[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6E2FVNFGT7SP495VMDBP64S":3,"skill-same:01M6E2FVNFGT7SP495VMDBP64S":128},{"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":25,"publishedAt":45,"linkOnly":46,"markdownContent":47,"markdownBody":48,"frontMatter":49,"refName":53,"commitSha":54,"skillDirectory":55,"forksCount":13,"repositoryHtmlUrl":56,"repositoryHomepageUrl":57,"ownerHtmlUrl":58,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":59,"install":60,"resources":73},"01M6E2FVNFGT7SP495VMDBP64S","OpenAI Automation",null,"Automate OpenAI API operations -- generate responses with multimodal and structured output support, create embeddings, generate images, and list models via the Composio MCP integration.","7dvq6yrkgs-stack\u002Fplaywright","7dvq6yrkgs-stack","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F251638143?v=4","01M6E061J2AMGWYZ6TK7Y8NM74","01M6E061PQCQJGQMPBERMCFN5G",0,"Apache-2.0","https:\u002F\u002Fgithub.com\u002F7dvq6yrkgs-stack\u002Fplaywright\u002Fblob\u002Fmain\u002Fskills\u002Fopenai-automation\u002FSKILL.md",[17,22],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000008","测试与质量","SCENARIO","使用场景",{"categoryNo":23,"categoryName":24,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发",[26,27,5,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44],"automation","openai","MCP Integration","via","structured output","composio","models","images","operations","list","api","MCP","integration","multimodal","embeddings","openai api","generate","create","support","2026-11-10 10:12:30",false,"---\nname: OpenAI Automation\ndescription: \"Automate OpenAI API operations -- generate responses with multimodal and structured output support, create embeddings, generate images, and list models via the Composio MCP integration.\"\nrequires:\n  mcp:\n    - rube\n---\n\n# OpenAI Automation\n\nAutomate your OpenAI API workflows -- generate text with the Responses API (including multimodal image+text inputs and structured JSON outputs), create embeddings for search and clustering, generate images with DALL-E and GPT Image models, and list available models.\n\n**Toolkit docs:** [composio.dev\u002Ftoolkits\u002Fopenai](https:\u002F\u002Fcomposio.dev\u002Ftoolkits\u002Fopenai)\n\n---\n\n## Setup\n\n1. Add the Composio MCP server to your client: `https:\u002F\u002Frube.app\u002Fmcp`\n2. Connect your OpenAI account when prompted (API key authentication)\n3. Start using the workflows below\n\n---\n\n## Core Workflows\n\n### 1. Generate a Response (Text, Multimodal, Structured)\n\nUse `OPENAI_CREATE_RESPONSE` for one-shot model responses including text, image analysis, OCR, and structured JSON outputs.\n\n```\nTool: OPENAI_CREATE_RESPONSE\nInputs:\n  - model: string (required) -- e.g., \"gpt-5\", \"gpt-4o\", \"o3-mini\"\n  - input: string | array (required)\n    Simple: \"Explain quantum computing\"\n    Multimodal: [\n      { role: \"user\", content: [\n        { type: \"input_text\", text: \"What is in this image?\" },\n        { type: \"input_image\", image_url: { url: \"https:\u002F\u002F...\" } }\n      ]}\n    ]\n  - temperature: number (0-2, optional -- not supported with reasoning models)\n  - max_output_tokens: integer (optional)\n  - reasoning: { effort: \"none\" | \"minimal\" | \"low\" | \"medium\" | \"high\" }\n  - text: object (structured output config)\n    - format: { type: \"json_schema\", name: \"...\", schema: {...}, strict: true }\n  - tools: array (function, code_interpreter, file_search, web_search)\n  - tool_choice: \"auto\" | \"none\" | \"required\" | { type: \"function\", function: { name: \"...