{"id":"3b83cc92-fc11-4c87-977d-d8154fb40497","shortId":"rXNUG9","kind":"skill","title":"crewai","tagline":"Expert in CrewAI - the leading role-based multi-agent framework","description":"# CrewAI\n\nExpert in CrewAI - the leading role-based multi-agent framework used by 60% of Fortune 500\ncompanies. Covers agent design with roles and goals, task definition, crew orchestration,\nprocess types (sequential, hierarchical, parallel), memory systems, and flows for complex\nworkflows. Essential for building collaborative AI agent teams.\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### Expertise\n\n- Agent persona design\n- Task decomposition\n- Crew orchestration\n- Process selection\n- Memory configuration\n- Flow design\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## Prerequisites\n\n- 0: Python proficiency\n- 1: Multi-agent concepts\n- 2: Understanding of delegation\n- Required skills: Python 3.10+, crewai package, LLM API access\n\n## Scope\n\n- 0: Python-only\n- 1: Best for structured workflows\n- 2: Can be verbose for simple cases\n- 3: Flows are newer feature\n\n## Ecosystem\n\n### Primary\n\n- CrewAI framework\n- CrewAI Tools\n\n### Common_integrations\n\n- OpenAI / Anthropic / Ollama\n- SerperDev (search)\n- FileReadTool, DirectoryReadTool\n- Custom tools\n\n### Platforms\n\n- Python applications\n- FastAPI backends\n- Enterprise deployments\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# config/agents.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/tasks.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/agents.yaml'\n    tasks_config = 'config/tasks.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=self.tasks_config['writing_task'])\n\n    @crew\n    def crew(self) -> Crew:\n        return Crew(\n            agents=self.agents,\n            tasks=self.tasks,\n            process=Process.sequential,\n            verbose=True\n        )\n\n# main.py\ncrew = ContentCrew()\nresult = crew.crew().kickoff(inputs={\"topic\": \"AI Agents in 2025\"})\n\n### Hierarchical Process\n\nManager agent delegates to workers\n\n**When to use**: Complex tasks needing coordination\n\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### Planning Feature\n\nGenerate execution plan before running\n\n**When to use**: Complex workflows needing structure\n\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### Memory Configuration\n\nEnable agent memory for context\n\n**When to use**: Multi-turn or complex workflows\n\nfrom crewai import Crew\n\n# Memory types:\n# - Short-term: Within task execution\n# - Long-term: Across executions\n# - Entity: About specific entities\n\ncrew = Crew(\n    agents=[...],\n    tasks=[...],\n    memory=True,  # Enable all memory types\n    verbose=True\n)\n\n# Custom memory config\nfrom crewai.memory import LongTermMemory, ShortTermMemory\n\ncrew = Crew(\n    agents=[...],\n    tasks=[...],\n    memory=True,\n    long_term_memory=LongTermMemory(\n        storage=CustomStorage()  # Custom backend\n    ),\n    short_term_memory=ShortTermMemory(\n        storage=CustomStorage()\n    ),\n    embedder={\n        \"provider\": \"openai\",\n        \"config\": {\"model\": \"text-embedding-3-small\"}\n    }\n)\n\n# Memory helps agents:\n# - Remember previous interactions\n# - Build on past work\n# - Maintain consistency\n\n### Flows for Complex Workflows\n\nEvent-driven orchestration with state\n\n**When to use**: Complex, multi-stage workflows\n\nfrom crewai.flow.flow import Flow, listen, start, and_, or_, router\n\nclass ContentFlow(Flow):\n    # State persists across steps\n    model_config = {\"extra\": \"allow\"}\n\n    @start()\n    def gather_requirements(self):\n        \"\"\"First step - gather inputs.\"\"\"\n        self.topic = self.inputs.get(\"topic\", \"AI\")\n        self.style = self.inputs.get(\"style\", \"professional\")\n        return {\"topic\": self.topic}\n\n    @listen(gather_requirements)\n    def research(self, requirements):\n        \"\"\"Research after requirements gathered.