[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M79EN2PAFY91SWGPHTETQJ2V":3,"skill-same:01M79EN2PAFY91SWGPHTETQJ2V":46},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":25,"commitSha":26,"skillDirectory":27,"forksCount":14,"repositoryHtmlUrl":28,"repositoryHomepageUrl":6,"ownerHtmlUrl":29,"ownerBlogUrl":6,"ownerDescription":30,"updatedAt":31,"install":32,"resources":45},"01M79EN2PAFY91SWGPHTETQJ2V","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 (sequential, hierarchical, parallel), memory systems, and flows for complex workflows. Essential for building collaborative AI agent teams. Use when: crewai, multi-agent team, agent roles, crew of agents, role-based agents.","sharepointoscar\u002Fagent-workflow-skills","sharepointoscar","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F1544014?v=4","01M79DYNNSWKW6C81A3C9QF0FQ","Oscar Medina","01M79DYNPDXTKQ5RFX4071RBM8",0,"MIT","https:\u002F\u002Fgithub.com\u002Fsharepointoscar\u002Fagent-workflow-skills\u002Fblob\u002Fmain\u002Fplugins\u002Fskills-data-ai\u002Fskills\u002Fcrewai\u002FSKILL.md",[],[],"2026-11-15 14:19:17",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 (sequential, hierarchical, parallel), memory systems, and flows for complex workflows. Essential for building collaborative AI agent teams. Use when: crewai, multi-agent team, agent roles, crew of agents, role-based agents.\"\nsource: vibeship-spawner-skills (Apache 2.0)\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","# 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",{"name":5,"source":24,"description":7},"vibeship-spawner-skills (Apache 2.0)","main","f27dd0e6c87efa56cfdea304aa3e89e63c02711b","plugins\u002Fskills-data-ai\u002Fskills\u002Fcrewai","https:\u002F\u002Fgithub.com\u002Fsharepointoscar\u002Fagent-workflow-skills","https:\u002F\u002Fgithub.com\u002Fsharepointoscar","Author | DevOps Practitioner | Docker | Terraform | Kubernetes | Jenkins X  | AWS | GCP | Azure and SP Veteran (since code-name Tahoe)","2026-11-11 12:44:52",{"source":8,"skillName":5,"commands":33,"prompt":43,"zipAvailable":44},[34,37,40],{"tool":35,"command":36},"npx","npx skills add sharepointoscar\u002Fagent-workflow-skills --skill crewai",{"tool":38,"command":39},"bunx","bunx skills add sharepointoscar\u002Fagent-workflow-skills --skill crewai",{"tool":41,"command":42},"pnpm","pnpm dlx skills add sharepointoscar\u002Fagent-workflow-skills --skill crewai","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fsharepointoscar\u002Fagent-workflow-skills\u002Fblob\u002Fmain\u002Fplugins\u002Fskills-data-ai\u002Fskills\u002Fcrewai\u002FSKILL.md",true,[],[47,54,61,68,75,82],{"skillNo":48,"name":49,"nameZh":6,"description":50,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":51,"categories":52,"keywords":53,"publishedAt":19},"01M79FQNYDNR7TYQAM5061GVKR","web-performance-optimization","Optimize website and web application performance including loading speed, Core Web Vitals, bundle size, caching strategies, and runtime performance","https:\u002F\u002Fgithub.com\u002Fsharepointoscar\u002Fagent-workflow-skills\u002Fblob\u002Fmain\u002Fplugins\u002Fskills-web-frontend\u002Fskills\u002Fweb-performance-optimization\u002FSKILL.md",[],[],{"skillNo":55,"name":56,"nameZh":6,"description":57,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":58,"categories":59,"keywords":60,"publishedAt":19},"01M79FQK396EEGX40QA7B9S404","web-design-guidelines","Review UI code for Web Interface Guidelines compliance. Use when asked to \"review my UI\", \"check accessibility\", \"audit design\", \"review UX\", or \"check my site against