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概要と使いどころ
Design and operate bounded multi-agent workflows with task decomposition, dependency graphs, ownership, handoff contracts, shared-state controls, approvals, recovery, and synthesis. Use when a task contains genuinely independent workstreams, specialized roles, parallel research or implementation, reviewer-worker loops, or coordination problems that one agent should not execute sequentially.
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Deploy trained machine learning models as production-ready services using REST APIs, containers, serverless functions, and orchestration platforms. Use when the user requests model deployment or provides relevant inputs for this workflow.
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 Data Engineer. Use when a task calls for Data Engineer judgment. Reasons from idempotent ELT, medallion bronze/silver/gold, Kimball grain and SCD2, CDC/Debezium and watermark incremental loads, dbt/GX quality gates, Airflow/Dagster orchestration, Iceberg/Delta lakehouse MERGE, data contracts and freshness SLIs while treating silent join drops, duplicate amplification, schema drift, and green-DAG-wrong-numbers as first-class failure modes.
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A vibrant claymation-meets-data interface for Clay.com (GTM data-orchestration platform). Anchors on white canvas with dark-navy primary CTAs, custom rounded display type, and saturated single-color feature cards — hot pink, deep teal, lavender, peach, ochre — that punctuate long-scroll explainer pages. Brand voltage comes from 3D-rendered claymation illustrations (mountains, characters, mascots) used as full-bleed hero artifacts and the bright multi-color card surfaces showing product UI fragments.
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。
Maxim Master Design Intelligence — full-spectrum UI/UX orchestration. Activates all design sub-domain skills in parallel. Use when a task spans multiple design domains simultaneously: product design strategy, interaction design, information architecture, usability heuristics, design critique, cross-platform consistency, design leadership. The master conductor of all Maxim design 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。
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 production-ready LLM applications, RAG systems, and intelligent agents. Use when implementing AI features, chatbots, vector search, or agent orchestration.
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 production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations.
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, debug, and optimize Docker configurations. Use when a user asks to create a Dockerfile, fix Docker build errors, optimize image size, write docker-compose files, debug container issues, set up multi-stage builds, or troubleshoot networking between containers. Covers Dockerfile best practices and compose orchestration.
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。
CrewAI is a Python framework for building multi-agent AI systems: you define agents with roles, goals and tools, give them tasks, and run them as a crew with sequential or hierarchical processes, memory and delegation. Use when a user wants to build a multi-agent workflow, set up a CrewAI project, write custom CrewAI tools, or run a crew from the crewai CLI.
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。
Dagster is a data pipeline orchestrator built around the concept of software-defined assets. Learn to define assets, ops, jobs, schedules, sensors, and resources for building maintainable data platforms.
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。
Run Codex CLI, Claude Code, or other coding agents as background processes for programmatic control. Use when a user asks to run a coding agent, delegate a task to another AI, spawn a sub-agent, run Claude Code in the background, or orchestrate multiple coding agents on separate tasks.
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。
ClearML is an open-source MLOps platform that records machine-learning experiments, versions datasets, chains tasks into pipelines and runs them on remote machines through agents and queues. Use when a user asks to "track experiments with ClearML", "log metrics, artifacts and models", "version a dataset", "run training on a remote GPU with clearml-agent", "build a ClearML pipeline", "run hyperparameter optimization", or "self-host ClearML Server". Covers the clearml 2.x Python SDK, clearml-agent 3.x and ClearML Server 2.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。
Build serverless applications with Azure Functions. Create HTTP and event-driven functions with input/output bindings, configure triggers for queues, timers, and blob storage. Use Durable Functions for stateful orchestration 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。
Run autonomous AI-driven penetration tests on web applications using tools like Shannon, PentAGI, and similar frameworks. Use when tasks involve setting up automated penetration testing pipelines, combining AI agents with security tools (nmap, subfinder, nuclei, sqlmap), building autonomous exploit chains, generating pentest reports with proof-of-concept exploits, or integrating AI pentesting into CI/CD pipelines. Covers the full pentest lifecycle from reconnaissance to reporting using AI orchestration.