\"\"\"\n        research_crew = ResearchCrew()\n        result = research_crew.crew().kickoff(\n            inputs={\"topic\": requirements[\"topic\"]}\n        )\n        self.research = result.raw\n        return result\n\n    @listen(research)\n    def write_content(self, research_result):\n        \"\"\"Write after research complete.\"\"\"\n        writing_crew = WritingCrew()\n        result = writing_crew.crew().kickoff(\n            inputs={\n                \"research\": self.research,\n                \"style\": self.style\n            }\n        )\n        return result\n\n    @router(write_content)\n    def quality_check(self, content):\n        \"\"\"Route based on quality.\"\"\"\n        if self.needs_revision(content):\n            return \"revise\"\n        return \"publish\"\n\n    @listen(\"revise\")\n    def revise_content(self):\n        \"\"\"Revision flow.\"\"\"\n        # Re-run writing with feedback\n        pass\n\n    @listen(\"publish\")\n    def publish_content(self):\n        \"\"\"Final publishing.\"\"\"\n        return {\"status\": \"published\", \"content\": self.content}\n\n# Run flow\nflow = ContentFlow()\nresult = flow.kickoff(inputs={\"topic\": \"AI Agents\"})\n\n### Custom Tools\n\nCreate tools for agents\n\n**When to use**: Agents need external capabilities\n\nfrom crewai.tools import BaseTool\nfrom pydantic import BaseModel, Field\n\n# Method 1: Class-based tool\nclass SearchInput(BaseModel):\n    query: str = Field(..., description=\"Search query\")\n\nclass WebSearchTool(BaseTool):\n    name: str = \"web_search\"\n    description: str = \"Search the web for information\"\n    args_schema: type[BaseModel] = SearchInput\n\n    def _run(self, query: str) -> str:\n        # Implementation\n        results = search_api.search(query)\n        return format_results(results)\n\n# Method 2: Function decorator\nfrom crewai import tool\n\n@tool(\"Database Query\")\ndef query_database(sql: str) -> str:\n    \"\"\"Execute SQL query and return results.\"\"\"\n    return db.execute(sql)\n\n# Assign tools to agents\nresearcher = Agent(\n    role=\"Researcher\",\n    goal=\"Find information\",\n    backstory=\"...\",\n    tools=[WebSearchTool(), query_database]\n)\n\n## Collaboration\n\n### Delegation Triggers\n\n- langgraph|state machine|graph -> langgraph (Need explicit state management)\n- observability|tracing -> langfuse (Need LLM observability)\n- structured output|json schema -> structured-output (Need structured responses)\n\n### Research and Writing Crew\n\nSkills: crewai, structured-output\n\nWorkflow:\n\n```\n1. Define researcher and writer agents\n2. Create research → analysis → writing pipeline\n3. Use structured output for research format\n4. Chain tasks with context\n```\n\n### Observable Agent Team\n\nSkills: crewai, langfuse\n\nWorkflow:\n\n```\n1. Build crew with agents and tasks\n2. Add Langfuse callback handler\n3. Monitor agent interactions\n4. Evaluate output quality\n```\n\n### Complex Workflow with Flows\n\nSkills: crewai, langgraph\n\nWorkflow:\n\n```\n1. Design workflow with CrewAI Flows\n2. Use LangGraph patterns for state\n3. Combine crews in flow steps\n4. Handle branching and routing\n```\n\n## Related Skills\n\nWorks well with: `langgraph`, `autonomous-agents`, `langfuse`, `structured-output`\n\n## When to Use\n- User mentions or implies: crewai\n- User mentions or implies: multi-agent team\n- User mentions or implies: agent roles\n- User mentions or implies: crew of agents\n- User mentions or implies: role-based agents\n- User mentions or implies: collaborative agents\n\n## Limitations\n- Use this skill only when the task clearly matches the scope described above.\n- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.