4,035 件 ・ 関連度順
概要と使いどころ
Guides clean architecture design with strict 200-line file limits. Use when starting new features, refactoring large files, or planning module structure. Enforces modular design and real 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。
Generate attention-grabbing hooks for the first 3 seconds of videos. The hook determines if viewers stay or scroll. Creates multiple hook variations for A/B testing. Use when crafting video openings, improving retention, or creating scroll-stopping content for YouTube, TikTok, or Reels.
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。
Optimize video titles for maximum click-through rate (CTR) and YouTube/TikTok SEO. Generates multiple title variations balancing curiosity, keywords, and platform best practices. Use when naming videos, improving CTR, or A/B testing titles.
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 validating product assumptions before building, discovering unmet user needs, understanding customer problems and workflows, testing concepts or positioning, researching target markets, identifying jobs-to-be-done and hiring triggers, uncovering pain points and workarounds, or when users mention user research, customer interviews, surveys, discovery interviews, validation studies, or voice of customer.
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。
Master metrics definition, KPI tracking, dashboarding, A/B testing, and data-driven decision making. Use data to guide product decisions.
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。
Test web applications end-to-end by simulating user interactions and verifying expected outcomes. Trigger when the user asks to: test a web app, verify a user flow, run end-to-end tests, QA a feature, check that a page works correctly, validate user journeys, or test a 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。
Generates 10+ ad copy variations for any major ad platform with A/B testing, character-count compliance, and four proven copy frameworks (PAS, AIDA, BAB, 4Ps)
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。
Native Rust browser automation CLI for AI agents. Use when the user needs to interact with websites — navigating pages, filling forms, clicking buttons, taking screenshots, sharing a live browser view, narrating browser actions, extracting structured data, running assertions, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "show me the browser", "share the screen", "pause the browser", "step through this", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", "visual regression test", "check for prompt injection", or any task requiring programmatic web interaction.
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。
Debug 404 errors for API endpoints in microservice architectures where code exists but routing sends requests to wrong service. Use when: (1) API endpoint returns 404 despite code being deployed, (2) Multiple services share same hostname via HTTPRoute/Ingress, (3) Service logs show "Health server" but you expected API endpoints, (4) Monorepo deploys multiple images and endpoint might be in different service than expected. Covers: verifying which service handles which paths, HTTPRoute path precedence, testing endpoints directly from inside cluster, matching image tags across services.
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。
Investigate a GitHub issue and produce a structured implementation plan. Fetches issue details, explores the codebase, identifies affected layers (UI, BLoC, Repository, Client), and outputs a step-by-step plan with files to modify, testing strategy, and risk assessment. Works for bugs, features, and tasks. Invoke with /plan.
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。
Convert structured project-note JSON manuscripts into validated EPUB and optional Kindle-clickable PDF files. Use when the user asks to turn project notes, book-system JSON, FIRE-indexed notes, Luhmann/Zettelkasten numbered chunks, table-of-contents cards, index cards, cross-linked project notes, or manuscript JSON into an EPUB/PDF with chapter directories, keyword index cards, weighted links, bidirectional backlinks, versioned filenames, chapter-splitting plans, and full link 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。
Comprehensive product launch planning including GTM strategy, launch checklists, stakeholder communication, beta testing plans, and post-launch analysis. Execute successful product launches with coordinated teams.
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 comprehensive marketing campaigns from strategy through execution. Develop campaign briefs, select optimal channels, create timelines, allocate budgets, define targeting parameters, design A/B testing strategies, and establish performance measurement frameworks.
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 and optimize email marketing campaigns including sequences, newsletters, drip campaigns. Design subject line strategies, develop segmentation approaches, implement A/B testing, and establish analytics tracking for email performance.
