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openai-api

Integrates OpenAI language models including GPT-4, GPT-4o, and Assistants API into applications.

3|1|Updated Feb 2, 2026
Search Tags:#embeddings#ai development#llm#api integration#openai#function calling#gpt-4o
One-click install
npx skills add https://github.com/HouseGarofalo/claude-code-base --skill openai-api-housegarofalo
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: openai-api
Source: https://github.com/HouseGarofalo/claude-code-base/tree/main/.claude/skills/openai-api
Command: npx skills add https://github.com/HouseGarofalo/claude-code-base --skill openai-api-housegarofalo

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill simplifies the integration of powerful OpenAI models like GPT-4, GPT-4o, and Assistants into your applications, enabling advanced AI capabilities.

Core Features & Use Cases

  • Chat Completions: Build conversational AI and perform complex reasoning tasks.
  • Structured Outputs: Ensure AI responses adhere to specific formats like JSON.
  • Function Calling: Enable AI to interact with external tools and APIs.
  • Vision: Analyze image content for various applications.
  • Embeddings: Generate vector representations for semantic search and RAG.
  • Assistants API: Create stateful AI agents for complex workflows.
  • Streaming: Receive responses in real-time for a better user experience.

Quick Start

Use the openai-api skill to generate a Python script that explains async/await in Python using the gpt-4o model.

Frequently Asked Questions about openai-api

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I integrate OpenAI models like GPT-4o into my application?▼

Integrate OpenAI models by using chat completions and the Assistants API to build conversational AI. This skill supports Python and TypeScript examples, handles streaming responses, and includes robust error handling with automatic retries.

What is the best way to get structured JSON outputs from an LLM?▼

Structured outputs from OpenAI models are achieved by enforcing a specific JSON schema. This ensures AI responses adhere to your required formats for reliable data parsing, tool integration, and programmatic application logic.

How do I use OpenAI embeddings for semantic search?▼

OpenAI embeddings generate vector representations of text for semantic search and retrieval-augmented generation (RAG). You can use these vector embeddings to find and retrieve contextually relevant information within your application data.

Can I use the OpenAI API to analyze images and call external functions?▼

Yes, the OpenAI API supports vision for image content analysis and function calling for tool integration. These features allow models to interact with external APIs and process visual data within your workflows.

Does this skill provide examples for streaming responses and handling API errors?▼

Yes, it provides examples for real-time streaming responses and robust error handling with retries. These implementations are available in both Python and TypeScript to ensure resilient API integration.

When should I use the OpenAI Assistants API instead of standard chat completions?▼

Use the Assistants API for developing stateful AI agents and managing complex workflows. Standard chat completions are better suited for stateless conversational interactions and simpler reasoning tasks.

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