Data Schema Validator

Data Schema Validator

Validate data structures, generate type-safe schemas, and detect drift between expected and actual data shapes.

Workflow

1. Collect data samples Gather data from JSON/YAML files, API responses, or inline data.

2. Analyze the data shape Recursively map every field:

   • Type(s) observed per field
   • Optional vs required (present in all samples?)
   • Nullable fields
   • Array item types (homogeneous vs heterogeneous)
   • Enum-like fields (small set of repeated string values)
   • Nested object structures

3. Generate TypeScript interfaces

   export interface User {
     id: number;
     name: string;
     email: string;
     avatar_url?: string | null;
     role: "admin" | "editor" | "viewer";
     settings: UserSettings;
     created_at: string;
   }
   

   • PascalCase names, extract nested objects into separate interfaces
   • Use ? for optional, | null for nullable
   • Prefer string literal unions when values are enumerable

4. Generate Zod validation schemas

   import { z } from "zod";
   export const userSchema = z.object({
     id: z.number().int().positive(),
     name: z.string().min(1),
     email: z.string().email(),
     avatar_url: z.string().url().nullable().optional(),
     role: z.enum(["admin", "editor", "viewer"]),
     settings: userSettingsSchema,
     created_at: z.string().datetime(),
   });
   export type User = z.infer<typeof userSchema>;
   

5. Validate data against existing schemas Compare sample data against existing types:

   • Fields in data but missing from schema → Schema needs update
   • Type mismatches → Type error
   • Null values for non-nullable fields → Validation failure

6. Detect schema drift across versions Compare two data samples (v1 vs v2) field by field:

   • Added/removed fields, type changes, nullability changes
   • Output migration summary with recommended type changes

Rules

• Always generate both TypeScript interfaces AND Zod schemas unless user asks for only one
• Use z.infer<typeof schema> to derive types — never define the same type twice
• Extract nested objects into named sub-schemas
• Prefer string literal unions over plain string for fewer than 10 known values
• When validating, distinguish "field missing" (drift) from "field null" (data issue)
• If multiple samples provided, merge to detect optional vs required fields