データ分析、機械学習、LLMアプリ、プロンプト設計（9,144 件）
Database Architect skill. Use this when you need to modify the database schema, add tables, or seed data. This project uses RAW SQL SCRIPTS orchestrated by .NET Aspire, NOT Entity Framework Migrations.
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 AI prompt engineering safety review and improvement prompt. Analyzes prompts for safety, bias, security vulnerabilities, and effectiveness while providing detailed improvement recommendations with extensive frameworks, testing methodologies, and educational content.
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。
Search the web with LLM-optimized results via the Tavily CLI. Use this skill when the user wants to search the web, find articles, look up information, get recent news, discover sources, or says "search for", "find me", "look up", "what's the latest on", "find articles about", or needs current information from the internet. Returns relevant results with content snippets, relevance scores, and metadata optimized for LLM consumption. Supports domain filtering, time ranges, and multiple search depths.
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。
Detects chart patterns in OHLC data from natural-language hypotheses or visual chart selections.
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。
Fetch historical OHLC chart data from yfinance (stocks) or ccxt (crypto) and load it onto the chart.
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。
Research historical news events that moved (or are moving) an asset's price and plot them as data points directly on the chart. Click a marker in the Historic News tab to zoom the chart to that event and read the article. Useful for identifying the fundamental drivers behind price action — earnings, regulation, product launches, macro shocks, geopolitical events.
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, 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 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。
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。
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 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。
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 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。
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 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。
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。
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。
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。
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。
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。