Explore tree-shakeable schema validation with v.pipe(), actions, parse/safeParse, InferOutput, and transformation actions
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What is the primary design advantage of Valibot over similar validators?
Valibot: composes schemas from small standalone functions rather than chained methods on a large object, so a bundler includes only the validators you actually import. The result is a dramatically smaller footprint than method-chaining libraries — a key benefit for client-side and edge bundles where size matters.
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How do you define an object schema with a constrained string in Valibot?
Valibot pipes:v.object({ name: v.pipe(v.string(), v.minLength(2), v.maxLength(50)) }). A schema is built by piping a base type through validation actions. Each action is a separately importable function, which is what enables tree-shaking. The pipe reads left to right: parse as string, then enforce min and max length.
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What does v.parse(schema, input) do, and what is the safe variant?
parse vs safeParse:v.parse(schema, input) returns the validated, typed value or throws a ValiError. v.safeParse(schema, input) returns { success, output, issues } — check success before using output, and inspect issues for detailed paths and messages, avoiding exceptions in control flow.
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How do you infer the TypeScript type of a Valibot schema?
Type inference:type User = v.InferOutput<typeof UserSchema> gives the post-validation/transform type, while v.InferInput gives the accepted input type (they differ when transforms are present). As with peer libraries, this keeps runtime validation and compile-time types in sync from one declaration.
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What is a transformation action in a Valibot pipe?
Transformations: within a pipe you can add v.transform((value) => ...) to reshape the output, e.g. v.pipe(v.string(), v.transform((s) => s.trim().toLowerCase())). Validation actions run before the transform, and the transform changes the inferred output type — mirroring transform behaviour in other modern validators.