crewaiinc
10 skills with substantive source documentation and an official publisher or observed installation demand. Read each skill’s original documentation through its card.
crewaiincask-docs
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.
- installazioni
- 3
- GitHub Stars
- 47
- Aggiornato
- 6 ott
crewaiincbuild-flow
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, restorefromstateid), checkpointing with CheckpointConfig, @humanfeedback 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 cr
- installazioni
- 3
- GitHub Stars
- 47
- Aggiornato
- 6 ott
crewaiinccall-deployed-crew
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/{kickoffid} 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
- installazioni
- 3
- GitHub Stars
- 47
- Aggiornato
- 6 ott
crewaiinccheck-crewai-api
Current crewai 1.15.x API versus the 0.x API that coding assistants remember: imports, Agent/Task/Crew parameters and defaults, kickoff variants, CrewOutput fields, LLM model strings and provider extras, structured output (outputpydantic, responsemodel, responseformat), guardrails, unified Memory, knowledge embedders, the crewai create crew wizard vs --classic, reset-memories flags, and removed features. Use when writing, reviewing, or debugging any crewai code, before trusting remembered crewai
- installazioni
- 3
- GitHub Stars
- 47
- Aggiornato
- 6 ott
crewaiincconnect-tools-and-mcp
Giving crewAI agents tools and MCP servers on crewai 1.15.x: custom BaseTool with argsschema and @tool, which crewaitools names really exist, tool caching, maxusagecount, 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 crewaitools', 'You are missing the mcp package', 'MCPConnect
- installazioni
- 3
- GitHub Stars
- 47
- Aggiornato
- 6 ott
crewaiincdesign-agent
CrewAI agent design and configuration. Use when creating, configuring, or debugging crewAI agents — choosing role/goal/backstory, selecting LLMs, assigning tools, tuning maxiter/maxrpm/maxexecutiontime, enabling planning/delegation, setting up knowledge sources, using guardrails, or configuring agents in YAML vs code.
- installazioni
- 3
- GitHub Stars
- 47
- Aggiornato
- 6 ott
crewaiincdesign-task
CrewAI task design and configuration. Use when creating, configuring, or debugging crewAI tasks — writing descriptions and expectedoutput, setting up task dependencies with context, configuring output formats (outputpydantic, outputjson, outputfile), using guardrails for validation, enabling humaninput, async execution, markdown formatting, or debugging task execution issues.
- installazioni
- 3
- GitHub Stars
- 47
- Aggiornato
- 6 ott
crewaiincgetting-started
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, building experimental conversational Flows with handleturn()/chat(), or using {variable} interpolation.
- installazioni
- 3
- GitHub Stars
- 47
- Aggiornato
- 6 ott
crewaiinctest-crewai-project
Deterministic, offline testing of CrewAI crews and flows with pytest: a stub BaseLLM (shipped in references/stubllm.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(
- installazioni
- 3
- GitHub Stars
- 47
- Aggiornato
- 6 ott
crewaiincdeploy-to-amp
Getting a CrewAI crew or flow onto CrewAI AMP: the project shape the build expects (uv.lock, [tool.crewai] type, src/ / entry points, a real Flow subclass, absolute imports, a self-contained root), crewai deploy validate, the three deploy paths (CLI create/push, GitHub, ZIP upload) and what each one actually uploads, env vars per deployment and what the CLI re-sends from .env, provider keys and extras, and machine-wide org selection. Use when running crewai deploy create / crewai deploy push / c
- installazioni
- 2
- GitHub Stars
- 47
- Aggiornato
- 6 ott