---

name: openai description: OpenAI API via curl. Use this skill for GPT chat completions, DALL-E image generation, Whisper audio transcription, embeddings, and text-to-speech. vm0_secrets:

• OPENAI_API_KEY

---

OpenAI API

Use the OpenAI API via direct curl calls to access GPT models, DALL-E image generation, Whisper transcription, embeddings, and text-to-speech.

Official docs: https://platform.openai.com/docs/api-reference

---

When to Use

Use this skill when you need to:

• Chat completions with GPT-4o, GPT-4, or GPT-3.5 models
• Image generation with DALL-E 3
• Audio transcription with Whisper
• Text-to-speech audio generation
• Text embeddings for semantic search and RAG
• Vision tasks (analyze images with GPT-4o)

---

Prerequisites

1. Sign up at OpenAI Platform and create an account
2. Go to API Keys and generate a new secret key
3. Add billing information and set usage limits

export OPENAI_API_KEY="sk-..."

Pricing (as of 2025)

Model	Input (per 1M tokens)	Output (per 1M tokens)
GPT-4o	$2.50	$10.00
GPT-4o-mini	$0.15	$0.60
GPT-4 Turbo	$10.00	$30.00
text-embedding-3-small	$0.02	-
text-embedding-3-large	$0.13	-

Rate Limits

Rate limits vary by tier (based on usage history). Check your limits at Platform Settings.

---

Important: When using $VAR in a command that pipes to another command, wrap the command containing $VAR in bash -c '...'. Due to a Claude Code bug, environment variables are silently cleared when pipes are used directly.

bash -c 'curl -s "https://api.example.com" -H "Authorization: Bearer $API_KEY"' | jq .

How to Use

All examples below assume you have OPENAI_API_KEY set.

Base URL: https://api.openai.com/v1

---

1. Basic Chat Completion

Send a simple chat message:

Write to /tmp/openai_request.json:

{
  "model": "gpt-4o-mini",
  "messages": [{"role": "user", "content": "Hello, who are you?"}]
}

Then run:

bash -c 'curl -s "https://api.openai.com/v1/chat/completions" -H "Content-Type: application/json" -H "Authorization: Bearer ${OPENAI_API_KEY}" -d @/tmp/openai_request.json' | jq '.choices[0].message.content'

Available models:

• gpt-4o: Latest flagship model (128K context)
• gpt-4o-mini: Fast and affordable (128K context)
• gpt-4-turbo: Previous generation (128K context)
• gpt-3.5-turbo: Legacy model (16K context)
• o1: Reasoning model for complex tasks
• o1-mini: Smaller reasoning model

---

2. Chat with System Prompt

Use a system message to set behavior:

Write to /tmp/openai_request.json:

{
  "model": "gpt-4o-mini",
  "messages": [
    {"role": "system", "content": "You are a helpful assistant that responds in JSON format."},
    {"role": "user", "content": "List 3 programming languages with their main use cases."}
  ]
}

Then run:

bash -c 'curl -s "https://api.openai.com/v1/chat/completions" -H "Content-Type: application/json" -H "Authorization: Bearer ${OPENAI_API_KEY}" -d @/tmp/openai_request.json' | jq '.choices[0].message.content'

---

3. Streaming Response

Get real-time token-by-token output:

Write to /tmp/openai_request.json:

{
  "model": "gpt-4o-mini",
  "messages": [{"role": "user", "content": "Write a haiku about programming."}],
  "stream": true
}

Then run:

curl -s "https://api.openai.com/v1/chat/completions" -H "Content-Type: application/json" -H "Authorization: Bearer ${OPENAI_API_KEY}" -d @/tmp/openai_request.json

Streaming returns Server-Sent Events (SSE) with delta chunks.

---

4. JSON Mode

Force the model to return valid JSON:

Write to /tmp/openai_request.json:

{
  "model": "gpt-4o-mini",
  "messages": [
    {"role": "system", "content": "Return JSON only."},
    {"role": "user", "content": "Give me info about Paris: name, country, population."}
  ],
  "response_format": {"type": "json_object"}
}

Then run:

bash -c 'curl -s "https://api.openai.com/v1/chat/completions" -H "Content-Type: application/json" -H "Authorization: Bearer ${OPENAI_API_KEY}" -d @/tmp/openai_request.json' | jq '.choices[0].message.content'

---

5. Vision (Image Analysis)

Analyze an image with GPT-4o:

Write to /tmp/openai_request.json:

{
  "model": "gpt-4o-mini",
  "messages": [
    {
      "role": "user",
      "content": [
        {"type": "text", "text": "What is in this image?"},
        {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/3/3a/Cat03.jpg/1200px-Cat03.jpg"}}
      ]
    }
  ],
  "max_tokens": 300
}

