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-aigit 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-ai)<a href="https://agentmods.dev/skills/ax-llm/ax/ax-ai"><img src="https://agentmods.dev/badge/skills/ax-llm/ax/ax-ai/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-ai"><img src="https://agentmods.dev/badge/skills/ax-llm/ax/ax-ai.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Privilege Escalation · line 588 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
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 | $0.00105 | $0.15800 |
| Opus 5.5 | $0.00042 | $0.06320 |
| Sonnet 5.5 | $0.00021 | $0.03160 |
| Haiku 4.5 | $0.00011 | $0.01580 |
Grade A, and why
ax-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 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 — 1,257 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Provider Codegen Rules (@ax-llm/ax)
Use this skill to generate AI provider setup, configuration, and chat code. Prefer short, modern, copyable patterns. Do not write tutorial prose unless the user explicitly asks for explanation.
Quick Setup
import { ai } from '@ax-llm/ax';
const openai = ai({ name: 'openai', apiKey: 'sk-...' });
const claude = ai({ name: 'anthropic', apiKey: 'sk-ant-...' });
const gemini = ai({ name: 'google-gemini', apiKey: 'AIza...' });
const azure = ai({ name: 'azure-openai', apiKey: 'your-key', resourceName: 'your-resource', deploymentName: 'gpt-5-4-mini' });
const deepseek = ai({ name: 'deepseek', apiKey: 'sk-...' });
const mistral = ai({ name: 'mistral', apiKey: 'your-key' });
const cohere = ai({ name: 'cohere', apiKey: 'your-key' });
const custom = ai({
name: 'openai-compatible',
apiKey: process.env.PROVIDER_API_KEY,
apiURL: 'https://example.com/v1',
config: { model: 'provider/model-name' },
});
const reka = ai({ name: 'reka', apiKey: 'your-key' });
const grok = ai({ name: 'grok', apiKey: 'your-key' });
name selects a deployment profile; config.model selects a model inside that
deployment. Never infer provider behavior from a model ID. For example,
name: 'together' with a deepseek-ai/... model uses Together's profile rules,
not DeepSeek's native request shape.
Use axAIProfiles() to discover the complete named catalog and
axGetAIProfile(name) to inspect endpoint requirements, authentication,
operations, capabilities, model rules, sources, and review dates. Use
name: 'openai-compatible' plus apiURL for an unlisted custom endpoint;
unknown names are errors.
Profile-only branded classes were removed in the major-version migration. Use
ai({ name: ... }) for Azure OpenAI, Cohere, DeepSeek, DeepSeek Responses,
Mistral, Reka, and Grok. Retained low-level classes represent genuine
transports/runtimes only; legacy model enum and catalog exports remain usable.
Typesafe / Jev (TypeScript)
Use the ax-typesafe skill for Jev question design, rich criteria, native scoring, transport settings, and hybrid examples. The two interfaces serve different output contracts:
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · +5 lines 986514e4fb0a
- 2d ago Changed 12025aa29b3c
- 4d ago Changed · +6 lines 9b64b7882c15
- 5d ago Changed bbb822f4a66f
- 7d ago Changed ff91eb86d4f6
- 9d ago Changed · +30 lines 07e0f1c68d67
- 11d ago Changed · +17 lines 8d717d166c05
- 14d ago Changed · +12 lines 2d303c783561
- 15d ago Changed · +86 lines fa75f0c35c22
- 16d ago First seen · 1,101 lines · 105 tokens per session scan A 39bb663a81e7
ax-ai is a skill published in the GitHub repository ax-llm/ax (2,968 stars, last pushed 2d ago), licensed Apache-2.0. It adds 105 tokens to every session and 15,800 once invoked, about $0.0004 per session on Opus 5.5. 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.
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-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.
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-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.