A-Curated-List-of-Awesome-Claude-Skills OpenAI Automation

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

install

source · Clone the upstream repo

git clone https://github.com/Engineer1999/A-Curated-List-of-Awesome-Claude-Skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/Engineer1999/A-Curated-List-of-Awesome-Claude-Skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/app-automations/openai-automation" ~/.claude/skills/engineer1999-a-curated-list-of-awesome-claude-skills-openai-automation && rm -rf "$T"

manifest: app-automations/openai-automation/SKILL.md

source content

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: claude-skills.ai/toolkits/openai

Setup

Add the Claude Skill MCP server to your client:
```
https://rube.app/mcp
```
Connect your OpenAI account when prompted (API key authentication)
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

{ 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

Part of the Claude Skills Hub