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概要と使いどころ
Automatically identify metamorphic properties (symmetry, linearity, additivity, input invariances) from programs or functions. Use when generating metamorphic tests, discovering program properties, validating transformations, or creating test oracles without explicit specifications. Analyzes control flow, data flow, and sample executions to output structured properties for metamorphic test generation and 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 randomized and edge-case inputs to detect unexpected failures, bugs, and security vulnerabilities through fuzz testing. Use when creating test cases for robustness testing, generating adversarial inputs, testing error handling, finding edge cases, or security testing. Produces Python test code with fuzzing inputs for strings, numbers, and structured data focusing on edge cases, invalid inputs, and random valid inputs. Triggers when users ask to generate fuzz tests, create randomized test inputs, test edge cases, find bugs through fuzzing, or generate adversarial test cases.
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。
Git expert combining atomic commits, rebase/squash, and history search (blame, bisect, log -S). Use for any git operations requiring structured commit strategies, history rewriting, or code archaeology. Triggers: 'commit', 'rebase', 'squash', 'who wrote', 'when was X added', 'find the commit that'.
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 clear and structured pull request descriptions from code changes. Use when Claude needs to: (1) Create PR descriptions from git diffs or code changes, (2) Summarize what changed and why, (3) Document breaking changes with migration guides, (4) Add technical details and design decisions, (5) Provide testing instructions, (6) Enhance descriptions with security, performance, and architecture notes, (7) Document dependency changes. Takes code changes as input, outputs comprehensive PR description in Markdown.
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。
Explain why counterexamples violate specifications by analyzing formal specifications (temporal logic, invariants, pre/postconditions, code contracts), informal requirements (user stories, acceptance criteria), test specifications (assertions, property-based tests), and providing step-by-step traces showing state changes, comparing expected vs actual behavior, identifying root causes, and assessing violation impact. Use when debugging test failures, understanding model checker output, explaining runtime assertion violations, analyzing static analysis warnings, or teaching specification concepts. Produces structured markdown explanations with traces, comparisons, state diagrams, and cause chains. Triggers when users ask why something failed, explain a violation, understand a counterexample, debug a specification, or analyze why a test fails.
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。
Conduct comprehensive code reviews identifying bugs, security issues, performance problems, code quality concerns, and best practice violations. Use when reviewing pull requests, examining code changes, evaluating new code, assessing code quality, or providing feedback on implementations. Analyzes code for correctness, security vulnerabilities, performance bottlenecks, maintainability issues, test coverage, documentation quality, and adherence to coding standards. Produces structured markdown reviews with categorized findings, severity ratings, specific examples, and actionable recommendations. Triggers when users ask to review code, check pull requests, evaluate implementations, find bugs, or assess code 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。
Prepare structured briefings for meetings with legal relevance and track resulting action items. Use when preparing for contract negotiations, board meetings, compliance reviews, or any meeting where legal context, background research, or action tracking is needed.
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。
TRIGGER THIS when preparing interviews, planning hiring processes, creating interview questions, building evaluation frameworks, developing interviewer guides, or standardizing interview approaches. Builds complete interview kits with competency-based questions, scorecards, rubrics, interviewer training, and structured evaluation protocols.
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 structured playtesting plans for user feedback. Use when the user says "playtest plan" or "playtesting"
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 code changes adversarially using parallel review layers (Blind Hunter, Edge Case Hunter, Acceptance Auditor) with structured triage into actionable categories. Use when the user says "run code review" or "review this 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。
Structured peer-review of academic papers.
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。
Brand onboarding setup skill. Captures a client's visual identity, content patterns, audience, and goals through evidence capture + pre-filled client doc + structured intake. Writes context/brand-style.md as the foundation for all social skills. Run once per client before using /social-creative-designer, /content-calendar, or /caption-writer.
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。
Builds a month of social media post ideas for SMBs. Takes brand context, platforms, and goals and produces a structured content calendar with post topics, formats, angles, and visual direction for each slot. Output saves to context/content-calendar.md for use by /caption-writer and /social-creative-designer. Supports Instagram, LinkedIn, Facebook, TikTok, and X.
