「defi」の検索結果

Orchestrate go-to-market strategy by running positioning, offer construction, and channel allocation as a sequenced workflow during PRD v0.9 Go-to-Market. Triggers on requests to plan launch, define GTM strategy, or when user asks "how do we launch?", "go-to-market", "launch plan", "marketing strategy", "GTM", "launch this product". Chains three framework-grounded sub-skills (Dunford → Hormozi → ORB) and reconciles their outputs.

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Use when working on the Bolt repository and you need to configure or compile code correctly and efficiently. This skill defines the canonical Bolt build workflow, including when and how to run `make`, `cmake --build ...`, and when build reconfiguration is necessary due to added files and test or build target changes.

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Iterative task completion loop with Definition of Done verification. Combines the Ralph Wiggum technique (prompt re-injection via Stop hook) with DoD-based independent verification. Claude proposes DoD criteria, user confirms, then Claude works autonomously. Stop hook re-injects the original prompt and blocks exit until all DoD items are independently verified. "/ralph", "ralph loop", "ralph 루프", "반복 작업", "DoD 루프", "완료 검증 루프", "task loop", "keep going until done"

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"/discuss", "discuss this", "think with me", "is this a good idea?", "what do you think about", "problem definition", "explore this idea", "/discuss --scored", "interview me", "clarify requirements", "요구사항 정리", "인터뷰", "딥 인터뷰", "뭘 만들어야 할지 모르겠어", Korean triggers: "같이 생각해보자", "이거 어떻게 생각해?", "문제 정의", "이게 좋은 아이디어야?", "이거 맞아?", "요구사항이 불명확", "아이디어 구체화"

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Build cloud backend applications with Encore — type-safe backend framework with built-in infrastructure. Use when someone asks to "build a backend", "Encore", "type-safe API framework", "backend with built-in infra", "auto-provision cloud resources", or "backend framework with databases built-in". Covers API definition, databases, pub/sub, cron, and auto-provisioned infrastructure.

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Define reproducible development environments with Dev Containers. Use when a user asks to standardize dev environments, set up VS Code remote containers, create reproducible dev setups, or onboard developers faster.

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Expert guidance for DeepEval, the open-source framework for unit testing LLM applications. Helps developers write test cases, define custom metrics, and integrate LLM quality checks into CI/CD pipelines using a pytest-like interface.

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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.

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Cube is an open-source semantic layer that sits between a data warehouse and analytics applications and serves governed metrics over REST, GraphQL and SQL APIs. Use when a user asks to define cubes, measures and dimensions, add pre-aggregations, build a metrics API or embedded analytics, set up multi-tenant row-level security, or query Cube from React or the REST API.

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Guides a founder from real decisions to a short set of written company values that people can act on: collects incidents, tests each candidate value, writes it as a trade-off with behaviors and a story, and wires it into hiring, feedback and promotion. Use when someone asks to "define our company values", "write a values document", "our values are generic, fix them", "turn our values into interview questions", "what should our culture be before we hire", or wants to check whether the team lives the values it has. Produces VALUES.md, an evidence log and an interview kit.

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Define component variants with CVA. Use when creating variant-based components with Tailwind, building design system tokens, or managing complex conditional class names in React.

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Checkly is a synthetic monitoring platform that runs API checks and Playwright browser checks from locations worldwide and lets you define them as code with the Checkly CLI. Use when a developer asks to set up monitoring as code, write API or browser checks, configure Slack or email alerts, run checks in CI after a deploy, or turn Playwright tests into production monitors.

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Create and maintain brand style guides for consistent visual identity. Use when a user asks to create brand guidelines, build a style guide, define a brand identity, create a design system document, establish brand colors and typography, or produce any brand standards documentation.

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Read and apply a project-specific BUILDING_SPEC.md or DIMENSIONS.md file before generating any architectural elements. Use when: generating floor plans, 3D buildings, or any spatial design where the project has defined custom dimensions, room requirements, or design standards.

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Configure Amazon CloudFront for global content delivery. Set up distributions with S3 and ALB origins, define cache behaviors and TTLs, invalidate cached content, and use Lambda@Edge for request/response manipulation at the edge.

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Argo CD for GitOps continuous delivery on Kubernetes. Use when the user needs to define applications declaratively, sync Kubernetes manifests from Git repositories, manage sync policies, and implement progressive delivery workflows.

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When the user wants to perform load testing or performance testing using Artillery with YAML-based scenario definitions. Also use when the user mentions "artillery," "YAML load test," "WebSocket testing," "Socket.io load test," "scenario-based load testing," or "artillery run." For JavaScript-based load testing, see k6.

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Run containerized applications with Amazon ECS. Define tasks and services, deploy on Fargate for serverless containers or EC2 for full control. Configure load balancing, auto-scaling, and service discovery for production workloads.

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Model Context Protocol expert for building MCP servers, tools, resources, and client integrationsUse when "mcp server, model context protocol, claude code extension, building ai tools, tool definition, mcp transport, stdio transport, sse transport, resource provider, prompt template, mcp, model-context-protocol, claude-code, ai-tools, llm-integration, anthropic, server, protocol" mentioned.

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Use when implementing data governance frameworks, building data catalogs, establishing data lineage, defining data quality rules, or setting up data stewardship programs - covers metadata management, data quality, and complianceUse when ", " mentioned.

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Use when designing disaster recovery strategies, defining RPO/RTO targets, implementing failover mechanisms, or conducting chaos engineering tests - covers active-active, pilot light, and backup strategiesUse when ", " mentioned.

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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, collaborative agents, crewai, multi-agent, agents, orchestration, roles, collaborative-ai" mentioned.

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Expert in Claude Code hooks - user-defined shell commands that execute at specific points in Claude Code's lifecycle. Provides guaranteed automation that doesn't rely on the LLM "remembering" to do something. Essential for enterprise workflows, code quality enforcement, and deterministic behavior in agentic coding. Use when "claude code hooks, automate claude, block tool use, pre tool use, post tool use, session start hook, claude lifecycle, claude-code, automation, hooks, lifecycle, cli, workflow, devtools" mentioned.

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Tools are how AI agents interact with the world. A well-designed tool is the difference between an agent that works and one that hallucinates, fails silently, or costs 10x more tokens than necessary. This skill covers tool design from schema to error handling. JSON Schema best practices, description writing that actually helps the LLM, validation, and the emerging MCP standard that's becoming the lingua franca for AI tools. Key insight: Tool descriptions are more important than tool implementations. The LLM never sees your code - it only sees the schema and description. Use when "agent tool, function calling, tool schema, tool design, mcp server, mcp tool, tool use, build tool for agent, define function, input_schema, tool_use, tool_result, agents, tools, function-calling, mcp, json-schema, anthropic, openai, llm-tools" 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。