\n- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.","tags":["crewai","antigravity","awesome","skills","sickn33","agent-skills","agentic-skills","ai-agent-skills","ai-agents","ai-coding","ai-workflows","antigravity-skills"],"capabilities":["skill","source-sickn33","skill-crewai","topic-agent-skills","topic-agentic-skills","topic-ai-agent-skills","topic-ai-agents","topic-ai-coding","topic-ai-workflows","topic-antigravity","topic-antigravity-skills","topic-claude-code","topic-claude-code-skills","topic-codex-cli","topic-codex-skills"],"categories":["antigravity-awesome-skills"],"synonyms":[],"warnings":[],"endpointUrl":"https://skills.sh/sickn33/antigravity-awesome-skills/crewai","protocol":"skill","transport":"skills-sh","auth":{"type":"none","details":{"cli":"npx skills add sickn33/antigravity-awesome-skills","source_repo":"https://github.com/sickn33/antigravity-awesome-skills","install_from":"skills.sh"}},"qualityScore":"0.700","qualityRationale":"deterministic score 0.70 from registry signals: · indexed on github topic:agent-skills · 34831 github stars · SKILL.md body (10,939 chars)","verified":false,"liveness":"unknown","lastLivenessCheck":null,"agentReviews":{"count":0,"score_avg":null,"cost_usd_avg":null,"success_rate":null,"latency_p50_ms":null,"narrative_summary":null,"summary_updated_at":null},"enrichmentModel":"deterministic:skill-github:v1","enrichmentVersion":1,"enrichedAt":"2026-04-24T06:50:59.433Z","embedding":null,"createdAt":"2026-04-18T21:35:24.629Z","updatedAt":"2026-04-24T06:50:59.433Z","lastSeenAt":"2026-04-24T06:50:59.433Z","tsv":"'-1000':379 '0':161,183 '1':164,187,330,659,978,1105,1136,1164 '2':169,192,335,667,1026,1111,1143,1170 '2025':504 '3':199,338,674,775,1117,1148,1176 '3.10':176 '4':341,679,1124,1152,1182 '4o':589,653 '500':32 '60':29 '800':378 'access':181,685 'accur':256,282,534 'across':721,821 'action':387 'add':1144 'agent':12,25,35,62,68,78,94,123,137,167,235,349,389,410,418,424,430,434,436,441,445,447,485,502,508,526,528,540,553,570,597,633,675,693,729,749,779,954,960,964,1054,1056,1110,1130,1140,1150,1195,1214,1220,1228,1236,1242 'ai':61,77,501,839,953 'allow':826 'analysi':577,1114 'analyst':252,539,543,551,572 'analyz':273,545 'anthrop':213 'api':180 'applic':223 'architect':69 'arg':1006 'articl':374,395 'ask':1276 'assign':1051 'autonom':1194 'autonomous-ag':1193 'backend':225,760 'backstori':141,260,300,536,549,561,1062 'base':9,22,906,981,1235 'basemodel':975,985,1009 'basetool':971,994 'basic':229 'best':188 'boundari':1284 'branch':1184 'build':59,783,1137 'bullet':362 'callback':1146 'capabl':136,967 'case':198 'chain':1125 'chatopenai':585,649 'check':902 'cite':347,364 'clarif':1278 'clariti':315 'class':422,816,980,983,992 'class-bas':979 'clear':93,383,1251 'collabor':60,76,112,1067,1241 'combin':606,1177 'common':210 'compani':33 'compel':310 'complet':883 'complex':55,158,515,620,704,791,802,1156 'comprehens':255,354 'concept':168 'conclus':388 'config':233,425,428,437,439,448,450,460,462,473,475,741,770,824 'config/agents.yaml':247,426 'config/tasks.yaml':320,429 'configur':133,153,691 'consist':681,788 'content':291,299,555,560,876,899,904,912,921,936,943 'contentcrew':423,495 'contentflow':817,948 'context':399,696,1128 'contrarian':342 'coordin':518,567 'cover':34 'creat':99,294,558,957,1112 'crew':43,109,128,146,230,412,420,478,480,482,484,494,522,565,568,569,627,631,632,709,727,728,747,748,859,885,1098,1138,1178,1226 'crew.crew':497 'crew.kickoff':609,684 'crew.plan':689 'crew.py':406 'crewai':1,4,14,17,65,81,177,206,208,245,408,520,625,660,707,1030,1100,1133,1161,1168,1207 'crewai.flow.flow':808 'crewai.memory':743 'crewai.project':415 'crewai.tools':969 'crewbas':417,421 'criteria':1287 'custom':219,739,759,955 'customstorag':758,766 'data':542,548 'databas':1034,1038,1066 'db.execute':1049 'decid':595 'decomposit':127 'decor':1028 'def':431,442,453,466,479,828,850,874,900,919,934,1011,1036 'defin':102,234,524,1106 'definit':42,138 'deleg':90,172,509,603,1068 'depend':145 'deploy':227 'describ':1255 'descript':323,367,989,999 'design':36,75,92,125,135,143,1165 'develop':337 'directoryreadtool':218 'driven':795 'ecosystem':204 'embed':774 'embedd':767 'enabl':629,645,658,692,733 