3,430 件 ・ 関連度順
概要と使いどころ
Mobile app UI design expert for iOS and Android. Use when designing app interfaces, creating design systems, ensuring accessibility, or following platform guidelines. Covers Material Design 3, Human Interface Guidelines, color theory, typography, and 2025 trends.
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DevOps and CI/CD expert. Use when setting up pipelines, containerizing applications, deploying to Kubernetes, or implementing release strategies. Covers GitHub Actions, Docker, K8s, Terraform, and GitOps.
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HarmonyOS application development expert. Use when building HarmonyOS apps with ArkTS, ArkUI, Stage model, and distributed capabilities. Covers HarmonyOS NEXT (API 12+) best practices.
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Research-first content creation optimized for both human readers and AI search engines (Claude, ChatGPT, Perplexity, Gemini). Creates authentic, authoritative content that becomes the go-to citation source for AI models answering user questions. Use this skill when: - Creating content that should appear in AI search results (Perplexity, ChatGPT, Claude) - Building topical authority to become THE source AI cites for a topic - Launching a new product and need compelling, citable content - Creating blog posts, articles, social media, or press releases - Need content that references real trends, people, and recent events - Want AI-assisted content that doesn't sound AI-generated - Creating thought leadership content in any industry Triggers: "create content for", "write about", "research and write", "find experts for", "content for launch", "blog post about", "article on", "press release for", "AI search", "show up in AI", "Perplexity", "be cited by AI"
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。
Professional copywriter for SaaS and startups. Expert in landing page copy, positioning, messaging, conversion optimization, and voice-of-customer research. Use when writing compelling copy for SaaS products, landing pages, marketing materials, or when you need help with product positioning and messaging strategy.
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。
Applies the reasoning, architectural principles, and AI philosophy of Christopher Manning (natural language processing expert, Stanford University, director of Stanford AI Lab). Use this skill whenever you are discussing natural language processing, LLM architecture, AI research strategy, cognitive science, or the evolution of machine learning. Trigger this skill for questions about AGI timelines, academic vs. industry research trade-offs, linguistic structure in neural networks, modularity in AI design, or evaluating true intelligence versus mere memorization. Channel his pragmatic focus on domain science, adaptability, and competing on ideas rather than raw compute.
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Applies the reasoning style of Daphne Koller (machine learning pioneer, co-founder of Coursera, founder and CEO of Insitro). Use this skill whenever you encounter problems involving AI and machine learning in biology, drug discovery, interdisciplinary collaboration, data generation vs. data mining, or transitioning from academia to industry. Trigger this skill when advising on career trade-offs, building cross-functional teams (especially bridging engineers and domain experts), designing data pipelines, evaluating causality vs. correlation, or applying AI to physical systems ('where bits meet atoms'). Channel her focus on fit-for-purpose data, pragmatism, and disproportionate leverage.
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。
Applies the reasoning of Pieter Abbeel, robotics and reinforcement learning expert, UC Berkeley professor, and co-founder of Covariant. Use this skill whenever you are designing AI systems, tackling Sim2Real transfer, deploying machine learning in the physical world, or evaluating reinforcement learning architectures. Trigger this skill for questions about domain randomization, reward design, bootstrapping real-world AI, robotics hardware assumptions, or shifting from hard-coded rules to data-driven deep learning. It helps ground theoretical AI in physical embodiment and pragmatic 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。
Applies the reasoning of Stuart Russell, AI safety expert, UC Berkeley professor, and co-author of 'Artificial Intelligence: A Modern Approach'. Reach for this skill whenever evaluating AI safety, value alignment, the control problem, existential risk, AI regulation, or autonomous weapons. Use this when the user is discussing objective uncertainty, reinforcement learning risks, AI governance, or the societal impacts of AGI. Trigger this skill to apply his frameworks on provably beneficial AI, assistance games, and red-line regulation, ensuring AI systems remain deferential, uncertain of their objectives, and strictly aligned with human preferences.
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。
Expert SurrealDB 3 architect and developer skill (tracks v3.1.4+). SurrealQL mastery, multi-model data modeling (document, graph, vector, time-series, geospatial), schema design, security, deployment, performance tuning, SDK integration (JS, Python, Go, Rust, Java, .NET, C, PHP, Swift, Kotlin, Ruby), Surrealism WASM extensions, SurrealML scope coverage (preview), built-in MCP (v3.1+) and standalone SurrealMCP, LangChain Python integration, editor tooling (LSP v0.1.6, tree-sitter, CodeMirror v1.0.6, VS Code/JetBrains/Neovim/Zed), and ecosystem integrations (Surrealist v3.9, Surreal-Sync, SurrealFS, SurrealKit v0.7, n8n, Agent Skills, setup-surreal). Universal skill for 30+ AI agents.
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 modern SQL with cloud-native databases, OLTP/OLAP optimization, and advanced query techniques. Expert in performance tuning, data modeling, and hybrid analytical systems.
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 in CrewAI - the leading role-based multi-agent framework used by 60% of Fortune 500 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。
Expert in CrewAI - the leading role-based multi-agent framework used by 60% of Fortune 500 companies. Covers agent design with roles and goals, task definition, crew orchestration, process types (sequential, hierarchical, parallel), memory systems, and flows for complex workflows. Essential for building collaborative AI agent teams. Use when: crewai, multi-agent team, agent roles, crew of agents, role-based agents.
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 query AI-powered knowledge bases from claude-mem observations. Use when users want to create focused "brains" from their observation history, ask questions about past work patterns, or compile expertise on specific topics.
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 expert at creating and refining automated tests using TestDriver.ai
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 expert at creating and refining automated tests using TestDriver.ai
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 expert at creating and refining automated tests using TestDriver.ai
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 needing strategic project analysis from multiple independent expert perspectives. Triggers on business decisions, growth strategy, product direction, competitive analysis, or any situation where diverse C-level opinions reduce blind spots
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 guidance for writing secure, reliable, and performant Claude Code hooks - validates design decisions, enforces best practices, and prevents common pitfalls. Use when creating, reviewing, or debugging Claude Code hooks.
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 a task may benefit from a bounded subagent for implementation, large-codebase discovery at task start, independent review, requested or pre-commit code simplification, or expert work on complex decisions, modeling, automation, or repeated failures. The main agent chooses whether to delegate and accepts the result. Do not use for simple questions or short work with no meaningful review or delegation.
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。
Think and work like an expert Mechanical Design Engineer. Use when a task calls for Mechanical Design Engineer judgment. Reasons from function, datum reference frames, and tolerance budgets; releases inspectable drawings and MBD through ASME Y14.5 GD&T, WC/RSS/Monte Carlo stack-ups, SAE J1739 DFMEA (Action Priority), and Boothroyd–Dewhurst DFM/DFA—not stress plots alone.
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。
Think and work like an expert Mechanical Engineer. Use when a task calls for Mechanical Engineer judgment. Reasons from equilibrium, failure physics, and code-backed allowables; designs through requirements, GD&T, DFMEA, and hand/FEA/test validation with explicit governing failure modes.
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。
Think and work like an expert Medical Parasitologist. Use when a task calls for Medical Parasitologist judgment. Reasons from specimen-stage fit, exposure-based pretest probability, and antigen-versus-antibody kinetics through thick/thin Giemsa films, formalin-ethyl-acetate concentration with trichrome, multiplex PCR, and EITB serology against CDC DPDx and WHO algorithms, while treating pfhrp2/3-deleted RDT false-negatives, single-O&P misses of Strongyloides, and colonization-mistaken-for-infection as first-class failure modes.
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。