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.

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Ax Signature Reference

Name: ax-signature
Rating: 64.8 (1 reviews)
Author: ax-llm

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 !

Extended String Grammar (Modifier Bags + Nested Objects)

The string form is constraint-complete: everything the fluent API expresses (except Standard Schema fields) can be written in the string. A type takes an optional comma-separated, order-free modifier bag in parentheses, and objects declare structured fields inline.

`userAge:number(min 0, max 120), contactEmail:string(format email, cache), codeSnippet:code(python)
 -> userName:string(pattern "^[a-z_]+$" "lowercase name"), tagList:string(item "a short tag")[] "all tags",
    profileList:object{ fullName:string, userAge?:number(min 0) }[] "matched profiles"`

Modifier	Applies to	Effect
`min N` / `max N`	`string`, `number`	String length bounds / numeric value bounds
`format email\|uri\|date\|date-time`	`string`	Format validation
`pattern "regex" ["desc"]`	`string`	Regex validation with optional description
`cache`	top-level input	Prefix-cache breakpoint
`item "desc"`	arrays	Per-item description: `tags:string(item "a tag")[]`
`<language>`	`code`	Language of the snippet: `snippet:code(python)`

object{ field:type, opt?:type } nests recursively; append [] for an array of objects.
Optional goes on the name (userAge?:number), never after the type.
The string API is strict: a modifier that does not apply to its type (e.g. min on a boolean) is a parse error, where the fluent API silently ignores it.
Inside object{ ... }, the ! internal marker, media types, cache, and item are rejected (they only apply at the top level).
In quoted values, backslashes are doubled — a regex \d is written pattern "\\d+".
AxSignature.toString() renders every construct back to this grammar losslessly, so a signature round-trips — this is what lets a whole flow serialize its node contracts into mermaid %%ax directives (see the ax-flow skill).

Signature Gallery

Real-world contracts, one line each — every entry below parses with s() as written (# lines are captions, not part of the signature):

# Support triage: several class outputs plus a capped reply draft
ticketText:string -> priorityClass:class "p0, p1, p2", sentimentClass:class "angry, neutral, happy", replyDraft:string(max 500)

# Invoice extraction: regex-validated id, bounded totals, structured line items
invoiceText:string -> invoiceNumber:string(pattern "^INV-\\d+$" "INV- then digits"), totalAmount:number(min 0), lineItems:object{ description:string, quantity:number(min 1), unitPrice:number }[]

# Contact enrichment: optional format-validated outputs
bioText:string -> contactEmail?:string(format email), websiteUrl?:string(format uri), birthDate?:string(format date)

# RAG: cached corpus input plus per-item described citations
corpusText:string(cache), userQuestion:string -> answerText:string, citedChunks:string(item "verbatim quote")[]

# Code generation: language-tagged code outputs
taskBrief:string -> pythonScript:code(python), testCases:code(python), riskNotes?:string

# Chain of thought: internal reasoning stripped from the result
problemText:string -> reasoning!:string, solutionText:string

# Resume parsing: nested objects inside nested arrays
resumeText:string -> candidateProfile:object{ fullName:string, yearsExperience:number(min 0), skillList:string[], education:object{ schoolName:string, degreeName?:string }[] }

# Lead scoring: signature-level description, bounded score, class next step
"Score sales leads" leadNotes:string -> fitScore:number(min 0, max 100) "0-100 fit", nextStep:class "call, email, drop"

# Multimodal: top-level image input with an optional question
productPhoto:image, question?:string -> productDescription:string, detectedObjects:string[]

# Meeting audio: audio input, capped summary, per-item action list
meetingAudio:audio -> meetingSummary:string(max 1000), actionItems:string(item "one action item")[]

# Moderation: class verdict plus structured flagged spans
postText:string -> moderationVerdict:class "allow, review, block", flaggedSpans:object{ spanText:string, reasonNote:string }[]

# Translation: optional locale input
sourceText:string, targetLocale?:string -> translatedText:string, glossaryHits:string[]

# Text-to-SQL: cached schema plus SQL-tagged output
schemaText:string(cache), questionText:string -> sqlQuery:code(sql), queryNotes?:string(max 200)

# Calendar extraction: datetime fields and an optional end
emailText:string -> eventTitle:string, startsAt:datetime, endsAt?:datetime, attendeeNames:string[]

# Booking window: date range, bounded party size, and flexibility flag
requestText:string -> stayWindow:dateRange, partySize:number(min 1, max 12), flexibleDates:boolean

# Contract dates: date fields plus bounded notice period
contractText:string -> effectiveDate:date, expiryDate?:date, autoRenews:boolean, noticeDays?:number(min 0)

# Link audit: URL arrays and an optional primary URL
pageText:string -> referencedUrls:url[], primaryUrl?:url

# Config generation: JSON output plus per-item warnings
requirementsText:string -> serviceConfig:json, setupWarnings:string(item "one warning")[]

# Claims gate: cached policy, bounded confidence, and optional citation
claimText:string, policyText:string(cache) -> isCovered:boolean, confidenceScore:number(min 0, max 1), citedClause?:string

# Earnings extraction: structured period data plus a class outlook
filingText:string(cache) -> revenueByPeriod:object{ periodLabel:string, amountUsd:number }[], guidanceTone:class "raise, hold, cut"

# Pull request review: diff code, cached guide, structured comments, and verdict
diffText:code(diff), styleGuide?:string(cache) -> reviewComments:object{ filePath:string, lineNumber:number(min 1), commentText:string(max 300) }[], overallVerdict:class "approve, revise"

# Incident triage: severity class, optional service, and per-item runbook steps
alertLog:string -> incidentSeverity:class "sev1, sev2, sev3", suspectedService?:string, runbookSteps:string(item "one step")[]

# Product listing: image and file inputs with constrained listing outputs
productPhoto:image, priceSheet?:file -> listingTitle:string(max 80), bulletPoints:string(item "one selling point")[], priceUsd?:number(min 0)

# Study cards: nested object array with an optional difficulty tag
chapterText:string -> flashCards:object{ questionText:string, answerText:string, difficultyTag?:string }[]

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('Question to answer'),
    }),
    { fields: { contextData: { cache: true } } }  // companion options map
  )
  .output(
    z.object({
      reasoning: z.string().describe('Step-by-step thinking'),
      answer: z.string().describe('Final answer'),
    }),
    { fields: { reasoning: { internal: true } } }
  )
  .build();

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')
  .description('Search indexed docs')
  .arg('query', z.string().min(1), { cache: true })
  .arg('limit', z.number().int().positive().optional())
  .returnsField('results', z.array(z.string()))
  .handler(async ({ query }) => [])
  .build();

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[]'

// Constrained string form (no fluent builder needed)
'reviewText:string(max 2000) -> rating:number(min 1, max 5), themes:string(item "a theme")[]'

// Nested object output in the string form
'profileText:string -> profile:object{ fullName:string, age?:number(min 0) }'

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

Critical Rules

The string form is constraint-complete: reach for modifier bags (string(max 500), number(min 0, max 10), string(format email)) and inline object{ ... } before switching to fluent/zod just for constraints. Reserve fluent/Standard Schema for zod/valibot-backed fields.
The string API is strict — a modifier that does not apply to its type is a parse error (the fluent API silently ignores it).
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

Repository: ax-llm/ax
Path: website/static/typescript/.well-known/agent-skills/ax-signature/SKILL.md
Commit: 7911e27

Last updated: 5 days ago
First committed: 13 days ago

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