\" } }\n  - store: boolean (false to opt out of model distillation)\n  - stream: boolean\n```\n\n**Structured output example:** Set `text.format` to `{ type: \"json_schema\", name: \"person\", schema: { type: \"object\", properties: { name: { type: \"string\" }, age: { type: \"integer\" } }, required: [\"name\", \"age\"], additionalProperties: false }, strict: true }`.\n\n### 2. Create Embeddings\n\nUse `OPENAI_CREATE_EMBEDDINGS` for vector search, clustering, recommendations, and RAG pipelines.\n\n```\nTool: OPENAI_CREATE_EMBEDDINGS\nInputs:\n  - input: string | string[] | int[] | int[][] (required) -- max 8192 tokens, max 2048 items\n  - model: string (required) -- \"text-embedding-3-small\", \"text-embedding-3-large\", \"text-embedding-ada-002\"\n  - dimensions: integer (optional, only for text-embedding-3 and later)\n  - encoding_format: \"float\" | \"base64\" (default \"float\")\n  - user: string (optional, end-user ID for abuse monitoring)\n```\n\n### 3. Generate Images\n\nUse `OPENAI_CREATE_IMAGE` to create images from text prompts using GPT Image or DALL-E models.\n\n```\nTool: OPENAI_CREATE_IMAGE\nInputs:\n  - model: string (required) -- \"gpt-image-1\", \"gpt-image-1.5\", \"dall-e-3\", \"dall-e-2\"\n  - prompt: string (required) -- max 32000 chars (GPT Image), 4000 (DALL-E 3), 1000 (DALL-E 2)\n  - size: \"1024x1024\" | \"1536x1024\" | \"1024x1536\" | \"auto\" | \"256x256\" | \"512x512\" | \"1792x1024\" | \"1024x1792\"\n  - quality: \"standard\" | \"hd\" | \"auto\" | \"high\" | \"medium\" | \"low\"\n  - n: integer (1-10; DALL-E 3 supports n=1 only)\n  - background: \"transparent\" | \"opaque\" | \"auto\" (GPT Image models only)\n  - style: \"vivid\" | \"natural\" (DALL-E 3 only)\n  - user: string (optional)\n```\n\n### 4. List Available Models\n\nUse `OPENAI_LIST_MODELS` to discover which models are accessible with your API key.\n\n```\nTool: OPENAI_LIST_MODELS\nInputs: (none)\n```\n\n---\n\n## Known Pitfalls\n\n| Pitfall | Detail |\n|---------|--------|\n| DALL-E deprecation | DALL-E 2 and DALL-E 3 are deprecated and will stop being supported on 05\u002F12\u002F2026. Prefer GPT Image models. |\n| DALL-E 3 single image only | `OPENAI_CREATE_IMAGE` with DALL-E 3 only supports `n=1`. Use GPT Image models or DALL-E 2 for multiple images. |\n| Token limits for embeddings | Input must not exceed 8192 tokens per item and 2048 items per batch for embedding models. |\n| Reasoning model restrictions | `temperature` and `top_p` are not supported with reasoning models (o3-mini, etc.). Use `reasoning.effort` instead. |\n| Structured output strict mode | When `strict: true` in json_schema format, ALL schema properties must be listed in the `required` array. |\n| Prompt length varies by model | Image prompt max lengths differ: 32000 (GPT Image), 4000 (DALL-E 3), 1000 (DALL-E 2). |\n\n---\n\n## Quick Reference\n\n| Tool Slug | Description |\n|-----------|-------------|\n| `OPENAI_CREATE_RESPONSE` | Generate text\u002Fmultimodal responses with structured output support |\n| `OPENAI_CREATE_EMBEDDINGS` | Create text embeddings for search, clustering, and RAG |\n| `OPENAI_CREATE_IMAGE` | Generate images from text prompts |\n| `OPENAI_LIST_MODELS` | List all models available to your API key |\n\n---\n\n*Powered by [Composio](https:\u002F\u002Fcomposio.dev)*\n","# OpenAI Automation\n\nAutomate your OpenAI API workflows -- generate text with the Responses API (including multimodal image+text inputs and structured JSON outputs), create embeddings for search and clustering, generate images with DALL-E and GPT Image models, and list available models.