OpenAI Automation

SkillMedia

Automate OpenAI API operations -- generate responses with multimodal and structured output support, create embeddings, generate images, and list models via the Composio MCP integration.

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the OpenAI Automation skill

What this skill tells your AI

The instructions your AI receives, as published by composiohq/awesome-claude-skills in composio-skills/openai-automation/SKILL.md and read by ahel’s review.

Automate 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.

Toolkit docs: composio.dev/toolkits/openai


Setup

  1. Add the Composio MCP server to your client: https://rube.app/mcp
  2. Connect your OpenAI account when prompted (API key authentication)
  3. Start using the workflows below

Core Workflows

1. Generate a Response (Text, Multimodal, Structured)

Use OPENAI_CREATE_RESPONSE for one-shot model responses including text, image analysis, OCR, and structured JSON outputs.

Tool: OPENAI_CREATE_RESPONSE
Inputs:
  - model: string (required) -- e.g., "gpt-5", "gpt-4o", "o3-mini"
  - input: string | array (required)
    Simple: "Explain quantum computing"
    Multimodal: [
      { role: "user", content: [
        { type: "input_text", text: "What is in this image?" },
        { type: "input_image", image_url: { url: "https://..." } }
      ]}
    ]
  - temperature: number (0-2, optional -- not supported with reasoning models)
  - max_output_tokens: integer (optional)
  - reasoning: { effort: "none" | "minimal" | "low" | "medium" | "high" }
  - text: object (structured output config)
    - format: { type: "json_schema", name: "...", schema: {...}, strict: true }
  - tools: array (function, code_interpreter, file_search, web_search)
  - tool_choice: "auto" | "none" | "required" | { type: "function", function: { name: "..." } }
  - store: boolean (false to opt out of model distillation)
  - stream: boolean

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 }.

2. Create Embeddings

Use OPENAI_CREATE_EMBEDDINGS for vector search, clustering, recommendations, and RAG pipelines.

Tool: OPENAI_CREATE_EMBEDDINGS
Inputs:
  - input: string | string[] | int[] | int[][] (required) -- max 8192 tokens, max 2048 items
  - model: string (required) -- "text-embedding-3-small", "text-embedding-3-large", "text-embedding-ada-002"
  - dimensions: integer (optional, only for text-embedding-3 and later)
  - encoding_format: "float" | "base64" (default "float")
  - user: string (optional, end-user ID for abuse monitoring)

3. Generate Images

Use OPENAI_CREATE_IMAGE to create images from text prompts using GPT Image or DALL-E models.

Tool: OPENAI_CREATE_IMAGE
Inputs:
  - model: string (required) -- "gpt-image-1", "gpt-image-1.5", "dall-e-3", "dall-e-2"
  - prompt: string (required) -- max 32000 chars (GPT Image), 4000 (DALL-E 3), 1000 (DALL-E 2)
  - size: "1024x1024" | "1536x1024" | "1024x1536" | "auto" | "256x256" | "512x512" | "1792x1024" | "1024x1792"
  - quality: "standard" | "hd" | "auto" | "high" | "medium" | "low"
  - n: integer (1-10; DALL-E 3 supports n=1 only)
  - background: "transparent" | "opaque" | "auto" (GPT Image models only)
  - style: "vivid" | "natural" (DALL-E 3 only)
  - user: string (optional)

4. List Available Models

Use OPENAI_LIST_MODELS to discover which models are accessible with your API key.

Tool: OPENAI_LIST_MODELS
Inputs: (none)

Known Pitfalls

PitfallDetail
DALL-E deprecationDALL-E 2 and DALL-E 3 are deprecated and will stop being supported on 05/12/2026. Prefer GPT Image models.
DALL-E 3 single image onlyOPENAI_CREATE_IMAGE with DALL-E 3 only supports n=1. Use GPT Image models or DALL-E 2 for multiple images.
Token limits for embeddingsInput must not exceed 8192 tokens per item and 2048 items per batch for embedding models.
Reasoning model restrictionstemperature and top_p are not supported with reasoning models (o3-mini, etc.). Use reasoning.effort instead.
Structured output strict modeWhen strict: true in json_schema format, ALL schema properties must be listed in the required array.
Prompt length varies by modelImage prompt max lengths differ: 32000 (GPT Image), 4000 (DALL-E 3), 1000 (DALL-E 2).

Quick Reference

Tool SlugDescription
OPENAI_CREATE_RESPONSEGenerate text/multimodal responses with structured output support
OPENAI_CREATE_EMBEDDINGSCreate text embeddings for search, clustering, and RAG
OPENAI_CREATE_IMAGEGenerate images from text prompts
OPENAI_LIST_MODELSList all models available to your API key

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ahel review

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    community integration, published by composiohq, not openai

Automated review, not a security audit. Ruleset v1.

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Item type
skill
Key
openai-automation-composiohq
Source
github.com/composiohq/awesome-claude-skills