'engag':295,317,381,559 'enterpris':226 'entiti':723,726 'environ':1267 'environment-specif':1266 'essenti':57 'evalu':1153 'event':794 'event-driven':793 'execut':358,613,717,722,1042 'expect':105,351,391 'experi':269 'expert':2,15,73,264,339,537,550,562,1272 'expertis':98,122 'explicit':1076 'extern':966 'extra':825 'fact':332 'fastapi':224 'featur':203,611 'feedback':930 'field':976,988 'filereadtool':217 'final':938 'find':254,361,533,1060 'first':832 'flow':53,134,156,200,789,810,818,924,946,947,1159,1169,1180 'flow.kickoff':950 'focus':313,328 'format':1022,1123 'fortun':31 'framework':13,26,207 'function':1027 'gather':271,829,834,848,857 'generat':612,661 'goal':40,140,253,293,532,544,557,1059 'gpt':588,652 'gpt-4o':587,651 'graph':1073 'handl':598,1183 'handler':1147 'header':386 'help':778 'hierarch':48,120,151,505,564 'implement':1017 'impli':1206,1211,1219,1225,1232,1240 'import':409,416,521,626,708,744,809,970,974,1031 'inform':257,274,535,1005,1061 'inject':670 'input':499,835,864,890,951,1281 'integr':155,211 'interact':782,1151 'interpret':547 'introduct':382 'json':1087 'key':331,360 'kickoff':498,863,889 'know':114 'known':277 'langfus':1081,1134,1145,1196 'langgraph':1070,1074,1162,1172,1192 'lead':6,19 'limit':1243 'listen':811,847,872,917,932 'llm':179,584,648,1083 'long':719,753 'long-term':718 'longtermmemori':745,756 'machin':1072 'main.py':493 'maintain':787 'manag':507,566,583,590,594,1078 'match':1252 'memori':50,132,152,690,694,710,731,735,740,751,755,763,777 'mention':1204,1209,1217,1223,1230,1238 'method':977,1025 'miss':1289 'model':586,591,650,655,771,823 'monitor':1149 'multi':11,24,67,166,701,804,1213 'multi-ag':10,23,66,165,1212 'multi-stag':803 'multi-turn':700 'name':995 'narrat':311 'need':517,622,965,1075,1082,1092 'newer':202 'observ':1079,1084,1129 'ollama':214 'openai':212,769 'opinion':340 'optim':111 'orchestr':44,108,129,147,796 'output':106,352,392,403,1086,1091,1103,1120,1154,1199,1261 'overal':677 'packag':178 'parallel':49 'pass':931 'past':785 'pattern':228,1173 'permiss':1282 'persist':820 'persona':95,124 'pipelin':1116 'plan':610,614,630,643,646,647,657,666,668,687 'planner':654 'platform':221 'polish':394 'prerequisit':160 'previous':781 'primari':205 'print':688 'process':45,121,130,148,413,489,506,523,581,628,641 'process.hierarchical':582 'process.sequential':490,642 'profession':843 'profici':163 'project':246 'provid':371,768 'public':398 'publish':916,933,935,939,942 'pydant':973 'python':162,175,185,222 'python-on':184 'qualiti':901,908,1155 'queri':986,991,1014,1020,1035,1037,1044,1065 're':276,926 're-run':925 'readi':396 'recent':336 'recommend':240 'relat':1187 'rememb':780 'report':356 'requir':173,377,830,849,853,856,866,1280 'research':248,251,265,283,308,321,324,350,355,370,400,405,432,440,454,463,527,530,538,571,575,634,638,851,854,858,873,878,882,891,1055,1058,1095,1107,1113,1122 'research_crew.crew':862 'researchcrew':860 'respons':88,1094 'result':496,607,608,682,683,861,871,879,887,896,949,1018,1023,1024,1047 'result.raw':869 'return':435,446,458,471,483,844,870,895,913,915,940,1021,1046,1048 'review':636,640,1273 'revis':911,914,918,920,923 'role':8,21,38,64,87,139,249,290,529,541,554,1057,1221,1234 'role-bas':7,20,1233 'rout':905,1186 'router':815,897 'run':616,927,945,1012 'safeti':1283 'schema':1007,1088 'scope':182,1254 'search':216,990,998,1001 'search_api.search':1019 'searchinput':984,1010 'see':676 'select':131 'self':433,444,456,469,481,831,852,877,903,922,937,1013 'self.agents':438,449,486 'self.content':944 'self.inputs.get':837,841 'self.needs':910 'self.research':868,892 'self.style':840,894 'self.tasks':461,474,488 'self.topic':836,846 'senior':250 'sequenti':47,118,150 'serperdev':215 'serperdevtool':285 'short':713,761 'short-term':712 'shorttermmemori':746,764 'simpl':197 'skill':174,304,1099,1132,1160,1188,1246 'skill-crewai' 'small':776 'sourc':348,363 'source-sickn33' 'special':525 'specialist':531 'specif':97,725,1268 