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ax-python-playbook

Create Python playbooks with axllm for evolving agent context.

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

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Helps developers create, refine, and deploy evolving context playbooks for Python programs and agents built with axllm, while avoiding TypeScript-only APIs.

Core Features & Use Cases

  • Playbook creation: Define a program and attach a playbook workflow for context engineering in Python.
  • Online and offline refinement: Evolve task knowledge from live feedback or labeled examples to improve behavior over time.
  • Rendering and persistence: Render a playbook and inject it into a program context for execution or reuse.
  • Use case: Build an adaptive agent that learns from examples, updates its playbook, and ships as part of a generated Python package.

Quick Start

Ask for a Python example that uses axllm playbook to evolve an agent from labeled examples and then render the resulting playbook into the program context.

Frequently Asked Questions about ax-python-playbook

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

FAQPage Schema
How do I create a Python playbook for evolving agent context?▼

To create a Python playbook for evolving agent context, define a program and attach a playbook workflow using the axllm package API. This enables iterative task knowledge growth and context engineering for Python agents.

Can I refine agent context engineering playbooks using offline examples in Python?▼

Yes, you can refine context engineering playbooks using offline example-driven tuning in Python. The axllm package supports offline labeled examples to evolve task knowledge and adjust agent behavior over time.

What is the best way to render a playbook into a Python program context for execution?▼

The best way to render a playbook into a Python program context is by applying the axllm rendering and persistence features. This injects the evolved playbook directly into the program context for deterministic or live execution.

Does the axllm playbook workflow support online feedback refinement for Python agents?▼

Yes, the axllm playbook workflow supports online feedback refinement for Python agents. It applies live feedback to continuously refine and evolve the agent's task knowledge during runtime execution.

Do I need specific runtime profiles to deploy Python playbooks with axllm?▼

Yes, you need specific runtime profiles to deploy Python playbooks with axllm. These profiles provide the necessary guidance for deterministic or live execution alongside the generated axllm package API and optimizer support.

Why should I use axllm for context engineering instead of TypeScript-only APIs?▼

You should use axllm for context engineering to avoid TypeScript-only APIs and build native Python programs. It provides integrated playbook creation, refinement, and deployment specifically designed for Python agents.

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