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 structured documentation templates for components, patterns, or guidelines within a design system.
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。
An engineering-grade marketing system organized around two surface modes — a deep black canvas for hero and footer chapters and a flat paper-white canvas for body content — connected by a single, almost violently saturated NVIDIA Green accent that carries every CTA, every active tab, and the small decorative corner squares that mark out cards. The system is unapologetically angular: 2px radius across every surface, tight bold sans-serif typography in NVIDIA's proprietary EMEA cut, and a hairline gray rule that separates dense multi-column technical content. There is no decorative gradient, no atmospheric mesh, no soft drop shadow — just black, white, gray, and green stacked into a structured editorial grid that scales from product cards to massive industry landing pages without bending its rules.
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。
Runs a structured clarifying interview for new project requests before building. Instead of writing code, it outputs a fully specified prompt.md for a fresh agent session to execute, preventing expensive mistakes.
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。
Translate a vague request or GitHub issue into a concrete, testable spec using five structured elements (problem, scope, acceptance criteria, edge cases, success criterion) before writing any code. Prevents scope creep, PR churn, and mid-implementation pivots.
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。
Guides users through a structured interview to clarify their AI agent idea and produces an Agent Requirement Document (ARD).
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。
Production-grade Python and AI engineering standards. Use for ANY Python work — FastAPI services, LangChain/LangGraph agents, RAG pipelines, LLM API integrations, document processing, or data scripts — whether writing new code, reviewing, refactoring, or debugging. Enforces typed Pydantic boundaries, structured errors and logging, LLM call timeouts/retries/output validation, secret management, async correctness, and tested 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。
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。
Manage persistent coding sessions across Claude Code, Codex, Antigravity (agy), Cursor, and OpenCode engines. Use when orchestrating multi-engine coding agents, starting/sending/stopping sessions, running multi-agent council collaborations, cross-session messaging, ultraplan deep planning, ultrareview parallel code review, autoloop autonomous workspace iteration, ultraapp building deployable web apps from a structured Q&A interview, switching models/tools at runtime, exposing the orchestrator's 69 tools as an MCP server to Hermes Agent / Claude Desktop / Cursor / Cline / Continue / Zed / Windsurf / Goose, or running as an Agent Client Protocol (ACP) agent that Zed / JetBrains / Neovim / Emacs / VS Code / dsh can drive directly. Triggers on "start a session", "send to session", "run council", "ultraplan", "ultrareview", "autoloop", "ultraapp", "Forge tab", "build a web app", "one-click app", "AppSpec", "autonomous iteration", "iterate until goal", "deep paper review", "auto research", "switch model", "multi-agent", "coding session", "session inbox", "cursor agent", "opencode", "mcp server", "clawo-mcp", "hermes mcp", "model context protocol", "ultracode", "dynamic workflow", "fanout", "fan-out", "best-of-N", "steer turn", "interrupt turn", "fork thread", "rollback turns", "acp", "agent client protocol", "clawo acp", "zed agent", "jetbrains agent", "external agent", "dsh subagent", "deepseek harness".
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, read source pages, and retrieve structured records with Firecrawl. Use for current information, finding sources, reading a supplied URL, or looking up data-provider capabilities.
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 Human Interface Guidelines as structured design values — system colors (incl. increased-contrast variants), the full Dynamic Type ramp, Liquid Glass rules and budgets, layout and hit-target metrics, SF Symbols, and HIG-to-SwiftUI component mappings. Use when styling or reviewing any Apple-platform UI: picking a color, size, spacing, corner radius, or hit target; writing .glassEffect / material code; choosing an SF Symbol; or answering 'what does the HIG say about X'.
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 the user has a research question and needs a complete experiment package — design document, runnable code, results (measured or simulated with honest provenance), publication-grade figures, structured report. Single-stage, no Python runtime.
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。