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概要と使いどころ
Use when creating high-quality scientific or medical infographic images or prompts, including review figures, disease maps, teaching diagrams, mechanism summaries, classification overviews, timelines, matrices, and academic visual explainers. If the user asks to generate the image, use ChatGPT image generation.
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 when creating scientific magazine cover style images or prompts, research hero visuals, journal-cover-inspired concepts, academic poster covers, PPT covers, and high-impact scientific visual concepts. If the user asks to generate the image, use ChatGPT image generation.
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 when working with Auto.dev APIs, vehicle data, VIN decoding, car listings, vehicle photos, specs, recalls, payments, interest rates, taxes, OEM build data, plate-to-VIN, CLI commands, MCP tools, or SDK methods for any automotive data task
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 when orchestrating Loop Factory specs from factory/specs/inbox or factory/specs/active, dispatching work to Codex or Claude agents, generating implementation prompts, or deciding next loop state.
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 when orchestrating Loop Factory specs from factory/specs/inbox or factory/specs/active, dispatching work, generating implementation prompts, or deciding next loop state.
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。
Comprehensive Mastra framework guide. Teaches how to find current documentation, verify API signatures, and build agents and workflows. Covers documentation lookup strategies (embedded docs, remote docs), core concepts (agents vs workflows, tools, memory, RAG), TypeScript requirements, and common patterns. Use this skill for all Mastra development to ensure you're using current APIs from the installed version or latest 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。
Abductive reasoning about causes. Takes a phenomenon, separates observations from interpretations, then spawns diagnosticians applying different reasoning lenses (technical, human-factors, process, incentive-structure, environmental, temporal, measurement-artifact, statistical) in isolation. Orchestrator evaluates candidate causes for fit-against-evidence, calibrates confidence honestly, and reports leading candidate(s) with distinguishing evidence needed. Produces feedback only — no code, no tickets, no artifacts.
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 when creating, reviewing, debugging, or maintaining Home Assistant custom Lovelace cards or card editors, including custom elements, Web Components, hass data, setConfig, getCardSize/getGridOptions, getConfigElement/getConfigForm, window.customCards, resource registration, HACS card packages, entity display, service calls, translations, or frontend build validation.
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。
Optimizes natural-language image requests and rough prompts into ready-to-use GPT Image 2.5 prompts using the official prompting guide as a live reference. Use when users ask to improve image prompts, 优化生图提示词, 把想法变成提示词, or write prompts for GPT Image 2.5 Flare or Sunburst. Produces prompt text, not images, unless the user explicitly asks to generate.
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 media generation CLI tool using Google's Imagen 4, Veo 3.1, and Gemini TTS. Use when the user wants to (1) generate images from text prompts, (2) edit existing images with AI, (3) explain image contents, (4) generate videos from text or images, (5) create narration/voice audio with character settings. Triggers on requests like "generate an image of...", "create a video...", "make a voice that says...", "edit this image to...", "describe this image".
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 data science refit recipes when optimizing a Coworld player.
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 data science refit recipes in scripted Coworld policy optimization.
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 when finding and statistically confirming a reproducible Crewrift crux before changing the policy.
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。
Verify a native LLM voting advisor before trusting advisor-sensitive evaluations.
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 reusable data science refit recipes in software and coding-agent work.
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 Crewrift-specific data science refit recipes when optimizing a player.
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。
Opinionated house conventions for NestJS backends — module boundaries and layering, provider wiring and injection scopes, guards/interceptors/pipes/filters, DTOs and validation, domain exceptions, config, and the Nest 11 / Express 5 behaviour that compiles cleanly and fails at runtime. Use whenever the code is NestJS (`@nestjs/*` in package.json, or `@Module`/`@Injectable`/`@Controller`/`@Cron` in the file), including when the question sounds like plain TypeScript, an OOP refactor, a code review, or an ops problem — the Nest-specific answer differs from the generic one. Typical asks: scaffolding a module, adding a controller, provider, or endpoint, wiring an enhancer, a boot-time DI or circular-import failure, a response leaking entity fields despite `@Exclude()`, a cron job firing on every replica, upgrading NestJS, or reviewing a Nest diff. Not for plain Express or Fastify, Angular, frontend, SQL, or CI.
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。
Apply this opinionated workflow when reverse-engineering legacy or undocumented systems: scope, explore the code, trace data flows, document in EARS format, flag uncertainties. For code archaeology, onboarding, and requirements extraction.
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。
Task planning with scout data + task-type templates. Produces implementation checklists.
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。
shadcn-vue for Vue/Nuxt with Reka UI components and Tailwind. Use for accessible UI, Auto Form, data tables, charts, dark mode, or encountering component imports, Reka UI errors.
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。
Apple FoundationModels framework for on-device LLM — text generation, guided generation with @Generable, tool calling, and snapshot streaming in iOS 26+.
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。
Create reproducible, cross-platform development environments with Flox — a declarative environment manager built on Nix. ALWAYS use this skill when the user needs to: set up a project with system-level dependencies (compilers, databases, native libraries like openssl, libvips, BLAS, LAPACK); configure reproducible toolchains for Python, Node.js, Rust, Go, C/C++, Java, Ruby, Elixir, PHP, or any language; manage environments that must work identically across macOS and Linux; pin exact package versions for a team; run local services (PostgreSQL, Redis, Kafka) alongside development tools; onboard new developers with a single command; or solve 'works on my machine' problems. Especially valuable for AI-assisted and vibe coding — Flox lets agents install tools into a project-scoped environment without sudo, system pollution, or sandbox restrictions, and the resulting environment is committed to the repo so anyone can reproduce it instantly. Use this skill even if the user doesn't mention Flox — if they describe needing reproducible, declarative, cross-platform dev environments with system packages, this is the right tool. Also use when the user mentions .flox/, manifest.toml, flox activate, or FloxHub.
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。
Fact-forcing gate that blocks Edit/Write/Bash (including MultiEdit) and demands concrete investigation (importers, data schemas, user instruction) before allowing the action. Measurably improves output quality by +2.25 points vs ungated agents.
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 when large data ingestion, backfill, export, ETL, warehouse loading, manifest catch-up, or table synchronization needs to become much faster while preserving data correctness.
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。