\n\n**Toolkit docs:** [composio.dev\u002Ftoolkits\u002Fopenai](https:\u002F\u002Fcomposio.dev\u002Ftoolkits\u002Fopenai)\n\n---\n\n## Setup\n\n1. Add the Composio MCP server to your client: `https:\u002F\u002Frube.app\u002Fmcp`\n2. Connect your OpenAI account when prompted (API key authentication)\n3. Start using the workflows below\n\n---\n\n## Core Workflows\n\n### 1. Generate a Response (Text, Multimodal, Structured)\n\nUse `OPENAI_CREATE_RESPONSE` for one-shot model responses including text, image analysis, OCR, and structured JSON outputs.\n\n```\nTool: OPENAI_CREATE_RESPONSE\nInputs:\n  - model: string (required) -- e.g., \"gpt-5\", \"gpt-4o\", \"o3-mini\"\n  - input: string | array (required)\n    Simple: \"Explain quantum computing\"\n    Multimodal: [\n      { role: \"user\", content: [\n        { type: \"input_text\", text: \"What is in this image?\" },\n        { type: \"input_image\", image_url: { url: \"https:\u002F\u002F...\" } }\n      ]}\n    ]\n  - temperature: number (0-2, optional -- not supported with reasoning models)\n  - max_output_tokens: integer (optional)\n  - reasoning: { effort: \"none\" | \"minimal\" | \"low\" | \"medium\" | \"high\" }\n  - text: object (structured output config)\n    - format: { type: \"json_schema\", name: \"...\", schema: {...}, strict: true }\n  - tools: array (function, code_interpreter, file_search, web_search)\n  - tool_choice: \"auto\" | \"none\" | \"required\" | { type: \"function\", function: { name: \"...\" } }\n  - store: boolean (false to opt out of model distillation)\n  - stream: boolean\n```\n\n**Structured output example:** Set `text.format` to `{ type: \"json_schema\", name: \"person\", schema: { type: \"object\", properties: { name: { type: \"string\" }, age: { type: \"integer\" } }, required: [\"name\", \"age\"], additionalProperties: false }, strict: true }`.\n\n### 2. Create Embeddings\n\nUse `OPENAI_CREATE_EMBEDDINGS` for vector search, clustering, recommendations, and RAG pipelines.\n\n```\nTool: OPENAI_CREATE_EMBEDDINGS\nInputs:\n  - input: string | string[] | int[] | int[][] (required) -- max 8192 tokens, max 2048 items\n  - model: string (required) -- \"text-embedding-3-small\", \"text-embedding-3-large\", \"text-embedding-ada-002\"\n  - dimensions: integer (optional, only for text-embedding-3 and later)\n  - encoding_format: \"float\" | \"base64\" (default \"float\")\n  - user: string (optional, end-user ID for abuse monitoring)\n```\n\n### 3. Generate Images\n\nUse `OPENAI_CREATE_IMAGE` to create images from text prompts using GPT Image or DALL-E models.\n\n```\nTool: OPENAI_CREATE_IMAGE\nInputs:\n  - model: string (required) -- \"gpt-image-1\", \"gpt-image-1.5\", \"dall-e-3\", \"dall-e-2\"\n  - prompt: string (required) -- max 32000 chars (GPT Image), 4000 (DALL-E 3), 1000 (DALL-E 2)\n  - size: \"1024x1024\" | \"1536x1024\" | \"1024x1536\" | \"auto\" | \"256x256\" | \"512x512\" | \"1792x1024\" | \"1024x1792\"\n  - quality: \"standard\" | \"hd\" | \"auto\" | \"high\" | \"medium\" | \"low\"\n  - n: integer (1-10; DALL-E 3 supports n=1 only)\n  - background: \"transparent\" | \"opaque\" | \"auto\" (GPT Image models only)\n  - style: \"vivid\" | \"natural\" (DALL-E 3 only)\n  - user: string (optional)\n```\n\n### 4. List Available Models\n\nUse `OPENAI_LIST_MODELS` to discover which models are accessible with your API key.