A collection of skills for AI coding agents that teach best practices for building with CrewAI. Skills follow the Agent Skills format.
CrewAI architecture decisions and project scaffolding. Covers choosing the right abstraction (LLM.call() vs Agent.kickoff() vs Crew.kickoff() vs Flow), CLI scaffolding, YAML configuration, wiring @CrewBase crews, writing Flows with @start/@listen, and variable interpolation.
Use when:
- Starting a new CrewAI project
- Choosing between abstraction levels
- Scaffolding with
crewai create flow - Setting up agents.yaml and tasks.yaml
- Wiring crew.py or main.py
- Debugging common setup issues
CrewAI agent design and configuration. Covers the Role-Goal-Backstory framework, LLM selection, tool assignment, execution tuning (max_iter, max_rpm, max_execution_time), memory and knowledge sources, guardrails, and YAML vs code configuration.
Use when:
- Creating or configuring CrewAI agents
- Choosing role, goal, and backstory
- Assigning tools or selecting LLMs
- Tuning agent parameters
- Setting up knowledge sources or memory
- Debugging agent behavior
CrewAI task design and configuration. Covers writing effective descriptions and expected output, task dependencies with context, structured output (output_pydantic, output_json, output_file), guardrails, human-in-the-loop review, and async execution.
Use when:
- Creating or configuring CrewAI tasks
- Writing task descriptions and expected output
- Setting up task dependencies
- Configuring structured output formats
- Adding guardrails or human review
- Debugging task execution issues
npx skills add crewAIInc/skillsEach skill contains:
SKILL.md- Instructions for the agentreferences/- Supporting documentation (tools catalog, MCP servers, structured output patterns, etc.)
MIT
