[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71S11BGDBRPB6W5SWJC0QEG":3,"skill-same:01M71S11BGDBRPB6W5SWJC0QEG":63},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":37,"linkOnly":38,"markdownContent":39,"markdownBody":40,"frontMatter":41,"refName":43,"commitSha":44,"skillDirectory":45,"forksCount":13,"repositoryHtmlUrl":46,"repositoryHomepageUrl":6,"ownerHtmlUrl":47,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":48,"install":49,"resources":62},"01M71S11BGDBRPB6W5SWJC0QEG","crewai",null,"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 (s...","javiertarazon\u002Fide-agente-free-jt7-","javiertarazon","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F153141207?v=4","01M71R52TB3S9HXKH6WSNT8A7V","01M71R52VG5KS2DKDDH4GDQ30A",0,"MIT","https:\u002F\u002Fgithub.com\u002Fjaviertarazon\u002Fide-agente-free-jt7-\u002Fblob\u002Frelease\u002Fv4.2.11-panel-pro\u002F.github\u002Fskills\u002Fcrewai\u002FSKILL.md",[],[5,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36],"task","role","Role-Based","crew","process","goals","apache","roles","multi","with","unknown","orchestration","agent framework","spawner","design","expert","types","definition","fortune","2026-11-10 13:02:02",false,"---\nname: crewai\ndescription: \"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 (s...\"\nsource: vibeship-spawner-skills (Apache 2.0)\nrisk: unknown\n---\n\n# CrewAI\n\n**Role**: CrewAI Multi-Agent Architect\n\nYou are an expert in designing collaborative AI agent teams with CrewAI. You think\nin terms of roles, responsibilities, and delegation. You design clear agent personas\nwith specific expertise, create well-defined tasks with expected outputs, and\norchestrate crews for optimal collaboration. You know when to use sequential vs\nhierarchical processes.\n\n## Capabilities\n\n- Agent definitions (role, goal, backstory)\n- Task design and dependencies\n- Crew orchestration\n- Process types (sequential, hierarchical)\n- Memory configuration\n- Tool integration\n- Flows for complex workflows\n\n## Requirements\n\n- Python 3.10+\n- crewai package\n- LLM API access\n\n## Patterns\n\n### Basic Crew with YAML Config\n\nDefine agents and tasks in YAML (recommended)\n\n**When to use**: Any CrewAI project\n\n```python\n# config\u002Fagents.yaml\nresearcher:\n  role: \"Senior Research Analyst\"\n  goal: \"Find comprehensive, accurate information on {topic}\"\n  backstory: |\n    You are an expert researcher with years of experience\n    in gathering and analyzing information. You're known\n    for your thorough and accurate research.\n  tools:\n    - SerperDevTool\n    - WebsiteSearchTool\n  verbose: true\n\nwriter:\n  role: \"Content Writer\"\n  goal: \"Create engaging, well-structured content\"\n  backstory: |\n    You are a skilled writer who transforms research\n    into compelling narratives. You focus on clarity\n    and engagement.\n  verbose: true\n\n# config\u002Ftasks.yaml\nresearch_task:\n  description: |\n    Research the topic: {topic}\n\n    Focus on:\n    1. Key facts and statistics\n    2. Recent developments\n    3. Expert opinions\n    4. Contrarian viewpoints\n\n    Be thorough and cite sources.\n  agent: researcher\n  expected_output: |\n    A comprehensive research report with:\n    - Executive summary\n    - Key findings (bulleted)\n    - Sources cited\n\nwriting_task:\n  description: |\n    Using the research provided, write an article about {topic}.