crewai — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited crewai (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
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.
Define agents and tasks in YAML (recommended)
When to use: Any CrewAI project
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:
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
research_task: description: | Research the topic: {topic}
Focus on:
Be thorough and cite sources. agent: researcher expected_output: | A comprehensive research report with:
writing_task: description: | Using the research provided, write an article about {topic}.
Requirements:
agent: writer expected_output: "A polished article ready for publication" context:
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: return Task(config=self.tasks_config['writing_task'])
@crew def crew(self) -> Crew: return Crew( agents=self.agents, tasks=self.tasks, process=Process.sequential, verbose=True )
crew = ContentCrew() result = crew.crew().kickoff(inputs={"topic": "AI Agents in 2025"})
Manager agent delegates to workers
When to use: Complex tasks needing coordination
from crewai import Crew, Process
researcher = Agent( role="Research Specialist", goal="Find accurate information", backstory="Expert researcher..." )
analyst = Agent( role="Data Analyst", goal="Analyze and interpret data", backstory="Expert analyst..." )
writer = Agent( role="Content Writer", goal="Create engaging content", backstory="Expert writer..." )
crew = Crew( agents=[researcher, analyst, writer], tasks=[research_task, analysis_task, writing_task], process=Process.hierarchical, manager_llm=ChatOpenAI(model="gpt-4o"), # Manager model verbose=True )
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.