ax-java-playbook

Build and refine Java playbooks for Ax programs and agent stages.

2.9k|186|Updated Feb 23, 2023

Search Tags:#playbook #llm #agents #java #context-engineering #ax #optimizers

Ready to run

npx skills add https://github.com/ax-llm/ax --skill ax-java-playbook

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ax-java-playbook
Source: https://github.com/ax-llm/ax/tree/main/website/static/java/.well-known/agent-skills/ax-java-playbook
Command: npx skills add https://github.com/ax-llm/ax --skill ax-java-playbook

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you build, refine, and render Java playbooks for Ax programs and agent stages when you need an evolving context layer that can learn from examples and live feedback.

Core Features & Use Cases

Playbook Creation: Create a playbook around a Java Ax program so an agent can accumulate task knowledge over time.
Online and Offline Refinement: Improve a playbook from live feedback or from labeled examples during evaluation.
Render and Persist: Convert the playbook into a usable program context or store it for later reuse.
Use Case: Use it when you need a Java agent that adapts its instructions after reviewing past runs, then emits a clean playbook artifact for deployment.

Quick Start

Ask the assistant to write Java code that creates an Ax playbook, evolves it from examples, and renders it into the target program context.

Frequently Asked Questions about ax-java-playbook

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

FAQPage Schema

How do I create a Java playbook for Ax agents that learns from examples?▼

To create a Java playbook for Ax agents, you build a playbook around an Ax program and use example-driven evolution to refine context. This allows the agent to accumulate task knowledge from labeled examples over time.

Can I refine agent context playbooks using live feedback in Java?▼

Yes, you can refine agent context playbooks in Java using both online live feedback and offline labeled examples. The Skill supports evolving context through optimizer integration during evaluation runs.

What is context engineering for Java Ax programs and when do I need it?▼

Context engineering for Java Ax programs is the process of building an evolving instruction layer for agents. You need it when your Java agent must adapt its behavior after reviewing past runs and emit a clean playbook artifact.

How do I render a playbook into a program context in Java?▼

You render a playbook into a Java program context by applying the generated package API. This converts the evolved playbook rules into a usable context layer or persists it for later reuse in deployment.

Do I need optimizer support to evolve Java playbooks for Ax agents?▼

Yes, optimizer support is required to evolve Java playbooks for Ax agents. The Skill uses optimizers alongside package manifests and labeled examples as the source of truth for refining the context layer.

What are the limitations of evolving Java playbooks from labeled examples?▼

Evolving Java playbooks from labeled examples requires using the generated package API and package manifests as the source of truth. Without these structured inputs and optimizer support, the context refinement process cannot function properly.