[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6J4KCNC6JPRNDDKM9VMM3VT":3,"skill-same:01M6J4KCNC6JPRNDDKM9VMM3VT":81},{"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":23,"publishedAt":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":52,"commitSha":53,"skillDirectory":54,"forksCount":55,"repositoryHtmlUrl":56,"repositoryHomepageUrl":57,"ownerHtmlUrl":58,"ownerBlogUrl":6,"ownerDescription":59,"updatedAt":60,"install":61,"resources":74},"01M6J4KCNC6JPRNDDKM9VMM3VT","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.","onfire7777\u002Funiversal-ai-skills-library","onfire7777","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F240866566?v=4","01M6HZJCF2QEW8DY7HE1JDKGK2","Jake B","01M6HZJCKK8SEXKN76V9SGKZ12",17,"MIT","https:\u002F\u002Fgithub.com\u002Fonfire7777\u002Funiversal-ai-skills-library\u002Fblob\u002Fmain\u002Fskills\u002Fopenai-automation\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"automation","openai","OpenAI Automation","api","with","output","via","structured output","models","images","operations","list","MCP","MCP Integration","integration","multimodal","embeddings","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":49,"description":7},{"mcp":50},[51],"rube","main","2a76bf42ce5c860adf8d0f3fcf3f6804109ac65b","skills\u002Fopenai-automation",0,"https:\u002F\u002Fgithub.com\u002Fonfire7777\u002Funiversal-ai-skills-library","https:\u002F\u002Fgithub.com\u002Fonfire7777\u002Funiversal-ai-skills-library\u002Fwiki","https:\u002F\u002Fgithub.com\u002Fonfire7777","trying to create something actually useful","2026-09-11 13:25:26",{"source":8,"skillName":5,"commands":62,"prompt":72,"zipAvailable":73},[63,66,69],{"tool":64,"command":65},"npx","npx skills add onfire7777\u002Funiversal-ai-skills-library --skill openai-automation",{"tool":67,"command":68},"bunx","bunx skills add onfire7777\u002Funiversal-ai-skills-library --skill openai-automation",{"tool":70,"command":71},"pnpm","pnpm dlx skills add onfire7777\u002Funiversal-ai-skills-library --skill openai-automation","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fonfire7777\u002Funiversal-ai-skills-library\u002Fblob\u002Fmain\u002Fskills\u002Fopenai-automation\u002FSKILL.md",true,[75],{"path":76,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":79,"sourceHtmlUrl":80},".openskills.json","REFERENCE","参考文档",256,"https:\u002F\u002Fgithub.com\u002Fonfire7777\u002Funiversal-ai-skills-library\u002Fblob\u002Fmain\u002Fskills\u002Fopenai-automation\u002F.openskills.json",[82,106,115,125,145,161],{"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":97,"publishedAt":44},"01M6J7MPK8ZE9VQJR4KK6ZMZJQ","zyte-api-automation","Automate Zyte API tasks via Rube MCP (Composio). Always search tools first for current schemas.","https:\u002F\u002Fgithub.com\u002Fonfire7777\u002Funiversal-ai-skills-library\u002Fblob\u002Fmain\u002Fskills\u002Fzyte-api-automation\u002FSKILL.md",[88,91,92],{"categoryNo":89,"categoryName":90,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":93,"categoryName":94,"taxonomyType":95,"taxonomyTypeDesc":96},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[24,27,98,99,36,30,100,101,102,103,104,105],"zyte api automation","for","composio","automate","first","Tools","search","tasks",{"skillNo":107,"name":108,"nameZh":6,"description":109,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":110,"categories":111,"keywords":113,"publishedAt":44},"01M6J7MK45K2ZZ0EWTP1N6SZ7F","zylvie-automation","Automate Zylvie tasks via Rube MCP (Composio). Always search tools first for current schemas.","https:\u002F\u002Fgithub.com\u002Fonfire7777\u002Funiversal-ai-skills-library\u002Fblob\u002Fmain\u002Fskills\u002Fzylvie-automation\u002FSKILL.md",[112],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[24,114,36,105,99,30,100,101,102,103,104],"zylvie automation",{"skillNo":116,"name":117,"nameZh":6,"description":118,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":119,"categories":120,"keywords":122,"publishedAt":44},"01M6J7MGDCVZ3YCXGEAPR5V3MS","zoominfo-automation","Automate Zoominfo tasks via Rube MCP (Composio). Always search tools first for current schemas.","https:\u002F\u002Fgithub.com\u002Fonfire7777\u002Funiversal-ai-skills-library\u002Fblob\u002Fmain\u002Fskills\u002Fzoominfo-automation\u002FSKILL.md",[121],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[123,124,24,101,100,103,30,105,99,104,36,102],"zoominfo","zoominfo automation",{"skillNo":126,"name":127,"nameZh":6,"description":128,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":129,"categories":130,"keywords":132,"publishedAt":44},"01M6J7MDK48N09B7C6H9TCPY9A","zoom-out","Tell the agent to zoom out and give broader context or a higher-level perspective. Use when you're unfamiliar with a section of code or need to understand how it fits into the bigger picture.","https:\u002F\u002Fgithub.com\u002Fonfire7777\u002Funiversal-ai-skills-library\u002Fblob\u002Fmain\u002Fskills\u002Fzoom-out\u002FSKILL.md",[131],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[133,134,135,136,137,138,28,139,140,141,142,143,144],"zoom out","zoom","perspective","code","re","you","understand","it","use","context","how","true",{"skillNo":146,"name":147,"nameZh":6,"description":148,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":149,"categories":150,"keywords":152,"publishedAt":44},"01M6J7MAD47Q9A55AK3EXS914H","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\u002Fonfire7777\u002Funiversal-ai-skills-library\u002Fblob\u002Fmain\u002Fskills\u002Fzoho-mail-automation\u002FSKILL.md",[151],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[153,154,155,156,24,157,104,105,36,103,158,99,30,159,100,160,101,102],"mail","zoho mail automation","zoho","zoho mail","email","labels","mailbox","management",{"skillNo":162,"name":163,"nameZh":6,"description":164,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":165,"categories":166,"keywords":171,"publishedAt":44},"01M6J7M76HTWPP8TRWWCDS42NB","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\u002Fonfire7777\u002Funiversal-ai-skills-library\u002Fblob\u002Fmain\u002Fskills\u002Fzoho-invoice-automation\u002FSKILL.md",[167,168],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":169,"categoryName":170,"taxonomyType":95,"taxonomyTypeDesc":96},"01JBSEEDCAT000000000000040","财务",[172,24,155,173,36,30,99,174,175,176,100,177,178,101,102,103,104,105],"invoice","zoho invoice automation","invoices","expenses","clients","payment","tracking"]