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.
npx agentmods add skills/ax-llm/ax/ax-java-agent-optimizenpx skills add ax-llm/ax --skill ax-java-agent-optimizegit clone --depth 1 https://github.com/ax-llm/axWhat 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.
| Model | Per session | Once 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 |
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.
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.mdandaxir-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-apiexamples only when the user explicitly has provider credentials available. - Use
no-keyexamples 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.
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.
- yesterday First seen · 46 lines · 46 tokens per session scan A 4e67e231fa19
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.
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