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MinhHoangDono
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ai-api

Integrate Anthropic, OpenAI, and Gemini APIs for chat, tool use, and structured outputs.

Updated Mar 7, 2026
Search Tags:#api#embeddings#multimodal#tool calling#ai integration#streaming#structured output
One-click install
npx skills add https://github.com/MinhHoangDono/antigravity-kit --skill ai-api-minhhoangdono
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ai-api
Source: https://github.com/MinhHoangDono/antigravity-kit/tree/main/.agent/skills/ai-api
Command: npx skills add https://github.com/MinhHoangDono/antigravity-kit --skill ai-api-minhhoangdono

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Integrates major AI provider APIs into a single, usable interface so you can reliably generate chat responses, handle streaming outputs, and perform tool/function calling with structured results.

Core Features & Use Cases

  • Multi-provider API integration: Work with Anthropic Claude, OpenAI, and Google Gemini using their respective SDK patterns.
  • Streaming and token-aware responses: Generate user-facing output incrementally for better responsiveness and controllable cost.
  • Tool use and structured output: Support function calling/tool schemas and JSON/structured response formats for downstream automation.
  • Embeddings and multimodal inputs: Use embeddings for search/similarity and enable vision/multimodal workflows where supported.

Quick Start

Configure your provider API key in environment variables and request streaming tool-capable responses from the supported model for your target task.

Frequently Asked Questions about ai-api

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

FAQPage Schema
How do I integrate multiple AI APIs for chat, vision, and tool calling in my backend?▼

To integrate multiple AI APIs, this interface connects Anthropic Claude, OpenAI, and Gemini SDKs to generate chat responses, handle tool use, and process vision inputs with structured outputs across your application backend.

What is the best way to get structured JSON output from AI provider responses?▼

Getting structured JSON output requires applying prompt or response formatting to constrain provider-specific response blocks. This ensures AI provider APIs return schema-constrained results for reliable downstream automation.

Can I use streaming completions and function calling across different AI providers?▼

Yes, you can use streaming completions and function calling across different AI providers. The interface supports incremental output generation and tool schemas for Anthropic, OpenAI, and Gemini to improve responsiveness and control token costs.

How do I handle API key management and transient failures when connecting to AI models?▼

Handling AI model connections requires secure API key configuration in environment variables, explicit token limits, and retry/backoff strategies for transient failures. This ensures error-safe processing of provider-specific response blocks during generation.

Does this approach support multimodal inputs and embeddings for similarity search?▼

Yes, this approach supports multimodal inputs and embeddings for similarity search. It integrates provider APIs to enable vision workflows and generate embeddings, allowing you to handle multimodal inputs where supported by the target AI model.

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