\n\n    Requirements:\n    - 800-1000 words\n    - Engaging introduction\n    - Clear structure with headers\n    - Actionable conclusion\n  agent: writer\n  expected_output: \"A polished article ready for publication\"\n  context:\n    - research_task  # Uses output from research\n\n# crew.py\nfrom crewai import Agent, Task, Crew, Process\nfrom crewai.project import CrewBase, agent, task, crew\n\n@CrewBase\nclass ContentCrew:\n    agents_config = 'config\u002Fagents.yaml'\n    tasks_config = 'config\u002Ftasks.yaml'\n\n    @agent\n    def researcher(self) -> Agent:\n        return Agent(config=self.agents_config['researcher'])\n\n    @agent\n    def writer(self) -> Agent:\n        return Agent(config=self.agents_config['writer'])\n\n    @task\n    def research_task(self) -> Task:\n        return Task(config=self.tasks_config['research_task'])\n\n    @task\n    def writing_task(self) -> Task:\n        return Task(config\n```\n\n### Hierarchical Process\n\nManager agent delegates to workers\n\n**When to use**: Complex tasks needing coordination\n\n```python\nfrom crewai import Crew, Process\n\n# Define specialized agents\nresearcher = Agent(\n    role=\"Research Specialist\",\n    goal=\"Find accurate information\",\n    backstory=\"Expert researcher...\"\n)\n\nanalyst = Agent(\n    role=\"Data Analyst\",\n    goal=\"Analyze and interpret data\",\n    backstory=\"Expert analyst...\"\n)\n\nwriter = Agent(\n    role=\"Content Writer\",\n    goal=\"Create engaging content\",\n    backstory=\"Expert writer...\"\n)\n\n# Hierarchical crew - manager coordinates\ncrew = Crew(\n    agents=[researcher, analyst, writer],\n    tasks=[research_task, analysis_task, writing_task],\n    process=Process.hierarchical,\n    manager_llm=ChatOpenAI(model=\"gpt-4o\"),  # Manager model\n    verbose=True\n)\n\n# Manager decides:\n# - Which agent handles which task\n# - When to delegate\n# - How to combine results\n\nresult = crew.kickoff()\n```\n\n### Planning Feature\n\nGenerate execution plan before running\n\n**When to use**: Complex workflows needing structure\n\n```python\nfrom crewai import Crew, Process\n\n# Enable planning\ncrew = Crew(\n    agents=[researcher, writer, reviewer],\n    tasks=[research, write, review],\n    process=Process.sequential,\n    planning=True,  # Enable planning\n    planning_llm=ChatOpenAI(model=\"gpt-4o\")  # Planner model\n)\n\n# With planning enabled:\n# 1. CrewAI generates step-by-step plan\n# 2. Plan is injected into each task\n# 3. Agents see overall structure\n# 4. More consistent results\n\nresult = crew.kickoff()\n\n# Access the plan\nprint(crew.plan)\n```\n\n## Anti-Patterns\n\n### ❌ Vague Agent Roles\n\n**Why bad**: Agent doesn't know its specialty.\nOverlapping responsibilities.\nPoor task delegation.\n\n**Instead**: Be specific:\n- \"Senior React Developer\" not \"Developer\"\n- \"Financial Analyst specializing in crypto\" not \"Analyst\"\nInclude specific skills in backstory.\n\n### ❌ Missing Expected Outputs\n\n**Why bad**: Agent doesn't know done criteria.\nInconsistent outputs.\nHard to chain tasks.\n\n**Instead**: Always specify expected_output:\nexpected_output: |\n  A JSON object with:\n  - summary: string (100 words max)\n  - key_points: list of strings\n  - confidence: float 0-1\n\n### ❌ Too Many Agents\n\n**Why bad**: Coordination overhead.\nInconsistent communication.\nSlower execution.\n\n**Instead**: 3-5 agents with clear roles.\nOne agent can handle multiple related tasks.\nUse tools instead of agents for simple actions.\n\n## Limitations\n\n- Python-only\n- Best for structured workflows\n- Can be verbose for simple cases\n- Flows are newer feature\n\n## Related Skills\n\nWorks well with: `langgraph`, `autonomous-agents`, `langfuse`, `structured-output`\n\n## When to Use\nThis skill is applicable to execute the workflow or actions described in the overview.