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-ainpx skills add ax-llm/ax --skill ax-java-aigit 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 | $0.00048 | $0.01736 |
| Opus 5 | $0.00024 | $0.00868 |
| Sonnet 5 | $0.00010 | $0.00347 |
| Haiku 4.5 | $0.00005 | $0.00174 |
Grade A, and why
ax-java-ai 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 today.
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.
This is a copy
88% identical to ax-cpp-ai — 20 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AxAI Providers 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
- Create provider clients or normalize provider options.
- Choose a named deployment profile separately from the model ID served by that deployment.
- Attach renewable per-request credentials for expiring cloud tokens.
- Resolve structured-output modes from the selected profile and model.
- Choose between model-list routing, ordered failover, and adaptive operational routing.
- Route multimodal requests without flattening native images when the selected provider supports them.
- Use scripted transports for deterministic no-key examples.
- Use provider-api examples only when explicit provider credentials are available.
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
import dev.axllm.ax.*;
var llm = Ax.ai("openai", java.util.Map.of("apiKey", System.getenv("OPENAI_API_KEY")));
Named Deployment Profiles
- The first
ai/NewAIfactory argument selects deployment behavior. The model option selects a model only inside that deployment; never infer request rules from a vendor-looking model ID. openaiis the official OpenAI deployment.openai-compatibleis the conservative custom-endpoint profile and requires an explicit base URL. Unknown profile names are errors.- A Together-hosted DeepSeek model uses the
togetherprofile's URL, authentication, reasoning fields, and effort mapping. Native DeepSeekthinkingfields apply only to thedeepseekprofile. - Verified DeepSeek, Grok, Groq, Cerebras, and DeepInfra model rules default an omitted thinking level to logical
max, mapped to the strongest documented deployment effort. - Send
noneonly where the selected deployment and model document reasoning disablement. Unsupported levels fail before network I/O; dynamic Hugging Face Router routes remain conservative. - Structured output is an ordered model-aware capability:
native,function, andjson_object. Exact caller model metadata overrides the first matching profile rule, which overrides the profile default. - An explicit unsupported structured-output mode fails before transport.
structuredOutputs/structured_outputsremains the compatibility alias for native JSON Schema only. - The exact Vertex
google/gemma-4-26b-a4b-it-maasrule prefersjson_object, excludes native schema, defaults thinking tomax, writes nestedenable_thinking, and extracts/replaysreasoning_content. Unknown Vertex models stay conservative. - Use named factories for Azure OpenAI, Cohere, DeepSeek, DeepSeek Responses, Mistral, Reka, Grok, routers, hosted inference, and configurable runtimes. Profile-only branded client constructors were removed.
- Retained client classes are transport/runtime boundaries: OpenAI-compatible Chat Completions, OpenAI Responses, Anthropic Messages, and Gemini GenerateContent. Build ordinary applications through the named factory.
- Provider descriptors and conformance fixtures are generated from the shared profile manifest. Do not add provider-name switches or cross-profile model normalization in a generated package.
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.
- today Changed 56eb9600ff42
- 3d ago First seen · 87 lines · 48 tokens per session scan A 7cd8ca953192
ax-java-ai is a skill published in the GitHub repository ax-llm/ax (2,890 stars, last pushed yesterday), licensed Apache-2.0. It adds 48 tokens to every session and 1,736 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to ax-cpp-ai, differing in 20 lines, and is treated as a copy.
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