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ax-python-refine

Build Python refinement workflows for axllm with evaluator scoring and optimizer APIs.

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

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you build and improve Python refinement workflows for axllm when you need candidate generation, evaluator scoring, iterative improvement, and optimizer-backed selection.

Core Features & Use Cases

  • Reward-scored generation: Structure Python code so candidate outputs can be scored and ranked by an evaluator.
  • Iterative improvement loops: Apply feedback-driven refinement when outputs need multiple passes to reach the desired quality.
  • Optimizer-backed workflows: Use AxGEPA and related optimizer APIs when a standalone refine helper is not available.
  • Use Case: A developer can use this Skill to turn a rough Python generation flow into a feedback-aware pipeline that produces better candidates over time.

Quick Start

Use the ax-python-refine skill to rewrite a Python refinement pipeline for axllm that scores candidates, incorporates evaluator feedback, and applies optimizer-backed improvements.

Frequently Asked Questions about ax-python-refine

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

FAQPage Schema
How do I build a feedback loop for Python code refinement with axllm?▼

To build a Python refinement loop with axllm, structure your code to generate candidates, score them with an evaluator, and apply iterative feedback. This pipeline improves output quality over multiple passes using optimizer-backed selection.

What is reward-scored generation in axllm workflows?▼

Reward-scored generation in axllm workflows is the process of structuring Python code so candidate outputs can be scored and ranked by an evaluator. This mechanism allows developers to systematically select the highest-quality generated text.

Can I use optimizer APIs for iterative candidate improvement in axllm?▼

Yes, you can use AxGEPA and related optimizer APIs for iterative candidate improvement in axllm. These optimizer-backed workflows provide refinement capabilities when a standalone refine helper is not available.

Do I need native axllm APIs to create evaluator feedback loops?▼

Yes, creating evaluator feedback loops requires native axllm APIs, optimizer artifacts, and no-key local transport examples. You also need AxIR-aligned package documentation to ensure correct syntax and deterministic refinement behavior.

What's the best way to apply feedback-driven refinement to generated Python outputs?▼

The best way to apply feedback-driven refinement is to rewrite a rough Python generation flow into a feedback-aware pipeline. This involves scoring candidates, incorporating evaluator feedback, and applying optimizer-backed improvements across multiple passes.

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