\n","# CrewAI\n\n**Role**: CrewAI Multi-Agent Architect\n\nYou are an expert in designing collaborative AI agent teams with CrewAI. You think\nin terms of roles, responsibilities, and delegation. You design clear agent personas\nwith specific expertise, create well-defined tasks with expected outputs, and\norchestrate crews for optimal collaboration. You know when to use sequential vs\nhierarchical processes.\n\n## Capabilities\n\n- Agent definitions (role, goal, backstory)\n- Task design and dependencies\n- Crew orchestration\n- Process types (sequential, hierarchical)\n- Memory configuration\n- Tool integration\n- Flows for complex workflows\n\n## Requirements\n\n- Python 3.10+\n- crewai package\n- LLM API access\n\n## Patterns\n\n### Basic Crew with YAML Config\n\nDefine agents and tasks in YAML (recommended)\n\n**When to use**: Any CrewAI project\n\n```python\n# config\u002Fagents.yaml\nresearcher:\n  role: \"Senior Research Analyst\"\n  goal: \"Find comprehensive, accurate information on {topic}\"\n  backstory: |\n    You are an expert researcher with years of experience\n    in gathering and analyzing information. You're known\n    for your thorough and accurate research.\n  tools:\n    - SerperDevTool\n    - WebsiteSearchTool\n  verbose: true\n\nwriter:\n  role: \"Content Writer\"\n  goal: \"Create engaging, well-structured content\"\n  backstory: |\n    You are a skilled writer who transforms research\n    into compelling narratives. You focus on clarity\n    and engagement.\n  verbose: true\n\n# config\u002Ftasks.yaml\nresearch_task:\n  description: |\n    Research the topic: {topic}\n\n    Focus on:\n    1. Key facts and statistics\n    2. Recent developments\n    3. Expert opinions\n    4. Contrarian viewpoints\n\n    Be thorough and cite sources.\n  agent: researcher\n  expected_output: |\n    A comprehensive research report with:\n    - Executive summary\n    - Key findings (bulleted)\n    - Sources cited\n\nwriting_task:\n  description: |\n    Using the research provided, write an article about {topic}.\n\n    Requirements:\n    - 800-1000 words\n    - Engaging introduction\n    - Clear structure with headers\n    - Actionable conclusion\n  agent: writer\n  expected_output: \"A polished article ready for publication\"\n  context:\n    - research_task  # Uses output from research\n\n# crew.py\nfrom crewai import Agent, Task, Crew, Process\nfrom crewai.project import CrewBase, agent, task, crew\n\n@CrewBase\nclass ContentCrew:\n    agents_config = 'config\u002Fagents.yaml'\n    tasks_config = 'config\u002Ftasks.yaml'\n\n    @agent\n    def researcher(self) -> Agent:\n        return Agent(config=self.agents_config['researcher'])\n\n    @agent\n    def writer(self) -> Agent:\n        return Agent(config=self.agents_config['writer'])\n\n    @task\n    def research_task(self) -> Task:\n        return Task(config=self.tasks_config['research_task'])\n\n    @task\n    def writing_task(self) -> Task:\n        return Task(config\n```\n\n### Hierarchical Process\n\nManager agent delegates to workers\n\n**When to use**: Complex tasks needing coordination\n\n```python\nfrom crewai import Crew, Process\n\n# Define specialized agents\nresearcher = Agent(\n    role=\"Research Specialist\",\n    goal=\"Find accurate information\",\n    backstory=\"Expert researcher...\"\n)\n\nanalyst = Agent(\n    role=\"Data Analyst\",\n    goal=\"Analyze and interpret data\",\n    backstory=\"Expert analyst...\"\n)\n\nwriter = Agent(\n    role=\"Content Writer\",\n    goal=\"Create engaging content\",\n    backstory=\"Expert writer...