best practices\".","https:\u002F\u002Fgithub.com\u002Fsharepointoscar\u002Fagent-workflow-skills\u002Fblob\u002Fmain\u002Fplugins\u002Fskills-web-frontend\u002Fskills\u002Fweb-design-guidelines\u002FSKILL.md",[],[],{"skillNo":62,"name":63,"nameZh":6,"description":64,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":65,"categories":66,"keywords":67,"publishedAt":19},"01M79FQFK4GJCG8HVRG8JQF4QH","wcag-audit-patterns","Conduct WCAG 2.2 accessibility audits with automated testing, manual verification, and remediation guidance. Use when auditing websites for accessibility, fixing WCAG violations, or implementing accessible design patterns.","https:\u002F\u002Fgithub.com\u002Fsharepointoscar\u002Fagent-workflow-skills\u002Fblob\u002Fmain\u002Fplugins\u002Fskills-web-frontend\u002Fskills\u002Fwcag-audit-patterns\u002FSKILL.md",[],[],{"skillNo":69,"name":70,"nameZh":6,"description":71,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":72,"categories":73,"keywords":74,"publishedAt":19},"01M79FQ7WRQXC4D20Q2HTBMDG7","ui-ux-pro-max","UI\u002FUX design intelligence. 50 styles, 21 palettes, 50 font pairings, 20 charts, 9 stacks (React, Next.js, Vue, Svelte, SwiftUI, React Native, Flutter, Tailwind, shadcn\u002Fui). Actions: plan, build, create, design, implement, review, fix, improve, optimize, enhance, refactor, check UI\u002FUX code. Projects: website, landing page, dashboard, admin panel, e-commerce, SaaS, portfolio, blog, mobile app, .html, .tsx, .vue, .svelte. Elements: button, modal, navbar, sidebar, card, table, form, chart. Styles: glassmorphism, claymorphism, minimalism, brutalism, neumorphism, bento grid, dark mode, responsive, skeuomorphism, flat design. Topics: color palette, accessibility, animation, layout, typography, font pairing, spacing, hover, shadow, gradient. Integrations: shadcn\u002Fui MCP for component search and examples.","https:\u002F\u002Fgithub.com\u002Fsharepointoscar\u002Fagent-workflow-skills\u002Fblob\u002Fmain\u002Fplugins\u002Fskills-web-frontend\u002Fskills\u002Fui-ux-pro-max\u002FSKILL.md",[],[],{"skillNo":76,"name":77,"nameZh":6,"description":78,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":79,"categories":80,"keywords":81,"publishedAt":19},"01M79FPZC11BPBWDCVK2TER0RC","stitch-ui-design","Expert guide for creating effective prompts for Google Stitch AI UI design tool. Use when user wants to design UI\u002FUX in Stitch, create app interfaces, generate mobile\u002Fweb designs, or needs help crafting Stitch prompts. Covers prompt structure, specificity techniques, iteration strategies, and design-to-code workflows for Stitch by Google.","https:\u002F\u002Fgithub.com\u002Fsharepointoscar\u002Fagent-workflow-skills\u002Fblob\u002Fmain\u002Fplugins\u002Fskills-web-frontend\u002Fskills\u002Fstitch-ui-design\u002FSKILL.md",[],[],{"skillNo":83,"name":84,"nameZh":6,"description":85,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":86,"categories":87,"keywords":88,"publishedAt":19},"01M79FPTMMM3C57JN25RTRX2HR","scroll-experience","Expert in building immersive scroll-driven experiences - parallax storytelling, scroll animations, interactive narratives, and cinematic web experiences. Like NY Times interactives, Apple product pages, and award-winning web experiences. Makes websites feel like experiences, not just pages. Use when: scroll animation, parallax, scroll storytelling, interactive story, cinematic website.","https:\u002F\u002Fgithub.com\u002Fsharepointoscar\u002Fagent-workflow-skills\u002Fblob\u002Fmain\u002Fplugins\u002Fskills-web-frontend\u002Fskills\u002Fscroll-experience\u002FSKILL.md",[],[]]