ax-signature

name	ax-signature
description	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.
version	23.0.0

Ax Signature Reference

Signature Syntax

[description] input1:type, input2:type -> output1:type, output2:type

Field Types

Type	Syntax	TypeScript	Example
String	:string	string	userName:string
Number	:number	number	score:number
Boolean	:boolean	boolean	isValid:boolean
JSON	:json	any	metadata:json
Date	:date	Date	birthDate:date
DateTime	:datetime	Date	timestamp:datetime
DateRange	:dateRange	{ start: Date; end: Date }	travelDates:dateRange
DateTimeRange	:datetimeRange	{ start: Date; end: Date }	meetingWindow:datetimeRange
Image	:image	{mimeType, data}	photo:image (input only)
Audio	:audio	input: AxAudioInput; output: AxChatAudioOutput	recording:audio, speech:audio
File	:file	{mimeType, data}	document:file (input only)
URL	:url	string	website:url
Code	:code	string	pythonScript:code
Class	:class "a, b, c"	"a" | "b" | "c"	mood:class "happy, sad"

Date, datetime, and range fields are AI-friendly but strict. They accept ISO-style values, trim minor whitespace/casing issues, and parse ranges as { "start": "...", "end": "..." }, [start, end], start/end, or natural delimiters like start to end; invalid values and reversed ranges should fail validation rather than being silently autocorrected.

Arrays, Optional, and Internal Fields

'tags:string[] -> processedTags:string[]'  // arrays
'query:string, context?:string -> response:string'  // optional with ?
'problem:string -> reasoning!:string, solution:string'  // internal with !

Four Ways to Create Signatures

1. String-Based (Recommended for simple cases)

import { ax, s } from '@ax-llm/ax';
const gen = ax('input:string -> output:string');
const sig = s('query:string -> response:string');

2. Pure Fluent Builder API

import { f } from '@ax-llm/ax';
const sig = f()
  .input('userMessage', f.string('User input'))
  .input('contextData', f.string('Additional context').optional())
  .input('tags', f.string('Keywords').array())
  .output('responseText', f.string('AI response'))
  .output('confidenceScore', f.number('Confidence 0-1'))
  .output('debugInfo', f.string('Debug info').internal())
  .build();

3. Standard Schema (zod / valibot / arktype)

.input() and .output() accept any Standard Schema v1 compatible library — no wrapper, no adapter. Three shapes work everywhere:

import { z } from 'zod';
import { f } from '@ax-llm/ax';

// Shape A: per-field schema — name first, then the schema, then optional ax hints
const sig = f()
  .input('contextData', z.string().describe('Background context'), { cache: true })
  .input('userQuestion', z.string().describe('Question to answer'))
  .output('reasoning', z.string().describe('Step-by-step thinking'), { internal: true })
  .output('answer', z.string().describe('Final answer'))
  .build();

// Shape B: whole-object schema — decomposed into fields in declaration order
const sig2 = f()
  .description('Answer questions from retrieved context')
  .input(
    z.object({
      contextData: z.string().describe('Background context'),
      userQuestion: z.string().describe(),
    }),
    { : { : { :  } } }  
  )
  .(
    z.({
      : z.().(),
      : z.().(),
    }),
    { : { : { :  } } }
  )
  .();

Validation constraints from zod flow into ax's prompt validation:

// String constraints: .email(), .url(), .min(), .max(), .regex()
// Number constraints: .min(), .max()
// Arrays: z.array(z.string())
// Enums: z.enum([...])  — NOTE: enum maps to ax class type, output fields only
const sig3 = f()
  .input(z.object({
    emailAddress: z.string().email().describe('Contact email'),
    username: z.string().min(3).max(20).describe('Handle'),
    score: z.number().min(0).max(100).describe('Numeric score'),
  }))
  .output(z.object({
    priority: z.enum(['low', 'medium', 'high']).describe('Priority'),
    summary: z.string().describe('Result'),
  }))
  .build();

Companion options (AxFieldOptions) carry ax-specific hints that schema libraries don't represent:

Option	Effect
{ cache: true }	Mark input field as a prefix-cache breakpoint
{ internal: true }	Mark output field as internal scratchpad (stripped from result)

The same Standard Schema shapes work on fn() tools via .arg(), .returns(), and .returnsField() — argument types are inferred from the schema:

import { z } from 'zod';
import { fn } from '@ax-llm/ax';

// Whole-object zod on a tool — AI-SDK-style
const lookupProduct = fn('lookupProduct')
  .description('Look up a product by name and return its current details')
  .arg(
    z.object({
      productName: z.string().min(1).describe('Exact product name'),
      includeSpecs: z.boolean().optional(),
    })
  )
  .returns(
    z.object({
      price: z.number(),
      inStock: z.boolean(),
      rating: z.number().min(1).max(5),
    })
  )
  .handler(async ({ productName, includeSpecs }) => ({
    price: 79.99,
    inStock: true,
    rating: 4.3,
  }))
  .build();

