Zod Resolver & Shared Validation Rules Reuse rules without confusing form, transport and persistence contracts
Phase 21 - Forms with React Hook Form + Zod Volume 06 - Professional Frontend
Why Zod appears again NestJS already validates transport input with Zod. The frontend also needs immediate validation feedback. This is the first time the same field-level rules are genuinely useful in two workspace packages, so a small shared validators package has now earned its place.
packages/validators: reusable field primitivesapps/api: HTTP transport schemaapps/web: UI form schemaShared field primitives // packages/validators/src/tasks.ts import { z } from "zod";
export const taskTitleSchema = z .string() .trim() .min(1, "Title is required.") .max(200, "Title must be 200 characters or fewer.");
export const taskDescriptionSchema = z .string() .trim() .max(2000, "Description must be 2000 characters or fewer.");
export const taskPrioritySchema = z.enum([
"LOW", "MEDIUM", "HIGH",
]);// apps/web/src/components/tasks/task-form.schema.ts import { z } from "zod"; import { taskDescriptionSchema, taskPrioritySchema, taskTitleSchema, } from "@taskflow/validators/tasks";
export const taskFormSchema = z.object({ title: taskTitleSchema, description: taskDescriptionSchema, priority: taskPrioritySchema, dueAtLocal: z.string(), });
export type TaskFormValues = z.infer<typeof taskFormSchema>;Connect Zod to React Hook Form const form = useForm<TaskFormValues>({ resolver: zodResolver(taskFormSchema), defaultValues, mode: "onBlur", });
The UI form schema is allowed to differ from the API transport schema. For example, an HTML datetime-local value is a local string; the API may require an ISO timestamp. Map between them deliberately on submit.
Why not share the Prisma model? A Prisma model describes persistence. A form schema describes editing. A transport schema describes an HTTP request. They overlap, but they are not interchangeable contracts.
Zod Resolver and Shared Validation Rules matters because it changes a real boundary in TaskFlow. The goal is to understand the inputs, owner, observable output, and failure modes before adding more abstraction.
Restate the goal of “Zod Resolver and Shared Validation Rules” as one observable behavior. Write the success signal before touching code.
Trace the boundary map: untrusted input → runtime schema → validated value → business rule → safe operation. Mark the one boundary this lesson changes.
Inspect the current repository state and record the exact files, route, table, or runtime involved. Avoid creating an abstraction before you can name the existing owner.
Implement the smallest vertical slice. Add one coherent change, run the narrowest verification command, and read the output before continuing.
Connect the slice to TaskFlow’s phase artifact: Create/edit task forms with shared field rules, accessible errors, async submission, server-error mapping, and reset behavior.. Verify behavior from the outside as a user or API client would.
Create one controlled failure that is safe to reproduce. Predict the symptom first, capture evidence, locate the earliest broken boundary, then fix only that cause.
Close the reference and rebuild or explain the core change from memory. Record what you would monitor or test before trusting the same change in production.
const CreateTaskSchema = z.object({
title: z.string().trim().min(1).max(160),
priority: z.enum(["LOW", "MEDIUM", "HIGH"]),
});
const input = CreateTaskSchema.parse(requestBody);Do not copy this as a finished answer. Predict what each boundary does, type the smallest useful slice, run it, then compare the result with your prediction.
Return to the nearest observable boundary: input, network, process log, database, or rendered UI. Compare actual evidence with the predicted path.
Change one variable at a time and reproduce the original failure after the fix; otherwise you have correlation, not diagnosis.
Without copying the final source, create a second small scenario that applies “Zod Resolver and Shared Validation Rules” to a different TaskFlow resource or input. Write the acceptance criteria first, implement it, and provide the evidence that each criterion is satisfied.
Connect Zod resolver and reuse only rules that genuinely belong in both client and API validation.
Use the smallest change that demonstrates this lesson. Stop after it works and capture evidence.Clearing user input on every failed request
Predict the symptom before causing the failure. Then locate the earliest boundary where reality diverges from expectation.Why does this responsibility live here, what input does it trust, what output proves success, and what would go wrong if another layer owned it?
If your explanation is vague, repeat the boundary trace before moving on.Close the reference. Reproduce the core idea from memory or explain the exact request/data flow aloud. Then check your diff or notes for accidental shortcuts that bypass validation, authorization, error handling, or verification earned in earlier phases.