\n\n```\nTool: OPENAI_LIST_MODELS\nInputs: (none)\n```\n\n---\n\n## Known Pitfalls\n\n| Pitfall | Detail |\n|---------|--------|\n| DALL-E deprecation | DALL-E 2 and DALL-E 3 are deprecated and will stop being supported on 05\u002F12\u002F2026. Prefer GPT Image models. |\n| DALL-E 3 single image only | `OPENAI_CREATE_IMAGE` with DALL-E 3 only supports `n=1`. Use GPT Image models or DALL-E 2 for multiple images. |\n| Token limits for embeddings | Input must not exceed 8192 tokens per item and 2048 items per batch for embedding models. |\n| Reasoning model restrictions | `temperature` and `top_p` are not supported with reasoning models (o3-mini, etc.). Use `reasoning.effort` instead. |\n| Structured output strict mode | When `strict: true` in json_schema format, ALL schema properties must be listed in the `required` array. |\n| Prompt length varies by model | Image prompt max lengths differ: 32000 (GPT Image), 4000 (DALL-E 3), 1000 (DALL-E 2). |\n\n---\n\n## Quick Reference\n\n| Tool Slug | Description |\n|-----------|-------------|\n| `OPENAI_CREATE_RESPONSE` | Generate text\u002Fmultimodal responses with structured output support |\n| `OPENAI_CREATE_EMBEDDINGS` | Create text embeddings for search, clustering, and RAG |\n| `OPENAI_CREATE_IMAGE` | Generate images from text prompts |\n| `OPENAI_LIST_MODELS` | List all models available to your API key |\n\n---\n\n*Powered by [Composio](https:\u002F\u002Fcomposio.dev)*\n",{"name":5,"requires":50,"description":7},{"mcp":51},[52],"rube","main","a2e3284ed4488780397ee2f78217366e11470dfb","skills\u002Fopenai-automation","https:\u002F\u002Fgithub.com\u002F7dvq6yrkgs-stack\u002Fplaywright","https:\u002F\u002Fplaywright.dev","https:\u002F\u002Fgithub.com\u002F7dvq6yrkgs-stack","2026-04-25 06:04:45",{"source":8,"skillName":5,"commands":61,"prompt":71,"zipAvailable":72},[62,65,68],{"tool":63,"command":64},"npx","npx skills add 7dvq6yrkgs-stack\u002Fplaywright --skill \"OpenAI Automation\"",{"tool":66,"command":67},"bunx","bunx skills add 7dvq6yrkgs-stack\u002Fplaywright --skill \"OpenAI Automation\"",{"tool":69,"command":70},"pnpm","pnpm dlx skills add 7dvq6yrkgs-stack\u002Fplaywright --skill \"OpenAI Automation\"","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002F7dvq6yrkgs-stack\u002Fplaywright\u002Fblob\u002Fmain\u002Fskills\u002Fopenai-automation\u002FSKILL.md",true,[74,80,86,90,94,98,102,106,110,114,120,124],{"path":75,"resourceType":76,"resourceTypeDesc":77,"sizeBytes":78,"sourceHtmlUrl":79},".skillfish.json","REFERENCE","参考文档",253,"https:\u002F\u002Fgithub.com\u002F7dvq6yrkgs-stack\u002Fplaywright\u002Fblob\u002Fmain\u002Fskills\u002Fopenai-automation\u002F.skillfish.json",{"path":81,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":84,"sourceHtmlUrl":85},"assets\u002Fexpected_output.json","ASSET","资产文件",3710,"https:\u002F\u002Fgithub.com\u002Fagency-black\u002FSkills\u002Fblob\u002Fmain\u002Fproject-management\u002Fskills\u002Fscrum-master\u002Fassets\u002Fexpected_output.json",{"path":87,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":88,"sourceHtmlUrl":89},"assets\u002Fexpected_velocity_output.json",2077,"https:\u002F\u002Fgithub.com\u002Fagency-black\u002FSkills\u002Fblob\u002Fmain\u002Fproject-management\u002Fskills\u002Fscrum-master\u002Fassets\u002Fexpected_velocity_output.json",{"path":91,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":92,"sourceHtmlUrl":93},"assets\u002Fsample_sprint_data.json",24611,"https:\u002F\u002Fgithub.com\u002Fagency-black\u002FSkills\u002Fblob\u002Fmain\u002Fproject-management\u002Fskills\u002Fscrum-master\u002Fassets\u002Fsample_sprint_data.json",{"path":95,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