UNPKG

skills-ws

Version:

87 agent skills for AI coding assistants — marketing, growth, web3, dev, design & operations. Built for OpenClaw, Claude Code, Cursor, and Codex.

68 lines (57 loc) • 2.11 kB
## CrewAI: Multi-Agent Teams ```python # pip install crewai crewai-tools from crewai import Agent, Task, Crew, Process from crewai_tools import SerperDevTool, ScrapeWebsiteTool # Define specialized agents researcher = Agent( role="Senior Research Analyst", goal="Find comprehensive, accurate information about the given topic", backstory="You're a seasoned researcher with 15 years of experience in market analysis.", tools=[SerperDevTool(), ScrapeWebsiteTool()], verbose=True, allow_delegation=False, llm="gpt-5.5", ) writer = Agent( role="Technical Writer", goal="Create clear, engaging content based on research findings", backstory="You're a technical writer who excels at making complex topics accessible.", verbose=True, llm="gpt-5.5", ) editor = Agent( role="Editor", goal="Review and polish the content for accuracy, clarity, and engagement", backstory="You're a meticulous editor with an eye for detail and factual accuracy.", verbose=True, llm="gpt-5.5", ) # Define tasks research_task = Task( description="Research the current state of {topic}. Find key trends, statistics, and expert opinions.", expected_output="A comprehensive research brief with key findings, statistics, and sources.", agent=researcher, ) writing_task = Task( description="Write a 1500-word article based on the research brief.", expected_output="A well-structured article with introduction, key sections, and conclusion.", agent=writer, context=[research_task], # Uses output from research ) editing_task = Task( description="Edit the article for clarity, accuracy, and engagement. Fix any factual errors.", expected_output="A polished, publication-ready article.", agent=editor, context=[writing_task], ) # Assemble crew crew = Crew( agents=[researcher, writer, editor], tasks=[research_task, writing_task, editing_task], process=Process.sequential, # or Process.hierarchical with a manager verbose=True, ) result = crew.kickoff(inputs={"topic": "AI agents in production"}) ``` ---