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-gengit 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-gen)<a href="https://agentmods.dev/skills/ax-llm/ax/ax-gen"><img src="https://agentmods.dev/badge/skills/ax-llm/ax/ax-gen.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00086 | $0.05445 |
| Opus 5 | $0.00043 | $0.02722 |
| Sonnet 5 | $0.00017 | $0.01089 |
| Haiku 4.5 | $0.00009 | $0.00545 |
Grade A, and why
ax-gen 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.
How it starts
The opening of the file, as written. The whole thing — 578 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AxGen Codegen Rules (@ax-llm/ax)
Use this skill to generate AxGen code. Prefer short, modern, copyable patterns. Do not write tutorial prose unless the user explicitly asks for explanation.
Use the ax-mcp skill when AxGen attaches native MCP clients or consumes MCP
prompts, resources, tools, tasks, subscriptions, authentication, or events.
Use These Defaults
- Use
ax(...)factory, notnew AxGen(...). - Always pass an AI instance from
ai(...)as the first argument toforward(). - Streaming uses
streamingForward(), notforward()with a stream option. - Use schema validation for field shape and constraints.
- Use
addAssert(...)for whole-output hard invariants with correction retries. - Use
addStreamingAssert(...)for partial streaming hard invariants with fail-fast per-attempt correction retries. - Use
bestOfN(...)/refine(...)for reward-scored complete outputs. - Step hook mutations are applied at the next step boundary (pending pattern).
stopFunctionaccepts a string or string[] for multiple stop functions.- Multi-step continues until: all outputs filled, stop function called, or
maxStepsreached.
Canonical Pattern
import { ai, ax, s } from '@ax-llm/ax';
const llm = ai({
name: 'openai',
apiKey: process.env.OPENAI_APIKEY!,
});
// Inline signature
const gen = ax('input:string -> output:string, reasoning:string');
// Reusable signature
const sig = s('question:string, context:string[] -> answer:string');
const gen2 = ax(sig);
// With options
const gen3 = ax('input -> output', {
description: 'A helpful assistant',
maxRetries: 3,
maxSteps: 10,
temperature: 0.7,
});
const result = await gen.forward(llm, { input: 'Hello world' });
console.log(result.output);
Signatures from zod / valibot / arktype
ax() accepts any signature built with f(), and f().input() / .output() accept Standard Schema v1 validators directly — per-field or a whole z.object({...}):
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 · +14 lines 94a185ab4e54
- 4d ago First seen · 564 lines · 86 tokens per session scan A b1dfd2fbbcb2
ax-gen is a skill published in the GitHub repository ax-llm/ax (2,893 stars, last pushed today), licensed Apache-2.0. It adds 86 tokens to every session and 5,445 once invoked, about $0.0004 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 2026-09-03.
Other skills, from other repositories
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.
update-provider-models
Add new or remove obsolete model IDs for existing AI SDK providers. Use when adding a model to a provider, removing an obsolete model, or processing a list of model changes from an issue. Triggers on "add model", "remove model", "new model ID", "obsolete model", "update model IDs".
output-dev-eval-testing
Create offline evaluation tests for Output SDK workflows using @outputai/evals. Use when implementing test evaluators with verify(), creating dataset YAML files, building eval workflows, or running workflow tests via CLI.
output-dev-step-function
Create step functions in steps.ts for Output SDK workflows. Use when implementing I/O operations, error handling, HTTP requests, or LLM calls.
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.
output-eval-dataset-design
Design diverse eval datasets using dimension-based variation. Use when bootstrapping eval datasets, when real traces are sparse, or when existing datasets miss edge cases.