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-flowgit 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-flow)<a href="https://agentmods.dev/skills/ax-llm/ax/ax-flow"><img src="https://agentmods.dev/badge/skills/ax-llm/ax/ax-flow.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 | $0.00059 | $0.06979 |
| Opus 5 | $0.00030 | $0.03490 |
| Sonnet 5 | $0.00012 | $0.01396 |
| Haiku 4.5 | $0.00006 | $0.00698 |
Grade A, and why
ax-flow 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 — 707 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AxFlow Codegen Rules (@ax-llm/ax)
Use this skill to generate AxFlow workflow code. Prefer short, modern, copyable patterns. Do not write tutorial prose unless the user explicitly asks for explanation.
Use These Defaults
- Use
flow()factory, notnew AxFlow(). - Import:
import { ai, flow, f } from '@ax-llm/ax'; autoParallel: trueis the default; independent executes and derives run in parallel when their metadata reads/writes are known and non-conflicting.- Node results are stored as
${nodeName}Resultin state. - Always define
.node()before.execute()for that node. - Use
.returns()(or.r()) as the last step to lock the output type. - Use descriptive node names:
documentSummarizer, notproc1. - Use descriptive field names:
userInput,responseText, nottext,result.
Critical Rules
- Use
flow()factory syntax for new code. - Node results in state follow the pattern
state.${nodeName}Result.${fieldName}. .execute()maps current state to node inputs;.map()transforms state without AI calls..returns()maps final state to the flow output type.- Always define nodes before executing them; reversed order throws at runtime.
- Keep state flat; avoid deep nesting in
.map(). - Ensure loop conditions can change to avoid infinite loops.
- Structure independent executes to maximize safe auto-parallelization.
- Use
flow<InputType, OutputType>()for typed flows. - Aliases:
.n()=.node(),.nx()=.nodeExtended(),.m()=.map(),.r()=.returns().
Canonical Pattern
import { ai, flow } from '@ax-llm/ax';
const llm = ai({ name: 'openai', apiKey: process.env.OPENAI_APIKEY! });
const wf = flow<{ userInput: string }, { responseText: string }>()
.node('testNode', 'userInput:string -> responseText:string')
.execute('testNode', (state) => ({ userInput: state.userInput }))
.returns((state) => ({ responseText: state.testNodeResult.responseText }));
const result = await wf.forward(llm, { userInput: 'Hello world' });
console.log(result.responseText);
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 · +20 lines a3e8ac3c5792
- 4d ago First seen · 687 lines · 59 tokens per session scan A 674be774b2d8
ax-flow is a skill published in the GitHub repository ax-llm/ax (2,893 stars, last pushed today), licensed Apache-2.0. It adds 59 tokens to every session and 6,979 once invoked, about $0.0003 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.
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.
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.