3,519 件 ・ 関連度順
概要と使いどころ
Architectural guidance for building node-based UIs with React Flow. Use when designing flow-based applications, making decisions about state management, integration patterns, or evaluating whether React Flow fits a use case.
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。
Expert guide for property-based testing (fast-check, Hypothesis) and mutation testing (Stryker, mutmut) — AI-generated test properties, invariant discovery, mutation score optimization, and test suite quality verification / Panduan ahli property-based testing (fast-check, Hypothesis) dan mutation testing (Stryker, mutmut) — properti tes yang dihasilkan AI, penemuan invarian, optimasi skor mutasi, dan verifikasi kualitas suite tes.
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。
Implements permission-based authorization with custom attributes, policy providers, and authorization handlers. Provides granular access control beyond simple role-based authorization.
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。
Vocabulary learning strategies and retention science for any language -- frequency-based word selection, spaced repetition systems (Ebbinghaus forgetting curve, Leitner system, SM-2 algorithm), cognate exploitation, word family networks, context-based learning, collocations, depth of word knowledge (form, meaning, use), reading progression from controlled to authentic texts, and productive vs. receptive vocabulary thresholds. Use when building vocabulary plans, optimizing review schedules, selecting learning materials, or diagnosing vocabulary gaps.
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。
Pytest testing patterns for Python — fixtures, behavioral naming, behavior/risk-driven coverage, property-based testing with Hypothesis when useful, and mutation testing when justified. Use when writing tests, designing fixtures, configuring coverage, or applying parametrize, async testing, or property-based strategies.
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。
Hook recipes and working examples — plugin hooks, frontmatter hooks in skills/agents/commands, prompt-based LLM hooks, and complete code examples in Python and Node.js. Use when building hook scripts, integrating hooks into plugins, implementing prompt-based hooks, or looking for hook configuration patterns.
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。
This skill should be used when the user asks to "create a hook", "add a PreToolUse/PostToolUse/Stop hook", "validate tool use", "implement prompt-based hooks", "use ${CLAUDE_PLUGIN_ROOT}", "set up event-driven automation", "block dangerous commands", or mentions hook events (PreToolUse, PostToolUse, Stop, SubagentStop, SessionStart, SessionEnd, UserPromptSubmit, PreCompact, Notification). Provides comprehensive guidance for creating and implementing Claude Code plugin hooks with focus on advanced prompt-based hooks API.
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。
Choose the right serialization format for .NET applications. Prefer schema-based formats (Protobuf, MessagePack) over reflection-based (Newtonsoft.Json). Use System.Text.Json with AOT source generators for JSON scenarios.
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。
Advises on setting up LLM-based text classification as a validated measurement design. Covers codebook construction, learning-regime and model choice, prompt construction, validation against human coding with kappa and F1, hybrid human-LLM review, downstream measurement error, and reporting, plus a short resumable batch pattern with a rule-based baseline for repeated free text against a closed codebook. Use when the user wants to classify, code, or label text at scale. Category discovery goes to topic-modeling.
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。
Generate or redesign evidence-based README files for apps, libraries, CLIs, monorepos, and agent skills. This skill should be used for /readme-write, /readme, generate readme, write readme, improve readme, 帮我写 README, 美化 README, README を書いて, README 작성해 줘, напиши README, or their equivalents in any language. Confirm reader and visual preferences before generation; every README includes a source-backed flowchart, and every language edition keeps identical content apart from the language. Git-driven updates are routed to the companion readme-update skill.
Programmatic agent definitions for the Claude Agent SDK in TypeScript and Python. Use when creating agents for SDK-based applications rather than filesystem-based Claude 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。
Plan and execute account-based marketing campaigns for enterprise targets — account selection, personalized outreach, multi-channel touch sequences, and deal acceleration. Use when "ABM", "account-based marketing", "enterprise marketing", "target accounts", "named accounts", "enterprise outreach", "key accounts", or any request to build personalized campaigns for specific companies.
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。
测试时间有限要分优先级时使用。适用于版本测试规划、紧急修复验证、跨模块风险评估。融合概率×影响矩阵、Bach Heuristic Risk-Based Testing、HAZOP。
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。
Interactive button-based UI for XMTP agents following XIP-67. Use when creating menus, confirmation dialogs, selection options, or any button-based interaction. Triggers on inline actions, buttons, menus, or ActionBuilder.
