Borrowing it
Nothing to install: this file belongs to dosco/aithy. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/dosco/aithy/main/.claude/skills/ax-llm/SKILL.mdgit clone --depth 1 https://github.com/dosco/aithyWrote 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/dosco/aithy/ax-llm)<a href="https://agentmods.dev/skills/dosco/aithy/ax-llm"><img src="https://agentmods.dev/badge/skills/dosco/aithy/ax-llm/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/dosco/aithy/ax-llm"><img src="https://agentmods.dev/badge/skills/dosco/aithy/ax-llm.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00068 | $0.03340 |
| Opus 5.5 | $0.00027 | $0.01336 |
| Sonnet 5.5 | $0.00014 | $0.00668 |
| Haiku 4.5 | $0.00007 | $0.00334 |
Grade A, and why
ax-llm 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 1mo ago.
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 — 354 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ax Library (@ax-llm/ax) Quick Reference
Ax is a TypeScript library for building LLM-powered applications with type-safe signatures, streaming support, and multi-provider compatibility.
Detailed skills available: ax-ai (providers, routing, adaptive balancing), ax-signature (signatures/types), ax-gen (generators), ax-agent (core agents/tools), ax-agent-rlm (agent runtime/RLM/delegation), ax-agent-observability (callbacks/logs/usage), ax-agent-memory-skills (recall and dynamic skill loading), ax-agent-optimize (agent tuning/eval), ax-flow (workflows), ax-gepa (top-level
optimize(...), BootstrapFewShot -> GEPA, Pareto optimization).
Imports & Factories
// Prefer factory functions: ax(), ai(), agent(), flow(); avoid class constructors.
import { ax, ai, f, s, fn, agent, flow, AxMemory, AxMCPClient } from '@ax-llm/ax';
import { z } from 'zod'; // optional — any Standard Schema v1 library works
// AI provider
const llm = ai({ name: 'openai', apiKey: process.env.OPENAI_APIKEY });
// Generator (from string signature)
const gen = ax('question:string -> answer:string');
// Generator (from fluent signature)
const gen = ax(
f()
.input('question', f.string('User question'))
.output('answer', f.string('AI response'))
.build()
);
// Generator (from zod — Standard Schema v1, also works with valibot/arktype)
const zodGen = ax(
f()
.input(z.object({ question: z.string().describe('User question') }))
.output(z.object({ answer: z.string().describe('AI response') }))
.build()
);
// Reusable signature
const sig = s('question:string, context:string[] -> answer:string');
// Agent
const myAgent = agent('userInput:string -> response:string', {
name: 'helper',
description: 'A helpful assistant',
});
// Flow
const wf = flow<{ input: string }, { output: string }>()
.node('step1', 'input:string -> output:string')
.execute('step1', (state) => ({ input: state.input }))
.returns((state) => ({ output: state.step1Result.output }));
// Function tool — native fluent
const tool = fn('search')
.description('Search the web')
.arg('query', f.string('Search query'))
.returns(f.string('Search results'))
.handler(({ query }) => searchWeb(query))
.build();
// Function tool — zod schema (Standard Schema v1: also works with valibot, arktype)
const zodTool = fn('calculateTax')
.description('Calculate tax for an amount')
.arg(z.object({
amount: z.number().positive().describe('Pre-tax amount in USD'),
region: z.enum(['US', 'EU', 'UK']).describe('Tax region'),
}))
.returns(z.object({ tax: z.number(), total: z.number() }))
.handler(async ({ amount }) => ({ tax: amount * 0.1, total: amount * 1.1 }))
.build();
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.
- 1mo ago Changed · +14 lines 3e5f4f04e547
- 1mo ago First seen · 340 lines · 68 tokens per session scan A b6496154160c
ax-llm is a skill published in the GitHub repository dosco/aithy (108 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 68 tokens to every session and 3,340 once invoked, about $0.0003 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 2026-08-30.
Other skills, from other repositories
ax-cpp-ai
Use when writing C++ code with axllm for named deployment profiles, generic provider clients, model selection, OpenAI-compatible calls, Responses, Gemini, Anthropic, routers, and balancers.
ax-cpp-gen
Use when writing C++ code with axllm for AxGen programs, forward calls, indexed multi-sampling, result pickers, streaming, tools, assertions, traces, usage, and output parsing.
ax-go-gen
Use when writing Go code with github.com/ax-llm/ax/packages/go for AxGen programs, forward calls, indexed multi-sampling, result pickers, streaming, tools, assertions, traces, usage, and output parsing.
ax-java-gen
Use when writing Java code with dev.axllm:ax for AxGen programs, forward calls, indexed multi-sampling, result pickers, streaming, tools, assertions, traces, usage, and output parsing.
ax-signature
This skill helps an LLM generate correct DSPy signature code using @ax-llm/ax. Use when the user asks about signatures, s(), f(), field types, string syntax, fluent builder API, validation constraints, or type-safe inputs/outputs.
ax-cpp-signature
Use when writing C++ code with axllm for string signatures, field descriptors, JSON schema output, validation, and typed tool argument shapes.