'sql':1039,1043,1050 'stage':805 'start':812,827 'state':798,819,1071,1077,1175 'statist':334 'status':941 'step':663,665,822,833,1181 'step-by-step':662 'stop':1274 'storag':757,765 'str':987,996,1000,1015,1016,1040,1041 'structur':190,298,384,623,678,1085,1090,1093,1102,1119,1198 'structured-output':1089,1101,1197 'style':842,893 'substitut':1264 'success':1286 'summari':359 'system':51 'task':41,103,126,142,237,322,366,401,411,419,427,452,455,457,459,464,465,468,470,472,477,487,516,574,576,578,580,600,637,673,716,730,750,1126,1142,1250 'team':63,79,1131,1215 'term':85,714,720,754,762 'test':1270 'text':773 'text-embed':772 'think':83 'thorough':280,345 'tool':154,209,220,284,956,958,982,1032,1033,1052,1063 'topic':259,326,327,376,500,838,845,865,867,952 'topic-agent-skills' 'topic-agentic-skills' 'topic-ai-agent-skills' 'topic-ai-agents' 'topic-ai-coding' 'topic-ai-workflows' 'topic-antigravity' 'topic-antigravity-skills' 'topic-claude-code' 'topic-claude-code-skills' 'topic-codex-cli' 'topic-codex-skills' 'trace':1080 'transform':307 'treat':1259 'trigger':1069 'true':288,319,492,593,644,732,738,752 'turn':702 'type':46,149,711,736,1008 'understand':170 'use':27,117,243,368,402,514,619,699,801,963,1118,1171,1202,1244 'user':1203,1208,1216,1222,1229,1237 'valid':1269 'verbos':195,287,318,491,592,737 'viewpoint':343 'vs':119 'web':997,1003 'websearchtool':993,1064 'websitesearchtool':286 'well':101,297,1190 'well-defin':100 'well-structur':296 'within':715 'word':380 'work':786,1189 'worker':511 'workflow':56,159,191,621,705,792,806,1104,1135,1157,1163,1166 'write':365,372,467,476,579,639,875,880,884,898,928,1097,1115 'writer':289,292,305,390,443,451,552,556,563,573,635,1109 'writing_crew.crew':888 'writingcrew':886 'yaml':232,239 'year':267","prices":[{"id":"59768c7f-be77-4614-a540-2d179f9c71d7","listingId":"3b83cc92-fc11-4c87-977d-d8154fb40497","amountUsd":"0","unit":"free","nativeCurrency":null,"nativeAmount":null,"chain":null,"payTo":null,"paymentMethod":"skill-free","isPrimary":true,"details":{"org":"sickn33","category":"antigravity-awesome-skills","install_from":"skills.sh"},"createdAt":"2026-04-18T21:35:24.629Z"}],"sources":[{"listingId":"3b83cc92-fc11-4c87-977d-d8154fb40497","source":"github","sourceId":"sickn33/antigravity-awesome-skills/crewai","sourceUrl":"https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/crewai","isPrimary":false,"firstSeenAt":"2026-04-18T21:35:24.629Z","lastSeenAt":"2026-04-24T06:50:59.433Z"}],"details":{"listingId":"3b83cc92-fc11-4c87-977d-d8154fb40497","quickStartSnippet":null,"exampleRequest":null,"exampleResponse":null,"schema":null,"openapiUrl":null,"agentsTxtUrl":null,"citations":[],"useCases":[],"bestFor":[],"notFor":[],"kindDetails":{"org":"sickn33","slug":"crewai","github":{"repo":"sickn33/antigravity-awesome-skills","stars":34831,"topics":["agent-skills","agentic-skills","ai-agent-skills","ai-agents","ai-coding","ai-workflows","antigravity","antigravity-skills","claude-code","claude-code-skills","codex-cli","codex-skills","cursor","cursor-skills","developer-tools","gemini-cli","gemini-skills","kiro","mcp","skill-library"],"license":"mit","html_url":"https://github.com/sickn33/antigravity-awesome-skills","pushed_at":"2026-04-24T06:41:17Z","description":"Installable GitHub library of 1,400+ agentic skills for Claude Code, Cursor, Codex CLI, Gemini CLI, Antigravity, and more. Includes installer CLI, bundles, workflows, and official/community skill collections.","skill_md_sha":"b5d023318ac3a376c8422f858ac2f11f53336b43","skill_md_path":"skills/crewai/SKILL.md","default_branch":"main","skill_tree_url":"https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/crewai"},"layout":"multi","source":"github","category":"antigravity-awesome-skills","frontmatter":{"name":"crewai","description":"Expert in CrewAI - the leading role-based multi-agent framework"},"skills_sh_url":"https://skills.sh/sickn33/antigravity-awesome-skills/crewai"},"updatedAt":"2026-04-24T06:50:59.433Z"}}