[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71MKWDN13ZNDT4MX0VSX2XM":3,"skill-same:01M71MKWDN13ZNDT4MX0VSX2XM":73},{"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":43,"linkOnly":44,"markdownContent":45,"markdownBody":46,"frontMatter":47,"refName":51,"commitSha":52,"skillDirectory":53,"forksCount":54,"repositoryHtmlUrl":55,"repositoryHomepageUrl":6,"ownerHtmlUrl":56,"ownerBlogUrl":6,"ownerDescription":57,"updatedAt":58,"install":59,"resources":72},"01M71MKWDN13ZNDT4MX0VSX2XM","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.","q2805187159\u002FAnvil","q2805187159","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F105276843?v=4","01M71KF4TN999VNJX9FY9QT06A","铁甲勇士宋小宝","01M71KF4V97XXQ7AGES97R4Q9F",3,"MIT","https:\u002F\u002Fgithub.com\u002Fq2805187159\u002FAnvil\u002Fblob\u002Fmain\u002Fskills\u002Fcomposio-skills\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",[24,25,5,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"automation","openai","list","multimodal","embeddings","integration","models","support","operations","generate","via","MCP Integration","create","with","output","api","MCP","structured output","images","2026-11-10 10:56:23",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":48,"description":7},{"mcp":49},[50],"rube","main","6ffadc67c9777282fa1f9330ec12ac8778064409","skills\u002Fcomposio-skills\u002Fopenai-automation",0,"https:\u002F\u002Fgithub.com\u002Fq2805187159\u002FAnvil","https:\u002F\u002Fgithub.com\u002Fq2805187159","Independent Researcher\r\n\r\nEmail: q2805187159@gmail.com\r\n\r\nGithub: q2805187159\r\n\r\nResearch Interests:\r\n- LLM Agents\r\n- Tool Routing\r\n- RAG","2026-06-24 18:31:41",{"source":8,"skillName":5,"commands":60,"prompt":70,"zipAvailable":71},[61,64,67],{"tool":62,"command":63},"npx","npx skills add q2805187159\u002FAnvil --skill \"OpenAI Automation\"",{"tool":65,"command":66},"bunx","bunx skills add q2805187159\u002FAnvil --skill \"OpenAI Automation\"",{"tool":68,"command":69},"pnpm","pnpm dlx skills add q2805187159\u002FAnvil --skill \"OpenAI Automation\"","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fq2805187159\u002FAnvil\u002Fblob\u002Fmain\u002Fskills\u002Fcomposio-skills\u002Fopenai-automation\u002FSKILL.md",true,[],[74,105,138,145,167,190],{"skillNo":75,"name":76,"nameZh":6,"description":77,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":78,"licenseFileUrl":79,"sourceHtmlUrl":80,"categories":81,"keywords":88,"publishedAt":43},"01M71NPWVVMB1AZ8XGKNJFT0YP","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.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fq2805187159\u002FAnvil\u002Fblob\u002Fmain\u002Fskills\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fq2805187159\u002FAnvil\u002Fblob\u002Fmain\u002Fskills\u002Fwebapp-testing\u002FSKILL.md",[82,85],{"categoryNo":83,"categoryName":84,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":86,"categoryName":87,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[89,90,91,92,93,94,95,96,97,98,99,37,100,101,102,103,104],"webapp","testing","webapp testing","for","applications","local","frontend","debugging","toolkit","logs","browser","playwright","web","behavior","ui","screenshots",{"skillNo":106,"name":107,"nameZh":6,"description":108,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":109,"categories":110,"keywords":119,"publishedAt":43},"01M71NPTQ96H4XSZVZT422BNRB","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\u002Fq2805187159\u002FAnvil\u002Fblob\u002Fmain\u002Fskills\u002Fweb-design-guidelines\u002FSKILL.md",[111,114],{"categoryNo":112,"categoryName":113,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":115,"categoryName":116,"taxonomyType":117,"taxonomyTypeDesc":118},"01JBSEEDCAT000000000000032","设计师","PROFESSION","适用职业",[120,121,101,122,123,92,124,125,126,127,128,129,130,131,132,133,134,135,136,137],"guidelines","design","web design guidelines","web design","vercel","interface","web interface guidelines","use","when","best practices","file","code","review","check","compliance","audit","ux","accessibility",{"skillNo":139,"name":107,"nameZh":6,"description":108,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":140,"categories":141,"keywords":144,"publishedAt":43},"01M71NPS2D31Y2BGFYA9DB7CE4","https:\u002F\u002Fgithub.com\u002Fq2805187159\u002FAnvil\u002Fblob\u002Fmain\u002Fskills\u002Fweb-design-guidelines copy\u002FSKILL.md",[142,143],{"categoryNo":112,"categoryName":113,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":115,"categoryName":116,"taxonomyType":117,"taxonomyTypeDesc":118},[120,121,101,122,123,92,124,125,126,127,128,129,130,131,132,133,134,135,136,137],{"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":155,"publishedAt":43},"01M71NPQ9DRT54QSQJ0BHBM3VG","video-generation","Use this skill when the user requests to generate, create, or imagine videos. Supports structured prompts and reference image for guided generation.","https:\u002F\u002Fgithub.com\u002Fq2805187159\u002FAnvil\u002Fblob\u002Fmain\u002Fskills\u002Fvideo-generation\u002FSKILL.md",[151,154],{"categoryNo":152,"categoryName":153,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[156,157,158,92,127,128,159,36,160,161,33,162,163,164,165,166],"Video","video generation","generation","image","imagine","requests","videos","reference","prompts","user","structured",{"skillNo":168,"name":169,"nameZh":6,"description":170,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":171,"categories":172,"keywords":174,"publishedAt":43},"01M71NPN57CT154SWXAS52475X","youtube-downloader","Download YouTube videos with customizable quality and format options. Use this skill when the user asks to download, save, or grab YouTube videos. Supports various quality settings (best, 1080p, 720p, 480p, 360p), multiple formats (mp4, webm, mkv), and audio-only downloads as MP3.","https:\u002F\u002Fgithub.com\u002Fq2805187159\u002FAnvil\u002Fblob\u002Fmain\u002Fskills\u002Fvideo-downloader\u002FSKILL.md",[173],{"categoryNo":152,"categoryName":153,"taxonomyType":21,"taxonomyTypeDesc":22},[175,176,177,178,179,180,181,127,128,182,37,183,184,185,186,187,162,188,165,189],"youtube","youtube downloader","mp4","as","downloads","Audio","only","mp3","options","quality","format","settings","grab","formats","download",{"skillNo":191,"name":192,"nameZh":6,"description":193,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":194,"categories":195,"keywords":196,"publishedAt":43},"01M71NPKD95YB8N3J6J78QGFFW","verification-before-completion","Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always","https:\u002F\u002Fgithub.com\u002Fq2805187159\u002FAnvil\u002Fblob\u002Fmain\u002Fskills\u002Fverification-before-completion\u002FSKILL.md",[],[197,198,199,200,201,202,203,127,204,128,205,38,206,207,208,209],"verification","verification before completion","running","evidence","complete","prs","claim","assertions","committing","claims","work","about","commands"]