Output Dev Types File

Creating types.ts Files with Zod Schemas

Overview

This skill documents how to create types.ts files for Output SDK workflows. These files contain Zod schemas for input/output validation and their corresponding TypeScript types.

When to Use This Skill

• Creating a new workflow's type definitions
• Adding new schemas for steps
• Fixing schema validation errors
• Refactoring existing type definitions

Critical Import Rule

ALWAYS import z from @output.ai/core, NEVER from zod directly:

// CORRECT
import { z } from '@output.ai/core';

// WRONG - will cause runtime errors
import { z } from 'zod';

Related Skill: output-error-zod-import for troubleshooting import issues

Basic Structure

import { z } from '@output.ai/core';

// 1. Workflow Input Schema
export const WorkflowInputSchema = z.object({
  // Define input fields
});

// 2. Workflow Output Type
export type WorkflowInput = z.infer<typeof WorkflowInputSchema>;
export type WorkflowOutput = /* output type */;

// 3. Step Schemas (for each step)
export const StepNameInputSchema = z.object({
  // Step input fields
});

export const StepNameOutputSchema = z.object({
  // Step output fields
});

// 4. Type Exports
export type StepNameInput = z.infer<typeof StepNameInputSchema>;
export type StepNameOutput = z.infer<typeof StepNameOutputSchema>;

Common Schema Patterns

Basic Types

import { z } from '@output.ai/core';

// Strings
const stringField = z.string();
const optionalString = z.string().optional();
const stringWithDefault = z.string().default('default value');
const describedString = z.string().describe('Field description');

// Numbers
const numberField = z.number();
const integerField = z.number().int();
const rangedNumber = z.number().min(1).max(100);

// Booleans
const booleanField = z.boolean();
const defaultBoolean = z.boolean().default(false);

// Enums
const enumField = z.enum(['option1', 'option2', 'option3']);
const enumWithDefault = z.enum(['small', 'medium', 'large']).default('medium');

Complex Types

import { z } from '@output.ai/core';

// Arrays
const stringArray = z.array(z.string());
const objectArray = z.array(z.object({ id: z.string(), name: z.string() }));

// Objects
const nestedObject = z.object({
  user: z.object({
    id: z.string(),
    email: z.string().email()
  }),
  settings: z.object({
    notifications: z.boolean()
  })
});

// Union Types
const flexibleInput = z.union([
  z.string(),
  z.array(z.string())
]);

// Records
const keyValueMap = z.record(z.string(), z.number());

Validation Patterns

import { z } from '@output.ai/core';

// String Validations
const emailField = z.string().email();
const urlField = z.string().url();
const uuidField = z.string().uuid();
const minLengthString = z.string().min(1);
const maxLengthString = z.string().max(1000);

// Number Validations
const positiveNumber = z.number().positive();
const nonNegativeNumber = z.number().nonnegative();
const percentageNumber = z.number().min(0).max(100);

// Array Validations
const nonEmptyArray = z.array(z.string()).min(1);
const limitedArray = z.array(z.string()).max(10);

Complete Example

Based on a real workflow (image_infographic_nano):

import { z } from '@output.ai/core';

// ============================================
// Workflow Schemas
// ============================================

export const WorkflowInputSchema = z.object({
  content: z.string().describe('Text content to generate image ideas from'),
  mode: z.enum(['infographic']).default('infographic').describe('Type of image to generate'),
  colorPalette: z.string().optional().describe('Color palette preference for the images'),
  artDirection: z.string().optional().describe('Art direction or style preference'),
  numberOfIdeas: z.number().min(1).max(10).default(1).describe('Number of image concepts to generate'),
  referenceImageUrls: z.union([
    z.string(),
    z.array(z.string())
  ]).optional().describe('Reference image URLs for style guidance (max 14)'),
  aspectRatio: z.enum(['1:1', '16:9', '9:16', '4:3', '3:4']).default('1:1').describe('Aspect ratio for generated images'),
  resolution: z.enum(['1K', '2K', '4K']).default('1K').describe('Resolution for generated images'),
  numberOfGenerations: z.number().min(1).max(10).default(1).describe('Number of images to generate per concept'),
  storageNamespace: z.string().optional().describe('S3 folder path for storing images')
});

export type WorkflowInput = z.infer<typeof WorkflowInputSchema>;
export type WorkflowOutput = string[];

// ============================================
// Step Schemas
// ============================================

export const ValidateReferenceImagesInputSchema = z.object({
  referenceImageUrls: z.array(z.string()).optional()
});

export const GenerateImageIdeasInputSchema = z.object({
  content: z.string(),
  numberOfIdeas: z.number(),
  colorPalette: z.string().optional(),
  artDirection: z.string().optional()
});

export const GenerateImagesInputSchema = z.object({
  input: z.object({
    referenceImageUrls: z.union([z.string(), z.array(z.string())]).optional(),
    aspectRatio: z.enum(['1:1', '16:9', '9:16', '4:3', '3:4']),
    resolution: z.enum(['1K', '2K', '4K']),
    numberOfGenerations: z.number(),
    storageNamespace: z.string().optional()
  }),
  prompt: z.string()
});

// Schema for LLM response validation
export const ImageIdeasSchema = z.object({
  ideas: z.array(z.string()).describe('Array of detailed image prompts for Gemini')
});

// ============================================
// Type Exports
// ============================================

export type ValidateReferenceImagesInput = z.infer<typeof ValidateReferenceImagesInputSchema>;
export type GenerateImageIdeasInput = z.infer<typeof GenerateImageIdeasInputSchema>;
export type GenerateImagesInput = z.infer<typeof GenerateImagesInputSchema>;
export type ImageIdeas = z.infer<typeof ImageIdeasSchema>;

Best Practices

1. Use Descriptive Field Descriptions

// Good - helps with documentation and error messages
z.string().describe('User email address for notifications')

// Avoid - no context for errors
z.string()

2. Provide Sensible Defaults

// Good - workflow works without optional fields
numberOfIdeas: z.number().min(1).max(10).default(1)

// Avoid - forces users to provide every field
numberOfIdeas: z.number().min(1).max(10)

3. Separate Workflow and Step Schemas

// Workflow input schema (what the user provides)
export const WorkflowInputSchema = z.object({ ... });

// Step schemas (internal data shapes)
export const StepNameInputSchema = z.object({ ... });

4. Export Both Schemas and Types

// Export schema for runtime validation
export const UserSchema = z.object({ ... });

// Export type for TypeScript type checking
export type User = z.infer<typeof UserSchema>;

Verification Checklist

• z is imported from @output.ai/core
• WorkflowInputSchema is defined and exported
• WorkflowInput type is exported
• WorkflowOutput type is defined
• Each step has corresponding input/output schemas
• All schemas have .describe() for important fields
• Optional fields use .optional() or .default()
• Numeric fields have appropriate min/max constraints

Related Skills

• output-dev-workflow-function - Using schemas in workflow definitions
• output-dev-step-function - Using schemas in step definitions
• output-dev-folder-structure - Where types.ts belongs in the project
• output-error-zod-import - Troubleshooting schema import issues