Crewai

CrewAI

Overview

This public intake copy packages plugins/antigravity-awesome-skills-claude/skills/crewai from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.

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

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Capabilities, Prerequisites, Scope, Ecosystem, Patterns, Collaboration.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• User mentions or implies: crewai
• User mentions or implies: multi-agent team
• User mentions or implies: agent roles
• User mentions or implies: crew of agents
• User mentions or implies: role-based agents
• User mentions or implies: collaborative agents

Operating Table

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
2. Read the overview and provenance files before loading any copied upstream support files.
3. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
4. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
5. Validate the result against the upstream expectations and the evidence you can point to in the copied files.
6. Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
7. Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify.

Imported Workflow Notes

Imported: Capabilities

• Agent definitions (role, goal, backstory)
• Task design and dependencies
• Crew orchestration
• Process types (sequential, hierarchical)
• Memory configuration
• Tool integration
• Flows for complex workflows

Examples

Example 1: Ask for the upstream workflow directly

Use @crewai to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @crewai against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @crewai for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @crewai using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
• Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
• Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
• Treat generated examples as scaffolding; adapt them to the concrete task before execution.
• Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills-claude/skills/crewai, fails to mention provenance, or does not use any copied source files at all. Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task. Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @00-andruia-consultant - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

Imported Reference Notes

Imported: Prerequisites

• 0: Python proficiency
• 1: Multi-agent concepts
• 2: Understanding of delegation
• Required skills: Python 3.10+, crewai package, LLM API access

Imported: Scope

• 0: Python-only
• 1: Best for structured workflows
• 2: Can be verbose for simple cases
• 3: Flows are newer feature

Imported: Ecosystem

Primary

• CrewAI framework
• CrewAI Tools

Common_integrations

• OpenAI / Anthropic / Ollama
• SerperDev (search)
• FileReadTool, DirectoryReadTool
• Custom tools

Platforms

• Python applications
• FastAPI backends
• Enterprise deployments

Imported: 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:
    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
    )

main.py

crew = ContentCrew() result = crew.crew().kickoff(inputs={"topic": "AI Agents in 2025"})

Hierarchical Process

Manager agent delegates to workers

When to use: Complex tasks needing coordination

from crewai import Crew, Process

Define specialized agents

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..." )

Hierarchical crew - manager coordinates

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 )

Manager decides:

- Which agent handles which task

- When to delegate

- How to combine results

result = crew.kickoff()

Planning Feature

Generate execution plan before running

When to use: Complex workflows needing structure

from crewai import Crew, Process

Enable planning

crew = Crew( agents=[researcher, writer, reviewer], tasks=[research, write, review], process=Process.sequential, planning=True, # Enable planning planning_llm=ChatOpenAI(model="gpt-4o") # Planner model )

With planning enabled:

1. CrewAI generates step-by-step plan

2. Plan is injected into each task

3. Agents see overall structure

4. More consistent results

result = crew.kickoff()

Access the plan

print(crew.plan)

Memory Configuration

Enable agent memory for context

When to use: Multi-turn or complex workflows

from crewai import Crew

Memory types:

- Short-term: Within task execution

- Long-term: Across executions

- Entity: About specific entities

crew = Crew( agents=[...], tasks=[...], memory=True, # Enable all memory types verbose=True )

Custom memory config

from crewai.memory import LongTermMemory, ShortTermMemory

crew = Crew( agents=[...], tasks=[...], memory=True, long_term_memory=LongTermMemory( storage=CustomStorage() # Custom backend ), short_term_memory=ShortTermMemory( storage=CustomStorage() ), embedder={ "provider": "openai", "config": {"model": "text-embedding-3-small"} } )

Memory helps agents:

- Remember previous interactions

- Build on past work

- Maintain consistency

Flows for Complex Workflows

Event-driven orchestration with state

When to use: Complex, multi-stage workflows

from crewai.flow.flow import Flow, listen, start, and_, or_, router

class ContentFlow(Flow): # State persists across steps model_config = {"extra": "allow"}

@start()
def gather_requirements(self):
    """First step - gather inputs."""
    self.topic = self.inputs.get("topic", "AI")
    self.style = self.inputs.get("style", "professional")
    return {"topic": self.topic}

@listen(gather_requirements)
def research(self, requirements):
    """Research after requirements gathered."""
    research_crew = ResearchCrew()
    result = research_crew.crew().kickoff(
        inputs={"topic": requirements["topic"]}
    )
    self.research = result.raw
    return result

@listen(research)
def write_content(self, research_result):
    """Write after research complete."""
    writing_crew = WritingCrew()
    result = writing_crew.crew().kickoff(
        inputs={
            "research": self.research,
            "style": self.style
        }
    )
    return result

@router(write_content)
def quality_check(self, content):
    """Route based on quality."""
    if self.needs_revision(content):
        return "revise"
    return "publish"

@listen("revise")
def revise_content(self):
    """Revision flow."""
    # Re-run writing with feedback
    pass

@listen("publish")
def publish_content(self):
    """Final publishing."""
    return {"status": "published", "content": self.content}

Run flow

flow = ContentFlow() result = flow.kickoff(inputs={"topic": "AI Agents"})

Custom Tools

Create tools for agents

When to use: Agents need external capabilities

from crewai.tools import BaseTool from pydantic import BaseModel, Field

Method 1: Class-based tool

class SearchInput(BaseModel): query: str = Field(..., description="Search query")

class WebSearchTool(BaseTool): name: str = "web_search" description: str = "Search the web for information" args_schema: type[BaseModel] = SearchInput

def _run(self, query: str) -> str:
    # Implementation
    results = search_api.search(query)
    return format_results(results)

Method 2: Function decorator

from crewai import tool

@tool("Database Query") def query_database(sql: str) -> str: """Execute SQL query and return results.""" return db.execute(sql)

Assign tools to agents

researcher = Agent( role="Researcher", goal="Find information", backstory="...", tools=[WebSearchTool(), query_database] )

Imported: Collaboration

Delegation Triggers

• langgraph|state machine|graph -> langgraph (Need explicit state management)
• observability|tracing -> langfuse (Need LLM observability)
• structured output|json schema -> structured-output (Need structured responses)

Research and Writing Crew

Skills: crewai, structured-output

Workflow:

1. Define researcher and writer agents
2. Create research → analysis → writing pipeline
3. Use structured output for research format
4. Chain tasks with context

Observable Agent Team

Skills: crewai, langfuse

Workflow:

1. Build crew with agents and tasks
2. Add Langfuse callback handler
3. Monitor agent interactions
4. Evaluate output quality

Complex Workflow with Flows

Skills: crewai, langgraph

Workflow:

1. Design workflow with CrewAI Flows
2. Use LangGraph patterns for state
3. Combine crews in flow steps
4. Handle branching and routing

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.