ax-java-agent-optimize

A Java guide for building and improving AI agents with the dev.axllm:ax package, including tests that judge agent results.

In plain words
What is it for?
Use it when writing Java agents, evaluators, judges, optimization runs, playbooks, or BootstrapFewShot and GEPA workflows.
Why use it?
It provides the package-specific patterns and limits needed to optimize agents, evaluate failures, and save optimization outputs.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/ax-llm/ax/ax-java-agent-optimize
Any agent
npx skills add ax-llm/ax --skill ax-java-agent-optimize
Clone the repo
git clone --depth 1 https://github.com/ax-llm/ax

Made for: Claude Code, Codex.

Per session 46 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 456 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00046 $0.00456
Opus 5 $0.00023 $0.00228
Sonnet 5 $0.00009 $0.00091
Haiku 4.5 $0.00005 $0.00046

Measured yesterday against content hash 4e67e231fa19, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

ax-java-agent-optimize scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

packages/java/skills/ax-java-agent-optimize/SKILL.md · 46 lines

What it actually says

AxAgent Optimize For Java

This skill helps an agent write Java code with the generated Ax package dev.axllm:ax. Use the generated package API, examples, and manifests; do not import TypeScript-only APIs unless you are editing the TypeScript package.

When To Use

  • Optimize an AxAgent or reusable program component.
  • Mine grounded weaknesses from failed agent tasks and keep only playbook proposals that pass the verification gate.
  • Create evaluator callbacks and persist optimizer artifacts.
  • Keep optimization runs bounded by explicit budgets and dataset rows.

Package Facts

  • Language: Java.
  • Package: dev.axllm:ax.
  • Package API docs: API.md and axir-api.json.
  • Capability manifest: axir-capabilities.json.
  • Runnable examples: examples/.
  • Real network support: yes.
  • Scripted no-key transport support: yes.
  • Runtime profiles: javascript-quickjs, python-pyodide.

Core Pattern

AxGEPA engine = new AxGEPA(reflectionClient, java.util.Map.of());
var result = engine.optimize(request, evaluator);

Relevant API Surface

  • Agents And RLM: Ax.agent, AxAgent
  • Optimizers: Ax.optimize, Ax.playbook, AxPlaybook, AxBootstrapFewShot, AxGEPA, OptimizerEngine, OptimizerEvaluator

Guardrails

  • Start from package examples for exact native syntax before inventing a new call shape.
  • Use provider-api examples only when the user explicitly has provider credentials available.
  • Use no-key examples for deterministic local checks and provider request mapping.
  • Treat AxIR as the source of generated package truth: if package docs disagree with source code, update the compiler and regenerate packages.
  • Do not copy repo-maintainer skills from tools/*/skills/ into user packages.
Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday First seen · 46 lines · 46 tokens per session scan A 4e67e231fa19

Subscribe to this mod's changes

ax-java-agent-optimize is a skill published in the GitHub repository ax-llm/ax (2,888 stars, last pushed 2d ago), licensed Apache-2.0. It adds 46 tokens to every session and 456 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.