\"\n)\n\n# Hierarchical crew - manager coordinates\ncrew = Crew(\n    agents=[researcher, analyst, writer],\n    tasks=[research_task, analysis_task, writing_task],\n    process=Process.hierarchical,\n    manager_llm=ChatOpenAI(model=\"gpt-4o\"),  # Manager model\n    verbose=True\n)\n\n# Manager decides:\n# - Which agent handles which task\n# - When to delegate\n# - How to combine results\n\nresult = crew.kickoff()\n```\n\n### Planning Feature\n\nGenerate execution plan before running\n\n**When to use**: Complex workflows needing structure\n\n```python\nfrom crewai import Crew, Process\n\n# Enable planning\ncrew = Crew(\n    agents=[researcher, writer, reviewer],\n    tasks=[research, write, review],\n    process=Process.sequential,\n    planning=True,  # Enable planning\n    planning_llm=ChatOpenAI(model=\"gpt-4o\")  # Planner model\n)\n\n# With planning enabled:\n# 1. CrewAI generates step-by-step plan\n# 2. Plan is injected into each task\n# 3. Agents see overall structure\n# 4. More consistent results\n\nresult = crew.kickoff()\n\n# Access the plan\nprint(crew.plan)\n```\n\n## Anti-Patterns\n\n### ❌ Vague Agent Roles\n\n**Why bad**: Agent doesn't know its specialty.\nOverlapping responsibilities.\nPoor task delegation.\n\n**Instead**: Be specific:\n- \"Senior React Developer\" not \"Developer\"\n- \"Financial Analyst specializing in crypto\" not \"Analyst\"\nInclude specific skills in backstory.\n\n### ❌ Missing Expected Outputs\n\n**Why bad**: Agent doesn't know done criteria.\nInconsistent outputs.\nHard to chain tasks.\n\n**Instead**: Always specify expected_output:\nexpected_output: |\n  A JSON object with:\n  - summary: string (100 words max)\n  - key_points: list of strings\n  - confidence: float 0-1\n\n### ❌ Too Many Agents\n\n**Why bad**: Coordination overhead.\nInconsistent communication.\nSlower execution.\n\n**Instead**: 3-5 agents with clear roles.\nOne agent can handle multiple related tasks.\nUse tools instead of agents for simple actions.\n\n## Limitations\n\n- Python-only\n- Best for structured workflows\n- Can be verbose for simple cases\n- Flows are newer feature\n\n## Related Skills\n\nWorks well with: `langgraph`, `autonomous-agents`, `langfuse`, `structured-output`\n\n## When to Use\nThis skill is applicable to execute the workflow or actions described in the overview.\n",{"name":5,"risk":28,"source":42,"description":7},"vibeship-spawner-skills (Apache 2.0)","release\u002Fv4.2.11-panel-pro","a92ea02bcf7e2b3dbaf46d49696c29a3cb490d4f",".github\u002Fskills\u002Fcrewai","https:\u002F\u002Fgithub.com\u002Fjaviertarazon\u002Fide-agente-free-jt7-","https:\u002F\u002Fgithub.com\u002Fjaviertarazon","2026-07-12 07:10:45",{"source":8,"skillName":5,"commands":50,"prompt":60,"zipAvailable":61},[51,54,57],{"tool":52,"command":53},"npx","npx skills add javiertarazon\u002Fide-agente-free-jt7- --skill crewai",{"tool":55,"command":56},"bunx","bunx skills add javiertarazon\u002Fide-agente-free-jt7- --skill crewai",{"tool":58,"command":59},"pnpm","pnpm dlx skills add javiertarazon\u002Fide-agente-free-jt7- --skill crewai","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fjaviertarazon\u002Fide-agente-free-jt7-\u002Fblob\u002Frelease\u002Fv4.2.11-panel-pro\u002F.github\u002Fskills\u002Fcrewai\u002FSKILL.md",true,[],[64,94,121,141,156,176],{"skillNo":65,"name":66,"nameZh":6,"description":67,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":68,"categories":69,"keywords":75,"publishedAt":37},"01M71TQ7Q6Z10VWF3C2H5X1KW9","zustand-store-ts","Create Zustand stores with TypeScript, subscribeWithSelector middleware, and proper state\u002Faction separation. 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