[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6QXNJMABSR0JD2FWHQ145TP":3,"skill-same:01M6QXNJMABSR0JD2FWHQ145TP":65},{"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":38,"linkOnly":39,"markdownContent":40,"markdownBody":41,"frontMatter":42,"refName":44,"commitSha":45,"skillDirectory":46,"forksCount":47,"repositoryHtmlUrl":48,"repositoryHomepageUrl":6,"ownerHtmlUrl":49,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":50,"install":51,"resources":64},"01M6QXNJMABSR0JD2FWHQ145TP","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.","Mishit18\u002Fclaude-code-templates","Mishit18","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F244127857?v=4","01M6QXBYKWM76HSKYDNWSKKE41","Mishit Sharma","01M6QXBYRJ0CAVM51S1RTEKTZZ",1,"MIT","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fcrewai\u002FSKILL.md",[],[5,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37],"multi-agent","when","definition","spawner","flows","types","fortune","agent team","expert","agent teams","design","team","apache","framework","workflows","agent framework","orchestration","ai agent","memory","2026-11-10 10:12:30",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\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",{"name":5,"source":43,"description":7},"vibeship-spawner-skills (Apache 2.0)","main","015422534a7457348a313b6381f0255d3364eb8c","cli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fcrewai",0,"https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates","https:\u002F\u002Fgithub.com\u002FMishit18","2026-11-12 18:26:48",{"source":8,"skillName":5,"commands":52,"prompt":62,"zipAvailable":63},[53,56,59],{"tool":54,"command":55},"npx","npx skills add Mishit18\u002Fclaude-code-templates --skill crewai",{"tool":57,"command":58},"bunx","bunx skills add Mishit18\u002Fclaude-code-templates --skill crewai",{"tool":60,"command":61},"pnpm","pnpm dlx skills add Mishit18\u002Fclaude-code-templates --skill crewai","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fcrewai\u002FSKILL.md",true,[],[66,100,126,152,178,202],{"skillNo":67,"name":68,"nameZh":6,"description":69,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":70,"licenseFileUrl":71,"sourceHtmlUrl":72,"categories":73,"keywords":79,"publishedAt":38},"01M6R4ARN4W2VZRDERDTMN1KP0","web-artifacts-builder","Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn\u002Fui). Use for complex artifacts requiring state management, routing, or shadcn\u002Fui components - not for simple single-file HTML\u002FJSX artifacts.","Apache-2.0","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FSKILL.md",[74],{"categoryNo":75,"categoryName":76,"taxonomyType":77,"taxonomyTypeDesc":78},"01JBSEEDCAT000000000000009","前端开发","SCENARIO","使用场景",[80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99],"web artifacts builder","web","artifacts","artifacts builder","builder","frontend","css","components","react","routing","state management","state","file","Tailwind","use","single-file","ui components","multi","for","single",{"skillNo":101,"name":102,"nameZh":6,"description":103,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":70,"licenseFileUrl":104,"sourceHtmlUrl":105,"categories":106,"keywords":111,"publishedAt":38},"01M6R45GXREM5DP8T08MAC788H","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[107,110],{"categoryNo":108,"categoryName":109,"taxonomyType":77,"taxonomyTypeDesc":78},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":75,"categoryName":76,"taxonomyType":77,"taxonomyTypeDesc":78},[112,113,114,85,115,116,81,117,118,119,120,121,122,123,124,125,98],"testing","webapp testing","webapp","screenshots","playwright","debugging","toolkit","browser","ui","logs","applications","behavior","local","with",{"skillNo":127,"name":128,"nameZh":6,"description":129,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":130,"sourceHtmlUrl":131,"categories":132,"keywords":133,"publishedAt":38},"01M6R2GYJ470X8EB7R58V0B68K","think-tank","Run a Virtual Think Tank — a structured multi-persona debate — before planning or making architectural\u002Fdesign\u002Fstrategic decisions. Use this skill whenever the user is about to plan a system, make a technology choice, evaluate trade-offs, decide on an approach, or faces any decision where multiple perspectives would sharpen the outcome. Also trigger when the user says \"think tank\", \"debate this\", \"perspectives on\", \"trade-offs\", \"should I use X or Y\", \"help me decide\", \"before we plan\", or asks for pros\u002Fcons of competing approaches. This skill should run BEFORE any implementation planning begins — it produces a structured analysis that feeds into better plans.","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fthink-tank\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fthink-tank\u002FSKILL.md",[],[134,135,29,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,20],"think tank","think","run","feeds","analysis","persona","plan","sharpen","virtual","decision","choice","decide","perspectives","outcome","me","debate","make","trade-offs",{"skillNo":153,"name":154,"nameZh":6,"description":155,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":70,"licenseFileUrl":156,"sourceHtmlUrl":157,"categories":158,"keywords":162,"publishedAt":38},"01M6R2FSR55AVRFA9YZ8SC2GZG","skill-creator","Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fskill-creator\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fskill-creator\u002FSKILL.md",[159],{"categoryNo":160,"categoryName":161,"taxonomyType":77,"taxonomyTypeDesc":78},"01JBSEEDCAT000000000000023","Skill与智能体开发",[163,164,33,165,166,167,168,169,170,171,172,173,174,175,125,98,176,20,177],"skill creator","creator","create","update","capabilities","knowledge","tool","integrations","specialized","guide","new skill","existing skill","users","Existing","new",{"skillNo":179,"name":180,"nameZh":6,"description":181,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":182,"categories":183,"keywords":184,"publishedAt":38},"01M6R2FK42ZZ4HJTJVVK9WJBBP","ship-learn-next","Transform learning content (like YouTube transcripts, articles, tutorials) into actionable implementation plans using the Ship-Learn-Next framework. Use when user wants to turn advice, lessons, or educational content into concrete action steps, reps, or a learning quest.","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fship-learn-next\u002FSKILL.md",[],[185,186,187,188,32,189,190,191,94,192,193,194,195,196,197,198,199,200,201,20],"learn","ship learn next","ship","next","learning","read","write","advice","implementation","youtube","plans","quest","transform","actionable","action","lessons","youtube transcripts",{"skillNo":203,"name":204,"nameZh":6,"description":205,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":70,"licenseFileUrl":206,"sourceHtmlUrl":207,"categories":208,"keywords":215,"publishedAt":38},"01M6R2088G1RHDA5MDX16HASRB","slack-gif-creator","Knowledge and utilities for creating animated GIFs optimized for Slack. Provides constraints, validation tools, and animation concepts. Use when users request animated GIFs for Slack like \"make me a GIF of X doing Y for Slack.\"","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fenterprise-communication\u002Fslack-gif-creator\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fenterprise-communication\u002Fslack-gif-creator\u002FSKILL.md",[209,212],{"categoryNo":210,"categoryName":211,"taxonomyType":77,"taxonomyTypeDesc":78},"01JBSEEDCAT000000000000005","邮件与沟通",{"categoryNo":213,"categoryName":214,"taxonomyType":77,"taxonomyTypeDesc":78},"01JBSEEDCAT000000000000016","图像与多媒体",[216,217,218,164,219,220,221,222,168,94,148,175,150,223,224,98,225,20],"slack gif creator","slack","gif","validation","Tools","constraints","Animation","Utilities","concepts","request"]