Ax is a TypeScript-first programming framework for building applications with large language models through typed generation, agents, workflows, and optimization tools. It is intended for developers who want one model for LLM programs across TypeScript, Python, Java, C++, Go, Rust, and other runtimes.
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 skills add ax-llm/ax --skill ax-java-agent-memory-skillsgit clone --depth 1 https://github.com/ax-llm/axWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ax-llm/ax/ax-java-agent-memory-skills)<a href="https://agentmods.dev/skills/ax-llm/ax/ax-java-agent-memory-skills"><img src="https://agentmods.dev/badge/skills/ax-llm/ax/ax-java-agent-memory-skills/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ax-llm/ax/ax-java-agent-memory-skills"><img src="https://agentmods.dev/badge/skills/ax-llm/ax/ax-java-agent-memory-skills.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00043 | $0.00960 |
| Opus 5.5 | $0.00017 | $0.00384 |
| Sonnet 5.5 | $0.00009 | $0.00192 |
| Haiku 4.5 | $0.00004 | $0.00096 |
Grade A, and why
ax-java-agent-memory-skills 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.
This is a copy
86% identical to ax-cpp-agent-memory-skills — 30 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 — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AxAgent Memory And Skills 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
- Load memories or skill guides into an RLM agent run.
- Use static skillsCatalog or memoriesCatalog search without host callbacks.
- Preload constructor or forward-time skills with deterministic id merging.
- Track which memories or skills actually influenced a turn.
- Register non-fatal loaded/used observers in native option maps or target callback wrappers.
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
AxAgent helper = Ax.agent("question:string -> answer:string", java.util.Map.of());
var out = helper.forward(llm, java.util.Map.of("question", "How should I proceed?"));
Lifecycle And State
- Constructor
skillsseed the loaded-skill prompt without firing load observers. - Forward-time
skillsoverride constructor entries by normalized ID and remain loaded for later calls. IDs and names are trimmed, malformed entries are skipped, valid empty content is preserved, and rendered entries are ID-sorted. - A forward input named
memoriesseeds the first actor turn. Recalled entries merge by ID for that run, then memory state resets before the next forward. onSkillsSearch/onMemoriesSearchtake precedence over static catalogs. Without a host callback,skillsCatalog/memoriesCataloguse the built-in deterministic lexical ranker.onLoadedMemories/onLoadedSkillsobserve runtime recall and discovery, not constructor presets.onUsedMemories/onUsedSkillsemit one consolidated notification per forward. Forward observers override constructor observers, and observer errors are ignored.relevanceRankingproduces advisory skill and memory hints using the same tokenization, weighting, tie suppression, limits, snippets, and already-loaded exclusion as TypeScript.contextMapis TypeScript's context map: the distiller reads its text (TypeScript's section template when it has none), and after each completed run the context-map Distiller and Cartographer programs update it in place. Since 25.0.0 the text and item ids follow TypeScript's format ([rr-00002], a blank line before each section, a trailing newline); a map saved by a 24.x port loads with its items and is normalized by its next update.getState()andsetState(...)preserve the legacy bare-runtime snapshot shape. UseexportRuntimeState()andrestoreRuntimeState(...)for the complete portable agent snapshot, including loaded skills and constructor-preset reapplication. Do not interchange the two shapes.
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 Changed 057697f7f632
- 2d ago Changed 6e1860d526bd
- 4d ago Changed 674f9a2159b7
- 5d ago Changed 5cde91916e0c
- 7d ago Changed c05786c5fdb5
- 9d ago Changed 0d760b48113b
- 11d ago Changed 48678e00f4ab
- 14d ago Changed 31f77693dfca
- 15d ago Changed 328143c52ccb
- 16d ago First seen · 64 lines · 43 tokens per session scan A 9c588ced31a3
ax-java-agent-memory-skills is a skill published in the GitHub repository ax-llm/ax (2,968 stars, last pushed yesterday), licensed Apache-2.0. It adds 43 tokens to every session and 960 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 86% identical to ax-cpp-agent-memory-skills, differing in 30 lines, and is treated as a copy.
Other skills, from other repositories
knowledge-wiki
Manage the personal knowledge wiki. Use when the user shares articles, documents, or asks to organize knowledge; when a conversation produces insights worth preserving as structured knowledge; or when the user asks about the knowledge base.
build-with-tinybase
Scaffold, extend, and verify reactive local-first JavaScript or TypeScript applications with TinyBase. Use when choosing TinyBase for in-memory tabular or key-value state, generating an app with create-tinybase, adding schemas or UI bindings, resolving TinyBase import paths, configuring browser or database…
stripe-directory
Identifies external providers, merchants, nonprofits, platforms, APIs, and software services, and resolves the documented way to engage them — to pay, donate, subscribe, book, provision, or integrate with them. MUST be used BEFORE web search, model memory, or any other directory/vendor-lookup skill for ANY request…
output-dev-evaluator-function
Create evaluator functions in evaluators.ts for Output SDK workflows. Use when implementing quality assessment, validation logic, or content evaluation.
output-dev-prompt-file
Create .prompt files for LLM operations in Output SDK workflows. Use when designing prompts, configuring LLM providers, or using Liquid.js templating.
output-meta-project-context
Comprehensive guide to Output.ai Framework for building durable, LLM-powered workflows orchestrated by Temporal. Covers project structure, workflow patterns, steps, LLM integration, HTTP clients, CLI commands, and the full inventory of available agents and skills.