exploration before implementation. Covers new features, components, refactors, or architecture decisions. Creates design docs and proposes approaches with trade-offs.

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。

Diagnose a dygo Business App that does not validate, boot, route, render, authorize, migrate, or execute background work correctly. Use for evidence-first investigation rather than feature implementation.

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Review a dygo Business App for framework conventions, model quality, access control, SDK boundaries, observability, and safe lifecycle behavior. Use for App audits and pre-merge reviews, not ordinary implementation.

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Improve or review ordinary dygo code changes for simplicity, focused scope, canonical ownership, clear boundaries, and verifiable behavior. Use for normal implementation and refactoring quality; use dygo-thermonuclear-review only for an explicitly severe audit.

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Write, refactor, or review Go code in the dygo framework or App SDK using dygo package boundaries and runtime conventions. Use for substantial Go implementation that is not primarily Cobra CLI work.

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欧盟 AI 法案知识引擎——以 70 份官方欧盟来源文件为基础的权威监管问答（包括 2026 年委员会关于 Art. 6 高风险分类的指南草稿——一般原则、附件 I、附件 III）。基于完整条例文本、委员会指南、EDPB/EDPS 意见、《实践守则》、协调标准、FRIA 指南、事件模板和行业特定指引，以条款级引用回答关于 AI 法案的任何问题。当用户询问"EU AI Act"、"AI Act regulation"、"Regulation 2024/1689"、"KI-Verordnung"、要求"explain an AI Act article"、"what does Article X say"、"AI Act requirements"、"AI Act penalties"、"AI Act timeline"、"GPAI obligations"、"high-risk AI"、"prohibited AI practices"、"AI Act and GDPR"、"fundamental rights impact assessment"、"AI Act standards"、"AI Act national implementation"、"AI literacy"、"serious incident reporting"、"Digital Omnibus"、"AI regulatory sandbox"，或任何关于欧盟人工智能监管的问题时，应使用本技能。德语术语同样触发："KI-Gesetz"、"KI-Verordnung"、"Hochrisiko-KI"、"KI-Kompetenz"、"Grundrechte-Folgenabschaetzung"、"GPAI-Verhaltenskodex"。对于产出分类决定而非知识答案的评估工作流，提议在会话内运行结构化风险等级分类。

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Clarify ambiguous requirements through focused dialogue before implementation. Use when requirements are unclear, features are complex (>2 days), or involve cross-team coordination. Ask two core questions - Why? (YAGNI check) and Simpler? (KISS check) - to ensure clarity before coding.

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。

Grounds every implementation decision in official documentation. Use when you want authoritative, source-cited code free from outdated patterns. Use when building with any framework or library where correctness matters.

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Transform learning content (like YouTube transcripts, articles, tutorials) into actionable implementation plans using the Ship-Learn-Next framework. Use when user wants to turn advice, lessons, or educational content into concrete action steps, reps, or a learning quest.

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Break a plan, spec, or PRD into independently-grabbable GitHub issues using tracer-bullet vertical slices. Use when user wants to convert a plan into issues, create implementation tickets, or break down work into issues.

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Delivers changes incrementally. Use when implementing any feature or change that touches more than one file. Use when you're about to write a large amount of code at once, or when a task feels too big to land in one step.

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。

Build high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services. Covers design principles, architecture, implementation patterns, and evaluation-driven development.

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Manages deprecation and migration. Use when removing old systems, APIs, or features. Use when migrating users from one implementation to another. Use when deciding whether to maintain or sunset existing 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。

Build signal-based forms in Angular v21+ using the new Signal Forms API. Use for form creation with automatic two-way binding, schema-based validation, field state management, and dynamic forms. Triggers on form implementation, adding validation, creating multi-step forms, or building forms with conditional fields. Signal Forms are experimental but recommended for new Angular projects. Don't use for template-driven forms without signals or third-party form libraries like Formly or ngx-formly.

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 and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.

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。

Archive a completed change in the experimental workflow. Use when the user wants to finalize and archive a change after implementation is complete.

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。

Implement tasks from an OpenSpec change. Use when the user wants to start implementing, continue implementation, or work through tasks.

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。