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Found 39 Skills
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.
Integrate You.com remote MCP server with crewAI agents for web search, AI-powered answers, and content extraction. - MANDATORY TRIGGERS: crewAI MCP, crewai mcp integration, remote MCP servers, You.com with crewAI, MCPServerHTTP, MCPServerAdapter - Use when: developer mentions crewAI MCP integration, needs remote MCP servers, integrating You.com with crewAI
CrewAI architecture decisions and project scaffolding. Use when starting a new crewAI project, choosing between LLM.call() vs Agent.kickoff() vs Crew.kickoff() vs Flow, scaffolding with 'crewai create flow', setting up YAML config (agents.yaml, tasks.yaml), wiring @CrewBase crew.py, writing Flow main.py with @start/@listen, or using {variable} interpolation.
CrewAI task design and configuration. Use when creating, configuring, or debugging crewAI tasks — writing descriptions and expected_output, setting up task dependencies with context, configuring output formats (output_pydantic, output_json, output_file), using guardrails for validation, enabling human_input, async execution, markdown formatting, or debugging task execution issues.
Multi-agent orchestration framework for autonomous AI collaboration. Use when building teams of specialized agents working together on complex tasks, when you need role-based agent collaboration with memory, or for production workflows requiring sequential/hierarchical execution. Built without LangChain dependencies for lean, fast execution.
Use when "CrewAI", "multi-agent systems", "agent orchestration", "AI crews", or asking about "autonomous agents", "agent collaboration", "role-based agents", "agent workflows", "AI team coordination"
Query the official CrewAI documentation for answers. Use when the user has a CrewAI question that isn't fully covered by the getting-started, design-agent, design-task skills — e.g., specific API details, configuration options, advanced features, troubleshooting errors, enterprise features, tool references, or anything where the latest docs are the best source of truth.
CrewAI agent design and configuration. Use when creating, configuring, or debugging crewAI agents — choosing role/goal/backstory, selecting LLMs, assigning tools, tuning max_iter/max_rpm/max_execution_time, enabling planning/code execution/delegation, setting up knowledge sources, using guardrails, or configuring agents in YAML vs code.
Deterministic, offline testing of CrewAI crews and flows with pytest: a stub BaseLLM (shipped in references/stub_llm.py) that drives text, structured output and tool calls with no API key, asserting on the prompts crewai built, testing guardrails and flow routing, what `crewai test` really does, event listeners for debugging, and the exact tracing/telemetry env var values. Use when writing tests for a crewai project, mocking or stubbing the LLM, writing a custom BaseLLM, seeing `TypeError: ... call() got an unexpected keyword argument 'from_task'`, `ValueError: OPENAI_API_KEY is required` in CI, `TraceGrantError` in pytest, running `crewai test`, adding a BaseEventListener, or setting CREWAI_TRACING_ENABLED / CREWAI_DISABLE_TELEMETRY / OTEL_SDK_DISABLED.
Calling a crew or flow deployed on CrewAI AMP over HTTP: the bearer token and deployment URL, GET /inputs, POST /kickoff with the {"inputs": {...}} body, which input keys are required ({placeholder} tokens), polling GET /status/{kickoff_id} with a deadline and backoff, terminal states and where the result lives, webhooks, POST /resume, slow first calls, and writing crews that stay correct when they are kicked off repeatedly or concurrently. Use when writing a client, script, backend or frontend that calls a deployed crew, when a kickoff returns 422 "Missing inputs: ...", when a status poll never ends, says "NOT FOUND" or the result field is empty, when /resume returns 422 for executionId, when a run's prompt shows another run's data, or when the user pastes a kickoff_id, a /kickoff curl, or 'Template variable ... not found in inputs dictionary'.
Building CrewAI Flows on crewai 1.15.x: structured (Pydantic) vs dict state and state.id, @start/@listen/@router wiring, or_/and_ semantics, router labels vs method names, @persist (correct import, where it stores state, restore_from_state_id), checkpointing with CheckpointConfig, @human_feedback and HumanFeedbackResult.feedback, plot(), calling crews and agents from flow methods, kickoff inputs, and the `crewai create flow` scaffold. Use when writing or debugging a Flow subclass or main.py with `from crewai.flow import ...`, when a listener never fires or fires twice, or when you see errors like "listen condition 'x' references the handler name 'x'", "cannot import name 'persist'", "'StateWithId' object is not subscriptable", "Flow state model must have an 'id' field", "--inputs requires a declarative flow definition", "invoked synchronously from within a running event loop", or HumanFeedbackCollapseError.
Giving crewAI agents tools and MCP servers on crewai 1.15.x: custom BaseTool with args_schema and @tool, which crewai_tools names really exist, tool caching, max_usage_count, ToolFailure, Agent(mcps=[...]) string and MCPServerStdio/HTTP/SSE forms, rewritten MCP tool names, MCPServerAdapter, timeouts, and what works on a hosted deployment. Use when writing or debugging a tool or MCP connection, when you see 'cannot import name BaseTool from crewai_tools', 'You are missing the mcp package', 'MCPConnectionError', 'Operation timed out after 30 seconds', 'reached its usage limit', 'Invalid MCP reference', 'Anthropic function name ... must start with a letter', an agent that never calls an MCP tool, mcps=[...] that yields no tools (for example an https:// string under akickoff or on CrewAI AMP), or a stdio MCP server that works locally but not after deploy.