":96,"sourceHtmlUrl":97},"assets\u002Fsprint_report_template.md",7496,"https:\u002F\u002Fgithub.com\u002Fagency-black\u002FSkills\u002Fblob\u002Fmain\u002Fproject-management\u002Fskills\u002Fscrum-master\u002Fassets\u002Fsprint_report_template.md",{"path":99,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":100,"sourceHtmlUrl":101},"assets\u002Fteam_health_check_template.md",9965,"https:\u002F\u002Fgithub.com\u002Fagency-black\u002FSkills\u002Fblob\u002Fmain\u002Fproject-management\u002Fskills\u002Fscrum-master\u002Fassets\u002Fteam_health_check_template.md",{"path":103,"resourceType":76,"resourceTypeDesc":77,"sizeBytes":104,"sourceHtmlUrl":105},"references\u002Fretro-formats.md",8163,"https:\u002F\u002Fgithub.com\u002Fagency-black\u002FSkills\u002Fblob\u002Fmain\u002Fproject-management\u002Fskills\u002Fscrum-master\u002Freferences\u002Fretro-formats.md",{"path":107,"resourceType":76,"resourceTypeDesc":77,"sizeBytes":108,"sourceHtmlUrl":109},"references\u002Fteam-dynamics-framework.md",24621,"https:\u002F\u002Fgithub.com\u002Fagency-black\u002FSkills\u002Fblob\u002Fmain\u002Fproject-management\u002Fskills\u002Fscrum-master\u002Freferences\u002Fteam-dynamics-framework.md",{"path":111,"resourceType":76,"resourceTypeDesc":77,"sizeBytes":112,"sourceHtmlUrl":113},"references\u002Fvelocity-forecasting-guide.md",13346,"https:\u002F\u002Fgithub.com\u002Fagency-black\u002FSkills\u002Fblob\u002Fmain\u002Fproject-management\u002Fskills\u002Fscrum-master\u002Freferences\u002Fvelocity-forecasting-guide.md",{"path":115,"resourceType":116,"resourceTypeDesc":117,"sizeBytes":118,"sourceHtmlUrl":119},"scripts\u002Fretrospective_analyzer.py","SCRIPT","脚本",36183,"https:\u002F\u002Fgithub.com\u002Fagency-black\u002FSkills\u002Fblob\u002Fmain\u002Fproject-management\u002Fskills\u002Fscrum-master\u002Fscripts\u002Fretrospective_analyzer.py",{"path":121,"resourceType":116,"resourceTypeDesc":117,"sizeBytes":122,"sourceHtmlUrl":123},"scripts\u002Fsprint_health_scorer.py",28751,"https:\u002F\u002Fgithub.com\u002Fagency-black\u002FSkills\u002Fblob\u002Fmain\u002Fproject-management\u002Fskills\u002Fscrum-master\u002Fscripts\u002Fsprint_health_scorer.py",{"path":125,"resourceType":116,"resourceTypeDesc":117,"sizeBytes":126,"sourceHtmlUrl":127},"scripts\u002Fvelocity_analyzer.py",21963,"https:\u002F\u002Fgithub.com\u002Fagency-black\u002FSkills\u002Fblob\u002Fmain\u002Fproject-management\u002Fskills\u002Fscrum-master\u002Fscripts\u002Fvelocity_analyzer.py",[129,152,162,172,187,202],{"skillNo":130,"name":131,"nameZh":6,"description":132,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":133,"categories":134,"keywords":145,"publishedAt":45},"01M6E3XK4PRPMQHM7Y98FQPTY1","zyte-api-automation","Automate Zyte API tasks via Rube MCP (Composio). Always search tools first for current schemas.","https:\u002F\u002Fgithub.com\u002F7dvq6yrkgs-stack\u002Fplaywright\u002Fblob\u002Fmain\u002Fskills\u002Fzyte-api-automation\u002FSKILL.md",[135,136,139,140],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":137,"categoryName":138,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":23,"categoryName":24,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":141,"categoryName":142,"taxonomyType":143,"taxonomyTypeDesc":144},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[146,36,26,37,147,148,149,150,151,31,29],"zyte api automation","tasks","search","Tools","first","automate",{"skillNo":153,"name":154,"nameZh":6,"description":155,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":156,"categories":157,"keywords":160,"publishedAt":45},"01M6E3XFX6QSTR1QR59NFXJTMA","zylvie-automation","Automate