OpenAI Automation
Automate OpenAI API operations -- generate responses with multimodal and structured output support, create embeddings, generate images, and list models via the Composio MCP integration.
Best use case
OpenAI Automation is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Automate OpenAI API operations -- generate responses with multimodal and structured output support, create embeddings, generate images, and list models via the Composio MCP integration.
Teams using OpenAI Automation should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/openai-automation/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How OpenAI Automation Compares
| Feature / Agent | OpenAI Automation | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Automate OpenAI API operations -- generate responses with multimodal and structured output support, create embeddings, generate images, and list models via the Composio MCP integration.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# OpenAI Automation
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](https://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
| Pitfall | Detail |
|---------|--------|
| DALL-E deprecation | DALL-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 only | `OPENAI_CREATE_IMAGE` with DALL-E 3 only supports `n=1`. Use GPT Image models or DALL-E 2 for multiple images. |
| Token limits for embeddings | Input must not exceed 8192 tokens per item and 2048 items per batch for embedding models. |
| Reasoning model restrictions | `temperature` and `top_p` are not supported with reasoning models (o3-mini, etc.). Use `reasoning.effort` instead. |
| Structured output strict mode | When `strict: true` in json_schema format, ALL schema properties must be listed in the `required` array. |
| Prompt length varies by model | Image prompt max lengths differ: 32000 (GPT Image), 4000 (DALL-E 3), 1000 (DALL-E 2). |
---
## Quick Reference
| Tool Slug | Description |
|-----------|-------------|
| `OPENAI_CREATE_RESPONSE` | Generate text/multimodal responses with structured output support |
| `OPENAI_CREATE_EMBEDDINGS` | Create text embeddings for search, clustering, and RAG |
| `OPENAI_CREATE_IMAGE` | Generate images from text prompts |
| `OPENAI_LIST_MODELS` | List all models available to your API key |
---
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