ax

Develop LLM applications with typed signatures using the @ax-llm/ax TypeScript library.

Updated Feb 15, 2026
One-click install
npx skills add https://github.com/eycjur/wandb_agent_hackathon --skill ax-eycjur
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ax
Source: https://github.com/eycjur/wandb_agent_hackathon/tree/main/llm-as-a-judge-mvp/.claude/skills/ax
Command: npx skills add https://github.com/eycjur/wandb_agent_hackathon --skill ax-eycjur

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill simplifies the development of LLM-powered applications by providing a type-safe framework for defining inputs, outputs, and interactions with various AI models.

Core Features & Use Cases

  • Type-Safe Signatures: Define clear input/output schemas for LLM calls using a fluent TypeScript API.
  • Multi-Provider Support: Easily switch between different LLM providers (OpenAI, Anthropic, Gemini, etc.).
  • Agents & Workflows: Build complex agents with tools and orchestrate multi-step AI workflows.
  • Use Case: Develop a customer support chatbot that can understand user queries, search a knowledge base, and generate helpful responses, all while maintaining type safety and allowing for easy provider switching.

Quick Start

Use the ax skill to create a simple generator that takes a question and returns an answer.

Frequently Asked Questions about ax

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

FAQPage Schema
How do I build type-safe signatures for LLM applications in TypeScript?▼

You can build type-safe signatures using a fluent TypeScript API to define clear input and output schemas for LLM calls. This ensures structured, maintainable, and robust interactions with various AI providers.

Can I switch between different LLM providers like OpenAI and Anthropic in the same workflow?▼

Yes, you can easily switch between different LLM providers like OpenAI, Anthropic, and Gemini. The framework supports multi-provider integration, allowing flexible model selection within your workflows.

What is the best way to orchestrate multi-step AI agents with tools?▼

Orchestrating multi-step AI agents is done by using a framework that supports building complex agents with tools and orchestrating workflows. This enables structured task execution and maintainable type safety across interactions.

Does this framework support building a customer support chatbot with a knowledge base?▼

Yes, the framework supports developing a customer support chatbot that can understand user queries, search a knowledge base, and generate helpful responses. It maintains type safety while allowing for easy provider switching.

Why do I need type-safe signatures for LLM workflow orchestration?▼

Type-safe signatures are needed for LLM workflow orchestration to provide structured, maintainable, and robust application development. They define clear input and output schemas, preventing runtime errors when integrating AI models.