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.svg" alt="Measured on agentmods" 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 | $0.00100 | $0.10292 |
| Opus 5 | $0.00050 | $0.05146 |
| Sonnet 5 | $0.00020 | $0.02058 |
| Haiku 4.5 | $0.00010 | $0.01029 |
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 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.
How it starts
The opening of the file, as written. The whole thing — 880 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.
Renewable Credentials
Use credentialProvider for expiring bearer tokens. The callback runs for each
request attempt and receives { profile, operation, method, url }. Fresh
headers override static authentication headers.
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 · +11 lines 668965c08338
- yesterday Changed · +110 lines · +2 tokens per session 4ce6bbf6400d
- 5d ago First seen · 759 lines · 98 tokens per session scan A 33250cc27eca
ax-ai is a skill published in the GitHub repository ax-llm/ax (2,893 stars, last pushed today), licensed Apache-2.0. It adds 100 tokens to every session and 10,292 once invoked, about $0.0005 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.