Zylvie tasks via Rube MCP (Composio). Always search tools first for current schemas.","https:\u002F\u002Fgithub.com\u002F7dvq6yrkgs-stack\u002Fplaywright\u002Fblob\u002Fmain\u002Fskills\u002Fzylvie-automation\u002FSKILL.md",[158,159],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":23,"categoryName":24,"taxonomyType":20,"taxonomyTypeDesc":21},[161,26,151,37,147,148,149,150,31,29],"zylvie automation",{"skillNo":163,"name":164,"nameZh":6,"description":165,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":166,"categories":167,"keywords":170,"publishedAt":45},"01M6E3XCX2GDYYCHN58ZJHT37M","zoominfo-automation","Automate Zoominfo tasks via Rube MCP (Composio). Always search tools first for current schemas.","https:\u002F\u002Fgithub.com\u002F7dvq6yrkgs-stack\u002Fplaywright\u002Fblob\u002Fmain\u002Fskills\u002Fzoominfo-automation\u002FSKILL.md",[168,169],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":23,"categoryName":24,"taxonomyType":20,"taxonomyTypeDesc":21},[171,26,148,149,150,151,31,29,147,37],"zoominfo automation",{"skillNo":173,"name":174,"nameZh":6,"description":175,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":176,"categories":177,"keywords":180,"publishedAt":45},"01M6E3X9WJ2T73216WQHHTB9BD","zoho_mail-automation","Automate Zoho Mail tasks via Rube MCP (Composio): email sending, folders, labels, and mailbox management. Always search tools first for current schemas.","https:\u002F\u002Fgithub.com\u002F7dvq6yrkgs-stack\u002Fplaywright\u002Fblob\u002Fmain\u002Fskills\u002Fzoho_mail-automation\u002FSKILL.md",[178,179],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":23,"categoryName":24,"taxonomyType":20,"taxonomyTypeDesc":21},[181,182,183,26,37,147,148,184,149,150,151,185,31,186,29],"mail","zoho mail automation","zoho","email","management","mailbox",{"skillNo":188,"name":189,"nameZh":6,"description":190,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":191,"categories":192,"keywords":198,"publishedAt":45},"01M6E3X719W648XZBW1EC6JQ64","zoho_invoice-automation","Automate Zoho Invoice tasks via Rube MCP (Composio): invoices, estimates, expenses, clients, and payment tracking. Always search tools first for current schemas.","https:\u002F\u002Fgithub.com\u002F7dvq6yrkgs-stack\u002Fplaywright\u002Fblob\u002Fmain\u002Fskills\u002Fzoho_invoice-automation\u002FSKILL.md",[193,194,195],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":23,"categoryName":24,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":196,"categoryName":197,"taxonomyType":143,"taxonomyTypeDesc":144},"01JBSEEDCAT000000000000040","财务",[199,183,26,147,148,149,150,151,200,201,31,29,37],"zoho invoice automation","tracking","payment",{"skillNo":203,"name":204,"nameZh":6,"description":205,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":206,"categories":207,"keywords":210,"publishedAt":45},"01M6E3X28JYTC3QTK32AQF7BYB","zoho_inventory-automation","Automate Zoho Inventory tasks via Rube MCP (Composio): items, orders, warehouses, shipments, and stock management. Always search tools first for current schemas.","https:\u002F\u002Fgithub.com\u002F7dvq6yrkgs-stack\u002Fplaywright\u002Fblob\u002Fmain\u002Fskills\u002Fzoho_inventory-automation\u002FSKILL.md",[208,209],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":23,"categoryName":24,"taxonomyType":20,"taxonomyTypeDesc":21},[26,211,183,212,150,151,149,148,147,37,31,29,213,214,185],"inventory","zoho inventory automation","stock","orders"]