| name | output-dev-types-file |
| description | Create types.ts files with Zod schemas for Output SDK workflows. Use when defining input/output schemas, creating type definitions, or fixing schema-related errors. |
| allowed-tools | ["Read","Write","Edit"] |
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 @outputai/core, NEVER from zod directly:
import { z } from '@outputai/core';
import { z } from 'zod';
Related Skill: output-error-zod-import for troubleshooting import issues
Basic Structure
import { z } from '@outputai/core';
export const WorkflowInputSchema = z.object({
});
export type WorkflowInput = z.infer<typeof WorkflowInputSchema>;
export type WorkflowOutput = ;
export const StepNameInputSchema = z.object({
});
export const StepNameOutputSchema = z.object({
});
export type StepNameInput = z.infer<typeof StepNameInputSchema>;
export type StepNameOutput = z.infer<typeof StepNameOutputSchema>;
Common Schema Patterns
Basic Types
import { z } from '@outputai/core';
const stringField = z.string();
const optionalString = z.string().optional();
const stringWithDefault = z.string().default('default value');
const describedString = z.string().describe('Field description');
const numberField = z.number();
const integerField = z.number().int();
const rangedNumber = z.number().min(1).max(100);
const booleanField = z.boolean();
const defaultBoolean = z.boolean().default(false);
const enumField = z.enum(['option1', 'option2', 'option3']);
const enumWithDefault = z.enum(['small', 'medium', 'large']).default('medium');
Complex Types
import { z } from '@outputai/core';
const stringArray = z.array(z.string());
const objectArray = z.array(z.object({ id: z.string(), name: z.string() }));
const nestedObject = z.object({
user: z.object({
id: z.string(),
email: z.string().email()
}),
settings: z.object({
notifications: z.boolean()
})
});
const flexibleInput = z.union([
z.string(),
z.array(z.string())
]);
const keyValueMap = z.record(z.string(), z.number());
Validation Patterns
import { z } from '@outputai/core';
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);
const positiveNumber = z.number().positive();
const nonNegativeNumber = z.number().nonnegative();
const percentageNumber = z.number().min(0).max(100);
const nonEmptyArray = z.array(z.string()).min(1);
const limitedArray = z.array(z.string()).max(10);
Schema Constraints for LLM Output
Important: Schemas passed to Output.object() (sent to LLM providers) must NOT use .min()/.max() on z.number(). Anthropic rejects minimum/maximum JSON Schema constraints. Use .describe() instead to guide the LLM on expected ranges.
const llmOutputSchema = z.object({
score: z.number().describe('Quality score 0-100'),
confidence: z.number().describe('Confidence 0-1')
});
const workflowOutputSchema = z.object({
score: z.number().min(0).max(100).describe('Quality score 0-100'),
confidence: z.number().min(0).max(1).describe('Confidence 0-1')
});
Complete Example
Based on a real workflow (image_infographic_nano):
import { z } from '@outputai/core';
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().(),
: z.([, , , , ]).().(),
: z.([, , ]).().(),
: z.().().().().(),
: z.().().()
});
= z.< >;
= [];
= z.({
: z.(z.()).()
});
= z.({
: z.(),
: z.(),
: z.().(),
: z.().()
});
= z.({
: z.({
: z.([z.(), z.(z.())]).(),
: z.([, , , , ]),
: z.([, , ]),
: z.(),
: z.().()
}),
: z.()
});
= z.({
: z.(z.()).()
});
= z.< >;
= z.< >;
= z.< >;
= z.< >;
Best Practices
1. Use Descriptive Field Descriptions
z.string().describe('User email address for notifications')
z.string()
2. Provide Sensible Defaults
numberOfIdeas: z.number().min(1).max(10).default(1)
numberOfIdeas: z.number().min(1).max(10)
3. Separate Workflow and Step Schemas
export const WorkflowInputSchema = z.object({ ... });
export const StepNameInputSchema = z.object({ ... });
4. Export Both Schemas and Types
export const UserSchema = z.object({ ... });
export type User = z.infer<typeof UserSchema>;
Verification Checklist
Related Skills
output-dev-workflow-function - Using schemas in workflow definitions
output-dev-step-function - Using schemas in step definitions
output-dev-evaluator-function - Using schemas in evaluator definitions
output-dev-folder-structure - Where types.ts belongs in the project
output-error-zod-import - Troubleshooting schema import issues