3,474 件 ・ 関連度順
概要と使いどころ
Master Bash Automated Testing System (Bats) for comprehensive shell script testing. Use when writing tests for shell scripts, CI/CD pipelines, or requiring test-driven development of shell utilities.
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 comprehensive testing strategies with pytest, fixtures, mocking, and test-driven development. Use when writing Python tests, setting up test suites, or implementing testing best practices.
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。
Python background job patterns including task queues, workers, and event-driven architecture. Use when implementing async task processing, job queues, long-running operations, or decoupling work from request/response cycles.
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 comprehensive testing strategies using Jest, Vitest, and Testing Library for unit tests, integration tests, and end-to-end testing with mocking, fixtures, and test-driven development. Use when writing JavaScript/TypeScript tests, setting up test infrastructure, or implementing TDD/BDD workflows.
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。
Design microservices architectures with service boundaries, event-driven communication, and resilience patterns. Use when building distributed systems, decomposing monoliths, or implementing microservices.
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 saga patterns for distributed transactions and cross-aggregate workflows. Use this skill when implementing distributed transactions across microservices where 2PC is unavailable, designing compensating actions for failed order workflows that span inventory, payment, and shipping services, building event-driven saga coordinators for travel booking systems that must roll back hotel, flight, and car rental reservations atomically, or debugging stuck saga states in production where compensation steps never 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。
Creates and maintains project context artifacts (product.md, tech-stack.md, workflow.md, tracks.md) in a `conductor/` directory. Scaffolds new projects from scratch, extracts context from existing codebases, validates artifact consistency before implementation, and synchronizes documents as the project evolves. Use when setting up a project, creating or updating product docs, managing a tech stack file, defining development workflows, tracking work units, onboarding to an existing codebase, or running project scaffolding.
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 proven backend architecture patterns including Clean Architecture, Hexagonal Architecture, and Domain-Driven Design. Use this skill when designing clean architecture for a new microservice, when refactoring a monolith to use bounded contexts, when implementing hexagonal or onion architecture patterns, or when debugging dependency cycles between application layers.
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。
Pester migration skill for upgrading PowerShell Pester test suites across major versions — v3→v4, v4→v5, and v5→v6. Covers the Discovery/Run two-phase model, moving setup into BeforeAll, $PSScriptRoot vs $MyInvocation, mock changes (Assert-MockCalled → Should -Invoke, removed fall-through), Invoke-Pester parameters → PesterConfiguration, data-driven -ForEach/-TestCases, and the v6 breaking changes. Use when the user asks to upgrade, migrate, or modernize Pester tests, fix *.Tests.ps1 files that broke after bumping the Pester version, or convert legacy Should / Invoke-Pester syntax.
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。
Get best practices for JUnit 5 unit testing, including data-driven tests
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 and scale partner ecosystems that drive revenue and platform adoption. Use when building partner programs from scratch, tiering partnerships, managing co-marketing, making build-vs-partner decisions, or structuring crawl-walk-run partner deployment.
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。
Get best practices for XUnit unit testing, including data-driven tests
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。
Get best practices for NUnit unit testing, including data-driven tests
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。
Get best practices for MSTest 3.x/4.x unit testing, including modern assertion APIs and data-driven tests
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。
Get best practices for TUnit unit testing, including data-driven tests
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。
Spin up a personal, motivating daily focus board that renders in a browser canvas and that the user drives by talking to their AI partner. Tasks track status (to-do → in progress → done) with timestamped progress notes and roll up into a "today's momentum" feed; numeric-goal tasks (pages, pomodoros, reps) render as progress-bar counters. Executive-function / neurodivergent-friendly by design: Focus mode, kind "not today" carryover (no overdue-shaming), a brain-dump box, reduced-motion, and gentle deadline countdowns. Add, reorder, and relabel tasks live, assign Eisenhower priority (Do first / Schedule / Delegate / Later), open with an above/below-the-line check-in and a daily mantra, and save an end-of-day recap. Use when someone wants to plan their day, stay focused, kick off a work session, or track progress. Progress persists in the browser (localStorage).
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。
Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality
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, improves, and debugs LLM prompts using production trace data, evaluations, and annotations. Extracts prompts from spans, gathers performance signal, and runs a data-driven optimization loop using the ax CLI. Use when the user mentions optimize prompt, improve prompt, make AI respond better, improve output quality, prompt engineering, prompt tuning, or system prompt improvement.
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。
Compile one or more OpenViking knowledge bases or document collections into topic-organized, evidence-grounded high-level knowledge, including cross-source findings, trends, changes, drivers, comparisons, implications, and uncertainties. Use with ov compile when the user asks to distill or synthesize a knowledge base, compare multiple collections, or derive higher-order insights such as changes across financial reports; do not use for document-by-document summaries.
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。
Evidence-driven tracing lane that orchestrates competing tracer hypotheses in Claude built-in team mode
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。
Cross-session environment retrospective driven by OMC's own instrumentation — trace timelines, friction reports, logs, and plan notepads. Every finding lands on the surface that owns the fix (deterministic check, steering volume, tooling, or information access); nothing is written without user approval.
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。
Write flux commentary for every P&L and balance-sheet line over threshold — current vs prior period and vs budget, with the driver explained from underlying activity. Use for the month-end close package and management reporting.
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。
Review a pull request against the SGLang Cookbook (docs/, Mintlify) contribution checklist — the config-driven format (per-model config + benchmarks JSX consumed by the shared _deployment.jsx / _playground.jsx engines). Run with /cookbook-review-pr <PR number>.
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。