Then run:

bash -c 'curl -s "https://api.openai.com/v1/chat/completions" -H "Content-Type: application/json" -H "Authorization: Bearer ${OPENAI_API_KEY}" -d @/tmp/openai_request.json' | jq '.choices[0].message.content'

---

6. Function Calling (Tools)

Define functions the model can call:

Write to /tmp/openai_request.json:

{
  "model": "gpt-4o-mini",
  "messages": [{"role": "user", "content": "What is the weather in Tokyo?"}],
  "tools": [
    {
      "type": "function",
      "function": {
        "name": "get_weather",
        "description": "Get current weather for a location",
        "parameters": {
          "type": "object",
          "properties": {
            "location": {"type": "string", "description": "City name"}
          },
          "required": ["location"]
        }
      }
    }
  ]
}

Then run:

bash -c 'curl -s "https://api.openai.com/v1/chat/completions" -H "Content-Type: application/json" -H "Authorization: Bearer ${OPENAI_API_KEY}" -d @/tmp/openai_request.json' | jq '.choices[0].message.tool_calls'

---

7. Generate Embeddings

Create vector embeddings for text:

Write to /tmp/openai_request.json:

{
  "model": "text-embedding-3-small",
  "input": "The quick brown fox jumps over the lazy dog."
}

Then run:

bash -c 'curl -s "https://api.openai.com/v1/embeddings" -H "Content-Type: application/json" -H "Authorization: Bearer ${OPENAI_API_KEY}" -d @/tmp/openai_request.json' | jq '.data[0].embedding[:5]'

This extracts the first 5 dimensions of the embedding vector.

Embedding models:

• text-embedding-3-small: 1536 dimensions, fastest
• text-embedding-3-large: 3072 dimensions, most capable

---

8. Generate Image (DALL-E 3)

Create an image from text:

Write to /tmp/openai_request.json:

{
  "model": "dall-e-3",
  "prompt": "A white cat sitting on a windowsill, digital art",
  "n": 1,
  "size": "1024x1024"
}

Then run:

bash -c 'curl -s "https://api.openai.com/v1/images/generations" -H "Content-Type: application/json" -H "Authorization: Bearer ${OPENAI_API_KEY}" -d @/tmp/openai_request.json' | jq '.data[0].url'

Parameters:

• size: 1024x1024, 1792x1024, or 1024x1792
• quality: standard or hd
• style: vivid or natural

---

9. Audio Transcription (Whisper)

Transcribe audio to text:

bash -c 'curl -s "https://api.openai.com/v1/audio/transcriptions" -H "Authorization: Bearer ${OPENAI_API_KEY}" -F "file=@audio.mp3" -F "model=whisper-1"' | jq '.text'

Supports: mp3, mp4, mpeg, mpga, m4a, wav, webm (max 25MB).

---

10. Text-to-Speech

Generate audio from text:

Write to /tmp/openai_request.json:

{
  "model": "tts-1",
  "input": "Hello! This is a test of OpenAI text to speech.",
  "voice": "alloy"
}

Then run:

curl -s "https://api.openai.com/v1/audio/speech" -H "Content-Type: application/json" -H "Authorization: Bearer ${OPENAI_API_KEY}" -d @/tmp/openai_request.json --output speech.mp3

Voices: alloy, echo, fable, onyx, nova, shimmer

Models: tts-1 (fast), tts-1-hd (high quality)

---

11. List Available Models

Get all available models:

bash -c 'curl -s "https://api.openai.com/v1/models" -H "Authorization: Bearer ${OPENAI_API_KEY}"' | jq -r '.data[].id' | sort | head -20

---

12. Check Token Usage

Extract usage from response:

Write to /tmp/openai_request.json:

{
  "model": "gpt-4o-mini",
  "messages": [{"role": "user", "content": "Hi!"}]
}

Then run:

bash -c 'curl -s "https://api.openai.com/v1/chat/completions" -H "Content-Type: application/json" -H "Authorization: Bearer ${OPENAI_API_KEY}" -d @/tmp/openai_request.json' | jq '.usage'

This returns token counts for both input and output.

Response includes:

• prompt_tokens: Input token count
• completion_tokens: Output token count
• total_tokens: Sum of both

---

Guidelines

1. Choose the right model: Use gpt-4o-mini for most tasks, gpt-4o for complex reasoning, o1 for advanced math/coding
2. Set max_tokens: Prevent runaway generation and control costs
3. Use streaming for long responses: Better UX for real-time applications
4. JSON mode requires system prompt: Include JSON instructions when using response_format
5. Vision requires gpt-4o models: Only gpt-4o and gpt-4o-mini support image input
6. Batch similar requests: Use embeddings API batch input for efficiency
7. Monitor usage: Check dashboard regularly to avoid unexpected charges