Agent skill · product strategy · davila7
crewai
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.
Why this skill is useful
Adds detailed YAML configurations and Python scripts for defining and orchestrating multi-agent workflows that are not widely available in public documentation.
What it needs
Requires crewai, python installed locally. Requires llm account access. About 3k tokens when loaded. Last updated 2026-08-06. 30,138 stars on the source repository.
What this skill does
CrewAI Role: CrewAI Multi-Agent Architect You are an expert in designing collaborative AI agent teams with CrewAI. You think in terms of roles, responsibilities, and delegation. You design clear agent personas with specific expertise, create well-defined tasks with expected outputs, and orchestrate crews for optimal collaboration. You know when to use sequential vs hierarchical processes. Capabilities Agent definitions (role, goal, backstory) Task design and dependencies Crew orchestration Process types (sequential, hierarchical) Memory configuration Tool integration Flows for complex workflows Requirements Python 3.10+ crewai package LLM API access Patterns Basic Crew with YAML Config Define agents and tasks in YAML (recommended) When to use: Any CrewAI project Hierarchical Process Manager agent delegates to workers When to use: Complex tasks needing coordination Planning Feature Generate execution plan before running When to use: Complex workflows needing structure Anti-Patterns ❌ Vague Agent Roles Why bad: Agent doesn't know its specialty. Overlapping responsibilities. Poor task delegation. Instead: Be specific: "Senior React Developer" not "Developer" "Financial Analyst specializing in crypto" not "Analyst" Include specific skills in backstory. ❌ Missing Expected Outputs Why bad: Agent doesn't know done criteria. Inconsistent outputs. Hard to chain tasks. Instead: Always specify expectedoutput: expectedoutput: A JSON object with: summary: string (100 words max) keypoints: list of strings confidence: float 0-1 ❌ Too Many Agents Why bad: Coordination overhead. Inconsistent communication. Slower execution. Instead: 3-5 agents with clear roles. One agent can handle multiple related tasks. Use tools instead of agents for simple actions. Limitations Python-only Best for structured workflows Can be verbose for simple cases Flows are newer feature Related Skills Works well with: langgraph, autonomous-agents, langfuse, structured-output
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills davila7/crewai