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。
AG2 (formerly AutoGen) is an open-source Python framework for building AI agents and multi-agent systems that use tools, human approval and structured conversations. Use it when you need agents that call functions, collaborate in a group chat, execute code, or when you are migrating AutoGen or pyautogen code to AG2 v1 or ag2-classic.
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。
Apache Airflow is a platform for programmatically authoring, scheduling, and monitoring workflows. Learn to write DAGs, use operators, set up connections, configure scheduling, and deploy with Docker Compose.
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 at organizing and optimizing monorepos. Covers Turborepo, Nx, pnpm workspaces, task orchestration, caching, and dependency management. Knows how to scale codebases without scaling build times. Use when "monorepo, turborepo, nx, pnpm workspace, shared packages, workspace, task caching, monorepo, turborepo, nx, pnpm, workspace, caching" mentioned.
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。
Kubernetes is the operating system for the cloud. It schedules containers, handles networking, manages storage, and keeps your apps running. But K8s has a steep learning curve - abstractions on abstractions on abstractions. This skill covers the core concepts (Pods, Deployments, Services), production patterns (health checks, resource limits, HPA), and the gotchas that catch everyone (namespace confusion, service discovery, secret management). 2025 reality: You probably don't need to run your own cluster. Use managed K8s (EKS, GKE, AKS) unless you have a very good reason not to. Focus on writing good manifests, not managing etcd. Use when "kubernetes, k8s, kubectl, deployment manifest, helm chart, kustomize, pod, service yaml, ingress, horizontal pod autoscaler, hpa, kubernetes, k8s, containers, orchestration, devops, cloud-native, helm, kustomize" mentioned.
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 LangGraph - the production-grade framework for building stateful, multi-actor AI applications. Covers graph construction, state management, cycles and branches, persistence with checkpointers, human-in-the-loop patterns, and the ReAct agent pattern. Used in production at LinkedIn, Uber, and 400+ companies. This is LangChain's recommended approach for building agents. Use when "langgraph, langchain agent, stateful agent, agent graph, react agent, agent workflow, multi-step agent, langgraph, langchain, agents, state-machine, workflow, graph, ai-agents, orchestration" mentioned.
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。
Container specialist for Docker, Docker Compose, image optimization, and container orchestration fundamentalsUse when "docker, dockerfile, container, docker-compose, image, containerize, docker build, multi-stage build, docker, containers, dockerfile, docker-compose, images, kubernetes, devops, containerization, microservices" mentioned.
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 patterns for Azure Functions development including isolated worker model, Durable Functions orchestration, cold start optimization, and production patterns. Covers .NET, Python, and Node.js programming models. Use when "azure function, azure functions, durable functions, azure serverless, function app, azure, serverless, functions, durable-functions, cloud" mentioned.
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 for building, maintaining, and scaling bioinformatics workflows. Covers Nextflow, Snakemake, WDL/Cromwell, container orchestration, and best practices for reproducible computational biology. Use when ", " mentioned.
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。
Global content at the speed of AI. This skill covers the full spectrum of AI-powered localization: translation, cultural adaptation, voice localization, visual adaptation, and the orchestration required to take content from one language/market to 50. Traditional localization is slow, expensive, and creates bottlenecks. AI-powered localization enables simultaneous global launches, personalized content by market, and continuous localization as content evolves. But localization isn't just translation. It's cultural adaptation. It's knowing that colors mean different things in different cultures, that humor doesn't translate, that some messages must be completely reimagined for new markets. Use when "localize, localization, translate, translation, multi-language, international, global, multilingual, market adaptation, cultural adaptation, other languages, different countries, localization, translation, international, global, multilingual, cultural-adaptation, ai-dubbing" mentioned.
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。