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。
Bubble Tea is a powerful Go framework for building terminal user interfaces based on The Elm Architecture. Developed by Charmbracelet, it supports inline, full-window, and mixed TUI applications with a high-performance cell-based renderer and declarative views.
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。
Designs rebalancing triggers with calendar-based, threshold-based, and hybrid approaches with tax and cost optimization. Use when designing rebalancing rules, optimizing rebalancing frequency, or modeling turnover impact.
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。
fast-check patterns for JS/TS — 6 canonical property categories with worked examples targeting agent-studio utilities (path normalization, safe-json, glob-to-regex, routing logic)
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。
Used when the user asks to "create HTML slides", "generate an HTML presentation", "make animated slides", "build interactive slides", "create web slides", "GSAP presentation", "browser-based slides", "convert PPTX to HTML", "animated deck", "reveal.js slides", or when the output format is HTML for any presentation task. Also triggers on "single-file presentation", "CSS animations in slides", "scroll-based slides", or "web presentation". Generates self-contained, single-file HTML with viewport fitting, CSS/GSAP animations, and curated style presets.
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。
OpenAI GPT Image generation and editing (gpt-image-2, 1.5, 1, mini). Text-to-image, mask-based inpainting, multi-reference composition, multi-turn conversational editing via Responses API, streaming with partial images. This skill should be used when generating or editing images via OpenAI's image models, when near-perfect text rendering in images is needed, when mask-based region-aware editing is required, when multi-turn conversational image editing is desired, or when streaming progressive image delivery is needed. Complements nano-banana-pro (Gemini) as a parallel image generation backend.
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。
Organize, track, and value a trading card game collection. Covers inventory methods, storage best practices, grade-based valuation, want-list management, and collection analytics for Pokemon, MTG, Flesh and Blood, and Kayou cards. Use when starting a new collection and setting up inventory tracking, cataloging an existing collection that has grown beyond casual knowledge, valuing a collection for insurance or sale, or deciding which cards to submit for professional grading based on value potential.
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。
Plan a software release cycle with milestones, feature freezes, release candidates, and go/no-go criteria. Covers calendar-based and feature-based release strategies. Use when starting planning for a major or minor version release, transitioning from ad-hoc to structured release cadence, coordinating a release across multiple teams or components, defining quality gates for a regulated project, or planning the first public release (v1.0.0) of a project.
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 a generative diffusion model (DDPM or score-based) with noise scheduling, U-Net architecture, training loop, and sampling procedures including DDIM acceleration. Use when building a generative model for image, audio, or molecular synthesis; implementing DDPM from a research paper; adding a custom noise schedule or conditioning mechanism; replacing a GAN-based generator with a diffusion alternative; or prototyping before scaling with production frameworks like diffusers.
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。
Configure reverse proxy patterns across multiple tools including Nginx, Traefik, and ShinyProxy. Covers WebSocket proxying, path-based and host-based routing, SSL termination, and Docker label auto-discovery. Use when routing multiple services behind a single entry point, proxying WebSocket connections (Shiny, Socket.IO), auto-discovering Docker services with Traefik labels, or adding SSL termination to services that don't handle TLS natively.
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。
FastAPI web framework conventions: APIRouter with prefix/tags/dependencies, Pydantic v2 schemas (BaseModel, model_config, field_validator, model_validator), dependency injection with Depends, async SQLAlchemy session yielding, OAuth2PasswordBearer + JWT auth, lifespan context manager, pydantic-settings configuration, OpenAPI customization, and HTTPException error handling. Activated automatically by fastapi-plugin/stack.md. Use this skill to: - Structure FastAPI apps with per-feature APIRouter modules and a central app factory. - Write Pydantic v2 request and response schemas with proper validators. - Build a reusable Depends-based dependency chain for DB session, auth, and pagination. - Implement JWT-based authentication with get_current_user dependency. - Configure the app from environment variables via pydantic-settings. Do NOT use this skill for: - SQLAlchemy ORM model configuration and Alembic migrations — see fastapi-plugin:sqlalchemy-patterns. - Python language idioms — see python-foundation:python-conventions. - Testing patterns — see python-foundation:pytest-testing.
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。