
Crewai
Design role-based CrewAI agent teams with tasks, crews, processes, memory, and flows for collaborative automation in Python.
Overview
CrewAI is an agent skill for the Build phase that helps solo builders design role-based multi-agent crews, tasks, and orchestration in the CrewAI Python framework.
Install
npx skills add https://github.com/sickn33/antigravity-awesome-skills --skill crewaiWhat is this skill?
- Covers agent personas (role, goal, backstory), task design, dependencies, and crew orchestration
- Documents process types: sequential, hierarchical, and parallel collaboration patterns
- Includes memory configuration, tool integration, and Flows for complex workflows
- Architect persona emphasizes delegation, decomposition, and when to pick each process type
- Prerequisites call out Python 3.10+, crewai package, and LLM API access
Adoption & trust: 559 installs on skills.sh; 40.1k GitHub stars; 2/3 security scanners passed (skills.sh audits).
What problem does it solve?
You need several LLM agents to collaborate with clear roles and ordered work, but ad-hoc prompts do not scale to dependable crews.
Who is it for?
Developers shipping Python agent automations who want Fortune-500-style crew patterns without reinventing orchestration from scratch.
Skip if: Non-Python stacks, single-prompt chatbots with no delegation model, or builders who only need a one-shot script with no multi-agent topology.
When should I use this skill?
Expert guidance on CrewAI agent design, task definition, crew orchestration, process types, memory, tools, and flows.
What do I get? / Deliverables
You get concrete CrewAI-oriented designs for agents, tasks, process choice, memory, tools, and flows ready to implement in Python.
- Agent and task specifications
- Crew process recommendation
- Flow or memory configuration guidance
Recommended Skills
Journey fit
Build is the canonical phase because the skill focuses on implementing multi-agent systems—roles, tasks, orchestration, and tools—not on shipping store listings or growth analytics. Agent-tooling is the right shelf for framework-specific expertise on CrewAI crews, hierarchical versus sequential processes, and flow design.
How it compares
Framework design skill for CrewAI crews, not a hosted MCP server or a no-code chat widget.
Common Questions / FAQ
Who is crewai for?
Solo builders and small teams implementing multi-agent workflows in Python with the crewai package who need help with roles, tasks, and crew processes.
When should I use crewai?
Use it during build when defining agent personas, task graphs, sequential or hierarchical crews, memory, tools, or Flows for complex agent pipelines.
Is crewai safe to install?
Treat it as third-party procedural guidance from the vibeship-spawner lineage; confirm license (Apache 2.0 noted in metadata) and review the Security Audits panel on this page before install.
SKILL.md
READMESKILL.md - Crewai
# 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. **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. ### Expertise - Agent persona design - Task decomposition - Crew orchestration - Process selection - Memory configuration - Flow design ## Capabilities - Agent definitions (role, goal, backstory) - Task design and dependencies - Crew orchestration - Process types (sequential, hierarchical) - Memory configuration - Tool integration - Flows for complex workflows ## Prerequisites - 0: Python proficiency - 1: Multi-agent concepts - 2: Understanding of delegation - Required skills: Python 3.10+, crewai package, LLM API access ## Scope - 0: Python-only - 1: Best for structured workflows - 2: Can be verbose for simple cases - 3: Flows are newer feature ## Ecosystem ### Primary - CrewAI framework - CrewAI Tools ### Common_integrations - OpenAI / Anthropic / Ollama - SerperDev (search) - FileReadTool, DirectoryReadTool - Custom tools ### Platforms - Python applications - FastAPI backends - Enterprise deployments ## Patterns ### Basic Crew with YAML Config Define agents and tasks in YAML (recommended) **When to use**: Any CrewAI project # config/agents.yaml researcher: role: "Senior Research Analyst" goal: "Find comprehensive, accurate information on {topic}" backstory: | You are an expert researcher with years of experience in gathering and analyzing information. You're known for your thorough and accurate research. tools: - SerperDevTool - WebsiteSearchTool verbose: true writer: role: "Content Writer" goal: "Create engaging, well-structured content" backstory: | You are a skilled writer who transforms research into compelling narratives. You focus on clarity and engagement. verbose: true # config/tasks.yaml research_task: description: | Research the topic: {topic} Focus on: 1. Key facts and statistics 2. Recent developments 3. Expert opinions 4. Contrarian viewpoints Be thorough and cite sources. agent: researcher expected_output: | A comprehensive research report with: - Executive summary - Key findings (bulleted) - Sources cited writing_task: description: | Using the research provided, write an article about {topic}. Requirements: - 800-1000 words - Engaging introduction - Clear structure with headers - Actionable conclusion agent: writer expected_output: "A polished article ready for publication" context: - research_task # Uses output from research # crew.py from crewai import Agent, Task, Crew, Process from crewai.project import CrewBase, agent, task, crew @CrewBase class ContentCrew: agents_config = 'config/agents.yaml' tasks_config = 'config/tasks.yaml' @agent def researcher(self) -> Agent: return Agent(config=self.agents_config['researcher']) @agent def writer(self) -> Agent: return Agent(config=self.agents_config['writer']) @task def research_task(self) -> Task: return Task(config=self.tasks_config['research_task']) @task def writing_task(self) -> Task: