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llmintegration

Integrate Claude, GPT, Gemini, and Ollama APIs with a pluggable provider abstraction.

1|Updated Dec 10, 2025
Search Tags:#prompt engineering#llm#claude#ai integration#gemini#token management#gpt#ollama
One-click install
npx skills add https://github.com/markus41/lobbi-design-system --skill llmintegration-markus41
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llmintegration
Source: https://github.com/markus41/lobbi-design-system/tree/main/.claude/skills/llm-integration
Command: npx skills add https://github.com/markus41/lobbi-design-system --skill llmintegration-markus41

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires anthropic, openai, google-generativeai, ollama, tiktoken.

What problem does it solve?

This Skill simplifies the integration of various Large Language Models (LLMs) and provides best practices for prompt engineering, enabling you to build powerful AI applications with optimal performance and output.

Core Features & Use Cases

  • Multi-Model Integration: Connect with Claude, OpenAI GPT, Google Gemini, and Ollama local models.
  • Prompt Engineering: Apply structured prompts, few-shot examples, and Chain-of-Thought techniques for better results.
  • Token Management: Count and truncate text to manage LLM token limits efficiently.
  • Use Case: Integrate the Anthropic Claude API to generate a response, then abstract it to work with other LLMs using a common interface, ensuring flexibility and future-proofing.

Quick Start

Integrate the Anthropic Claude API to generate a response to the prompt "Explain large language models in simple terms."

Frequently Asked Questions about llmintegration

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

FAQPage Schema
How do I integrate multiple LLM providers like Claude, GPT, and Gemini into one application?▼

Multi-provider LLM integration uses a pluggable abstraction layer to connect Claude, OpenAI GPT, Google Gemini, and Ollama through a unified factory-based interface, enabling seamless model switching without rewriting code.

What's the best way to handle token limits when working with large language models?▼

Token management involves counting and truncating text before sending to LLMs to stay within model limits, preventing truncation errors and controlling API costs across Claude, GPT, Gemini, and Ollama.

How do I improve LLM output quality through prompt engineering?▼

Prompt engineering applies structured prompts, few-shot examples, and Chain-of-Thought techniques to guide LLMs toward better reasoning and more accurate responses across all supported providers.

Can I use streaming and function calling with multiple LLM APIs simultaneously?▼

Yes, this Skill provides streaming and function calling capabilities across Claude, GPT, Gemini, and Ollama, enabling real-time responses and tool orchestration with a single abstraction layer.

Do I need to rewrite code if I switch from OpenAI to Claude or another LLM provider?▼

No, the factory-based LLMProvider instantiation abstracts provider differences, allowing you to switch between Claude, GPT, Gemini, and Ollama by changing configuration without altering application logic.

Can I run local LLM models alongside cloud-based APIs?▼

Yes, Ollama integration enables local model deployment within the same orchestration framework, letting you mix local models with cloud providers like Claude, GPT, and Gemini in production environments.

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