// Per-argument form — mix with f.*() args, attach ax hints
const searchDocs = fn('searchDocs')
  .()
  .(, z.().(), { :  })
  .(, z.().().().())
  .(, z.(z.()))
  .( ({ query }) => [])
  .();

4. Hybrid

import { s, f } from '@ax-llm/ax';
const sig = s('base:string -> result:string')
  .appendInputField('extra', f.json('Metadata').optional())
  .appendOutputField('score', f.number('Quality score'));

Fluent API Reference

Type creators:

• f.string(desc), f.number(desc), f.boolean(desc), f.json(desc)
• f.image(desc), f.audio(desc), f.file(desc), f.url(desc)
• f.email(desc), f.date(desc), f.datetime(desc), f.dateRange(desc), f.datetimeRange(desc)
• f.class(['a','b','c'], desc), f.code(desc)
• f.object({ field: f.string() }, desc)

Chainable modifiers (method chaining only, no nesting):

• .optional() - make field optional
• .array() / .array('list description') - make field an array
• .internal() - output only, hidden from final output
• .cache() - input only, mark for prompt caching

// Correct: pure fluent chaining
f.string('description').optional().array()
f.string('context').cache().optional()
f.object({ field: f.string() }, 'item desc').array('list desc')

// Wrong: nested function calls (removed)
f.array(f.string('description'))      // REMOVED
f.optional(f.string('description'))   // REMOVED
f.internal(f.string('description'))   // REMOVED

Validation Constraints

String Constraints

f.string('username').min(3).max(20)
f.string('email').email()
f.string('website').url()
f.string('birthDate').date()
f.string('timestamp').datetime()
f.string('pattern').regex('^[A-Z0-9]')

Number Constraints

f.number('age').min(18).max(120)
f.number('score').min(0).max(100)

Complete Validation Example

const sig = f()
  .input('formData', f.string('Raw form data'))
  .output('user', f.object({
    username: f.string('Username').min(3).max(20),
    email: f.string('Email').email(),
    age: f.number('Age').min(18).max(120),
    bio: f.string('Bio').max(500).optional(),
    website: f.string('Website').url().optional(),
    tags: f.string('Tag').min(2).max(30).array()
  }, 'User profile'))
  .build();

Cached Input Fields

const sig = f()
  .input('staticContext', f.string('Context').cache())
  .input('userQuery', f.string('Dynamic query'))
  .output('answer', f.string('Response'))
  .build();

Field Naming Rules

Good: userQuestion, customerEmail, analysisResult, confidenceScore Bad: text, data, input, output, a, x, val (too generic), 1field (starts with number)

Media Type Restrictions

• Image and file fields are top-level input fields only.
• Audio fields can be top-level inputs or single top-level outputs.
• Audio output fields are scripted speech artifacts: the model returns plain text, then Ax synthesizes AxChatAudioOutput.
• Media fields cannot be nested in objects.
• Media arrays are supported for inputs only; output audio[] is not supported.

Common Patterns

// Chain of Thought
'problem:string -> reasoning!:string, solution:string'

// Classification
'email:string -> priority:class "urgent, normal, low"'

// Multi-modal input
'imageData:image, question?:string -> description:string, objects:string[]'

// Scripted speech output
'question:string -> speech:audio, summary:string'

// Data Extraction
'invoiceText:string -> invoiceNumber:string, totalAmount:number, lineItems:json[]'

// With description
'"Answer TypeScript questions" question:string -> answer:string, confidence:number'

Critical Rules

• Use f() fluent builder, NOT nested f.array(f.string()) -- those are removed.
• Field names must be descriptive (not generic like text, data, input).
• Image/file media types are input-only, top-level only; audio may also be a single top-level output.
• .internal() / { internal: true } is output-only (for chain-of-thought reasoning).
• .cache() / { cache: true } is input-only (for prompt caching).
• Validation errors trigger auto-retry with correction feedback.
• f.email(), f.url(), f.date(), f.datetime() are shorthand for f.string().email() etc.; f.dateRange() and f.datetimeRange() return { start: Date; end: Date }.
• z.enum() maps to ax's class type — only valid on output fields.
• For multimodal inputs (images, audio, files) and scripted audio outputs, use f.image() / f.audio() / f.file() — zod has no equivalent.

Examples

Fetch these for full working code:

• Standard Schema (zod) — zod with f() and fn(), all three shapes
• Fluent Signature — native fluent f() API
• Structured Output — structured output with validation
• Debug Schema — JSON schema validation