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。
Generates automated test oracles to verify correct software behavior. Creates assertion-based oracles (expected values), property-based oracles (invariants), differential oracles (comparing implementations), and metamorphic oracles (input transformations). Use when you need to generate assertions for test cases, identify invariants that should always hold, compare new vs legacy implementations, create metamorphic test relationships, validate function correctness, or improve test coverage with better verification strategies. Supports Python (pytest, unittest, hypothesis) and Java (JUnit, property 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。
Generate comprehensive test case documentation from test code, test framework output, existing test docs, and source code context. Use when documenting test suites, creating test specifications, generating test coverage reports, onboarding developers to testing practices, or preparing QA documentation. Analyzes test functions (pytest, unittest) to extract test names, docstrings, assertions, and organization, then produces structured markdown with both overview-level summaries and detailed test case specifications including purpose, preconditions, steps, expected results, and test data. Triggers when users ask to document tests, generate test specifications, create test reports, summarize test coverage, or explain what tests do.
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。
Instruments code to track the flow of untrusted or sensitive data at runtime, enabling detection of injection vulnerabilities, data leaks, and privilege violations. Use when users need to: (1) Track untrusted input propagation through code, (2) Detect SQL injection, XSS, or command injection vulnerabilities, (3) Identify sensitive data leaks, (4) Monitor privilege escalation paths, (5) Perform dynamic taint analysis for security testing. Supports Python, Java, JavaScript, and C/C++ with configurable taint sources and sinks.
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。
Hardware verification tool for checking functional equivalence between two RTL designs (Verilog). Use when users need to: (1) Verify if two RTL versions are functionally equivalent, (2) Compare original vs. refactored RTL code, (3) Validate design changes or optimizations, (4) Identify semantic vs. cosmetic differences, (5) Generate counterexamples for non-equivalent designs. Analyzes interface alignment, state variables, logic differences, and produces detailed equivalence verdicts with plain language explanations. Particularly effective for design verification, code reviews, and regression testing of RTL modifications.
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 comprehensive test scenarios, user stories, and acceptance criteria from requirements and specifications. Use this skill when planning testing efforts, writing user stories, creating test cases, documenting acceptance criteria, exploring edge cases, designing test coverage, or translating requirements into actionable scenarios. Supports BDD (Given-When-Then), Gherkin syntax, user story format, and test case 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。
Generate targeted test inputs to reach specific code paths and hard-to-reach behaviors in Python code. Use when: (1) Targeting uncovered branches or specific execution paths, (2) Need coverage-guided test generation, (3) Want to leverage LLM understanding of code semantics for meaningful test inputs, (4) Testing boundary conditions and edge cases systematically, (5) Combining symbolic reasoning with fuzzing. Provides path analysis, constraint solving, coverage-guided strategies, and LLM-driven semantic generation for comprehensive test input creation.
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。
Automatically generate regression tests for Java codebases by analyzing changes between old and new code versions. Use when users need to: (1) Generate tests after refactoring or code changes, (2) Ensure previously tested behavior still works in new versions, (3) Cover modified or newly added code paths, (4) Migrate existing tests to work with updated APIs or signatures, (5) Maintain test coverage during code evolution. Supports JUnit and TestNG frameworks with unit tests, parameterized tests, and exception testing 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。
Analyze differences in program intervals between two versions of a program (old and new) to identify added, removed, or modified intervals. Use when comparing program versions, analyzing variable ranges, detecting behavioral changes in numeric computations, validating refactorings, or assessing migration impacts. Supports optional test suite integration to validate interval changes. Generates comprehensive reports highlighting intervals requiring further testing or verification.
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 unit tests with proper mocking for Python (unittest.mock/pytest) or Java (Mockito/JUnit) code. Use when users request test generation, unit tests with mocks, or testing code that has external dependencies like database calls, API requests, file I/O, or network operations. Automatically identifies dependencies to mock and creates executable, maintainable test 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。
Automatically identify potential boundary and exception cases from requirements, specifications, or existing code, and generate comprehensive test cases targeting boundary conditions, edge cases, and uncommon scenarios. Use this skill when analyzing programs, code repositories, functions, or APIs to discover and test corner cases, null handling, overflow conditions, empty inputs, concurrent access patterns, and other exceptional scenarios that are often missed in standard 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。