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Dokkabei97
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ai-integration

Generate LLM API integration code for OpenAI, Anthropic, and Google.

2|Updated Feb 28, 2026
Search Tags:#development#ai#llm#rag#api integration#openai#anthropic
One-click install
npx skills add https://github.com/Dokkabei97/forged-claude-code --skill ai-integration-dokkabei97
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ai-integration
Source: https://github.com/Dokkabei97/forged-claude-code/tree/main/skills/cto/ai-integration
Command: npx skills add https://github.com/Dokkabei97/forged-claude-code --skill ai-integration-dokkabei97

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill simplifies the complex process of integrating Large Language Model (LLM) APIs into your applications, enabling you to leverage AI capabilities efficiently.

Core Features & Use Cases

  • Multi-Provider Support: Integrates with OpenAI, Anthropic, and Google LLM APIs.
  • Advanced Patterns: Implements prompt engineering, streaming responses, structured output, and error handling.
  • RAG Setup: Provides guidance for setting up Retrieval-Augmented Generation pipelines.
  • Use Case: Quickly add a chatbot feature to your web application using Anthropic's Claude API, ensuring smooth streaming responses and robust error handling.

Quick Start

Use the ai-integration skill to generate TypeScript code for integrating with the OpenAI API.

Frequently Asked Questions about ai-integration

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

FAQPage Schema
How do I integrate LLM APIs into my web application?▼

To integrate LLM APIs, you can generate production-ready TypeScript code for major providers like OpenAI, Anthropic, and Google. This includes implementing advanced patterns such as prompt engineering, structured output, and streaming responses.

What is the best way to set up a RAG pipeline with an LLM API?▼

Setting up a RAG pipeline requires guiding the LLM API integration process and optionally configuring a vector database. This Skill provides the necessary setup guidance to implement Retrieval-Augmented Generation alongside your chosen provider.

Does this LLM API integration approach support streaming responses and error handling?▼

Yes, LLM API integration supports advanced patterns including streaming responses and robust error handling. It generates code to ensure smooth data delivery and manages API failures effectively across supported providers.

Can I use this to generate integration code for both OpenAI and Anthropic?▼

Yes, you can generate integration code for both OpenAI and Anthropic. It offers multi-provider support, allowing you to seamlessly implement API calls for various Large Language Models within a single application.

Do I need specific SDKs to implement structured output from LLMs?▼

Yes, implementing structured output requires the official SDKs for each LLM provider you intend to use. If you are setting up a RAG pipeline, you will also optionally need a vector database to store and retrieve embeddings.

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