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ax-python-llm

Generate Python code for the axllm package using native signatures and entrypoints.

2.9k|186|Updated Feb 23, 2023
Search Tags:#python#llm#agents#flows#optimizers#axllm
One-click install
npx skills add https://github.com/ax-llm/ax --skill ax-python-llm
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ax-python-llm
Source: https://github.com/ax-llm/ax/tree/main/website/static/python/.well-known/agent-skills/ax-python-llm
Command: npx skills add https://github.com/ax-llm/ax --skill ax-python-llm

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you write correct Python code for the generated Ax package axllm without mixing in TypeScript-only APIs or guessing the native call shape.

Core Features & Use Cases

  • Native package guidance for signatures, providers, agents, flows, and optimizers.
  • Helps translate package docs, examples, and manifests into Python code that matches the generated Ax runtime.
  • Useful when you need a fast starting point for an OpenAI client, a structured generator, or an end-to-end agent flow built for Python.

Quick Start

Ask for a Python axllm example that creates a client from an environment variable and uses the package's native entrypoint for your chosen provider or workflow.

Frequently Asked Questions about ax-python-llm

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

FAQPage Schema
How do I write Python code for the axllm package without using TypeScript APIs?▼

You can build an axllm Python client by using the package's native entrypoints for your chosen provider. Ask for a Python example that creates a client from an environment variable to quickly start generating structured outputs.

What components are available when creating axllm agents and flows in Python?▼

When creating axllm agents and flows in Python, available components include native signatures, providers, agents, flows, and optimizers. These map directly to the generated AxIR, ensuring your translated programs run correctly.

Why does my axllm Python code fail when calling translated Ax programs?▼

Your axllm Python code may fail if it relies on TypeScript-only APIs or guesses native call shapes. Correct translated Ax programs require referencing API.md, axir-api.json, and capability manifests to match the generated AxIR.

Do I need capability manifests to generate axllm Python examples?▼

Yes, you need capability manifests, API.md, and axir-api.json to generate runnable axllm Python examples. These inputs keep the generated code aligned with the package-specific entrypoints and the generated AxIR structure.

What is the best way to structure an end-to-end agent flow using axllm in Python?▼

The best way to structure an end-to-end agent flow using axllm in Python is to use native package guidance for agents, flows, and optimizers. This approach matches the generated Ax runtime instead of mixing in incompatible TypeScript-only APIs.

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