web-forms-zod-validation

Zod Schema Validation Patterns

Quick Guide: Use Zod for runtime validation at trust boundaries (API responses, form inputs, config, URL params). Define schemas once, derive types with z.infer. Use safeParse for error handling, refine/superRefine for custom validation, transform for data conversion. Named constants for all validation limits.

Version Note: Zod v4 is now the stable release (v4.1+). It brings 14.7x faster string parsing, 57% smaller bundle, and new top-level APIs (z.email(), z.url(), z.iso.*). The v3 method-chain equivalents (z.string().email()) still work but are deprecated. For migration details, see [reference.md](reference.md).

<critical_requirements>

CRITICAL: Before Using This Skill

All code must follow project conventions in CLAUDE.md (kebab-case, named exports, import ordering, import type, named constants)

(You MUST use safeParse instead of parse for user-facing validation - prevents unhandled exceptions)

(You MUST use z.infer<typeof schema> to derive types - never duplicate schema as separate interface)

(You MUST validate at trust boundaries - API responses, form inputs, config files, URL params)

(You MUST use named constants for validation limits - NO magic numbers in .min(), .max(), .length())

</critical_requirements>

Auto-detection: Zod schemas, z.object, z.string, z.number, z.infer, safeParse, refine, superRefine, transform, discriminatedUnion, z.coerce, z.pipe, z.catch, z.brand, z.lazy, z.email, z.url, z.iso

When to use:

• Validating API responses before using data
• Parsing form input data with type safety
• Validating configuration files or environment variables
• Defining contracts between systems (frontend/backend shared schemas)
• Runtime type checking for data from untrusted sources

When NOT to use:

• Internal function parameters (TypeScript is sufficient for trusted data)
• Simple boolean checks that don't need schema definition
• Performance-critical hot paths where validation overhead matters

<philosophy>

Philosophy

TypeScript provides compile-time type safety for code you control. Zod provides runtime validation for data you don't control - API responses, user input, configuration files, URL parameters. Use TypeScript for internal contracts; use Zod at trust boundaries where external data enters your system.

Key principle: Define the schema once, derive the type. Never maintain parallel type definitions and validation logic - they will drift apart.

// Schema is the source of truth
const UserSchema = z.object({
  name: z.string(),
  email: z.string().email(),
});

// Type is derived, always in sync
type User = z.infer<typeof UserSchema>;

</philosophy>

<patterns>

Core Patterns

Pattern 1: Schema Definition with Named Constants

Define schemas with named constants for all validation limits. Custom error messages for user-facing fields.

const MIN_USERNAME_LENGTH = 3;
const MAX_USERNAME_LENGTH = 50;

const UserSchema = z.object({
  username: z
    .string()
    .min(
      MIN_USERNAME_LENGTH,
      `Username must be at least ${MIN_USERNAME_LENGTH} characters`,
    )
    .max(
      MAX_USERNAME_LENGTH,
      `Username cannot exceed ${MAX_USERNAME_LENGTH} characters`,
    ),
  email: z.string().email("Invalid email format"),
});

type User = z.infer<typeof UserSchema>; // Always derived, never manual interface

Why good: named constants make limits discoverable, custom error messages improve UX, type derived from schema

See [examples/core.md](examples/core.md) for complete schema examples with reusable sub-schemas and CRUD composition patterns.

Pattern 2: Safe Parsing for Error Handling

Use safeParse for user input and API responses. Reserve parse for config/internal data where invalid = programming error.

const result = UserSchema.safeParse(data);

if (!result.success) {
  const errors = result.error.issues.reduce(
    (acc, err) => {
      const field = err.path.join(".");
      acc[field] = err.message;
      return acc;
    },
    {} as Record<string, string>,
  );
  return { success: false, errors };
}

return { success: true, user: result.data };

Why good: safeParse never throws, validation errors handled explicitly, error formatting provides useful field-level feedback

See [examples/core.md](examples/core.md) for form validation and API response validation patterns.

Pattern 3: Refinements and Cross-Field Validation

Use refine for custom validation logic. Use superRefine when you need cross-field validation with specific error paths.

const MIN_PASSWORD_LENGTH = 8;

const PasswordFormSchema = z
  .object({
    password: z.string().min(MIN_PASSWORD_LENGTH),
    confirmPassword: z.string(),
  })
  .superRefine((data, ctx) => {
    if (data.password !== data.confirmPassword) {
      ctx.addIssue({
        code: z.ZodIssueCode.custom,
        message: "Passwords do not match",
        path: ["confirmPassword"],
      });
    }
  });

Why good: superRefine enables cross-field validation with specific error paths, keeps all validation in the schema

See [examples/core.md](examples/core.md) for password refinement chains and conditional validation patterns.

Pattern 4: Transforms and Type Conversion

Use transform to convert data during validation. Use z.input and z.output when transforms change the type.

const DateSchema = z
  .string()
  .datetime()
  .transform((str) => new Date(str));

type DateInput = z.input<typeof DateSchema>; // string
type DateOutput = z.output<typeof DateSchema>; // Date

Gotcha: z.infer returns the output type. When a function accepts pre-validation input, use z.input for the parameter type.

See [examples/transforms.md](examples/transforms.md) for coercion patterns (URL params, form data) and transform pipelines.

Pattern 5: Discriminated Unions

Use discriminatedUnion when objects share a common discriminator field. Provides better error messages and TypeScript narrowing than union.

const NotificationSchema = z.discriminatedUnion("type", [
  z.object({
    type: z.literal("email"),
    email: z.string().email(),
    subject: z.string(),
  }),
  z.object({ type: z.literal("sms"), phone: z.string(), message: z.string() }),
  z.object({
    type: z.literal("push"),
    deviceId: z.string(),
    title: z.string(),
  }),
]);

Why good over z.union: discriminatedUnion reports which variant failed (not "Invalid input"), TypeScript narrows type in switch statements

See [examples/core.md](examples/core.md) for payment method union and type narrowing examples.

Pattern 6: Schema Composition

Compose schemas using extend, pick, omit, and partial for CRUD operations.

const BaseEntitySchema = z.object({
  id: z.string().uuid(),
  createdAt: z.string().datetime(),
  updatedAt: z.string().datetime(),
});

const UserSchema = BaseEntitySchema.extend({
  email: z.string().email(),
  name: z.string(),
});
const CreateUserSchema = UserSchema.omit({
  id: true,
  createdAt: true,
  updatedAt: true,
});
const UpdateUserSchema = CreateUserSchema.partial();
const UserSummarySchema = UserSchema.pick({ id: true, name: true });

See [examples/core.md](examples/core.md) for full CRUD schema composition example.

Pattern 7: Coercion for String Inputs

Use z.coerce for URL params and form data that arrive as strings. Simpler than manual parsing.

const DEFAULT_PAGE = 1;
const DEFAULT_LIMIT = 20;
const MAX_LIMIT = 100;

const PaginationSchema = z.object({
  page: z.coerce.number().int().positive().default(DEFAULT_PAGE),
  limit: z.coerce
    .number()
    .int()
    .positive()
    .max(MAX_LIMIT)
    .default(DEFAULT_LIMIT),
});

// "3" -> 3, "50" -> 50, missing -> defaults

Gotcha: z.coerce.boolean() coe