aiskillstore/typescript-best-practices

TypeScript Best Practices

Pair with React Best Practices

When working with React components (.tsx, .jsx files or @react imports), always load react-best-practices alongside this skill. This skill covers TypeScript fundamentals; React-specific patterns (effects, hooks, refs, component design) are in the dedicated React skill.

Type-First Development

Types define the contract before implementation. Follow this workflow:

1. Define the data model - types, interfaces, and schemas first
2. Define function signatures - input/output types before logic
3. Implement to satisfy types - let the compiler guide completeness
4. Validate at boundaries - runtime checks where data enters the system

Make Illegal States Unrepresentable

Use the type system to prevent invalid states at compile time.

Discriminated unions for mutually exclusive states:

// Good: only valid combinations possible
type RequestState<T> =
  | { status: 'idle' }
  | { status: 'loading' }
  | { status: 'success'; data: T }
  | { status: 'error'; error: Error };

// Bad: allows invalid combinations like { loading: true, error: Error }
type RequestState<T> = {
  loading: boolean;
  data?: T;
  error?: Error;
};

Branded types for domain primitives:

type UserId = string & { readonly __brand: 'UserId' };
type OrderId = string & { readonly __brand: 'OrderId' };

// Compiler prevents passing OrderId where UserId expected
function getUser(id: UserId): Promise<User> { /* ... */ }

function createUserId(id: string): UserId {
  return id as UserId;
}

Const assertions for literal unions:

const ROLES = ['admin', 'user', 'guest'] as const;
type Role = typeof ROLES[number]; // 'admin' | 'user' | 'guest'

// Array and type stay in sync automatically
function isValidRole(role: string): role is Role {
  return ROLES.includes(role as Role);
}

Required vs optional fields - be explicit:

// Creation: some fields required
type CreateUser = {
  email: string;
  name: string;
};

// Update: all fields optional
type UpdateUser = Partial<CreateUser>;

// Database row: all fields present
type User = CreateUser & {
  id: UserId;
  createdAt: Date;
};

Module Structure

Prefer smaller, focused files: one component, hook, or utility per file. Split when a file handles multiple concerns or exceeds ~200 lines. Colocate tests with implementation (foo.test.ts alongside foo.ts). Group related files by feature rather than by type.

Functional Patterns

• Prefer const over let; use readonly and Readonly<T> for immutable data.
• Use array.map/filter/reduce over for loops; chain transformations in pipelines.
• Write pure functions for business logic; isolate side effects in dedicated modules.
• Avoid mutating function parameters; return new objects/arrays instead.

Instructions

• Enable strict mode; model data with interfaces and types. Strong typing catches bugs at compile time.
• Every code path returns a value or throws; use exhaustive switch with never checks in default. Unhandled cases become compile errors.
• Propagate errors with context; catching requires re-throwing or returning a meaningful result. Hidden failures delay debugging.
• Handle edge cases explicitly: empty arrays, null/undefined inputs, boundary values. Defensive checks prevent runtime surprises.
• Use await for async calls; wrap external calls with contextual error messages. Unhandled rejections crash Node processes.
• Add or update focused tests when changing logic; test behavior, not implementation details.

Examples

Explicit failure for unimplemented logic:

export function buildWidget(widgetType: string): never {
  throw new Error(`buildWidget not implemented for type: ${widgetType}`);
}

Exhaustive switch with never check:

type Status = "active" | "inactive";

export function processStatus(status: Status): string {
  switch (status) {
    case "active":
      return "processing";
    case "inactive":
      return "skipped";
    default: {
      const _exhaustive: never = status;
      throw new Error(`unhandled status: ${_exhaustive}`);
    }
  }
}

Wrap external calls with context:

export async function fetchWidget(id: string): Promise<Widget> {
  const response = await fetch(`/api/widgets/${id}`);
  if (!response.ok) {
    throw new Error(`fetch widget ${id} failed: ${response.status}`);
  }
  return response.json();
}

Debug logging with namespaced logger:

import debug from "debug";

const log = debug("myapp:widgets");

export function createWidget(name: string): Widget {
  log("creating widget: %s", name);
  const widget = { id: crypto.randomUUID(), name };
  log("created widget: %s", widget.id);
  return widget;
}

Runtime Validation with Zod

• Define schemas as single source of truth; infer TypeScript types with z.infer<>. Avoid duplicating types and schemas.
• Use safeParse for user input where failure is expected; use parse at trust boundaries where invalid data is a bug.
• Compose schemas with .extend(), .pick(), .omit(), .merge() for DRY definitions.
• Add .transform() for data normalization at parse time (trim strings, parse dates).
• Include descriptive error messages; use .refine() for custom validation logic.

Examples

Schema as source of truth with type inference:

import { z } from "zod";

const UserSchema = z.object({
  id: z.string().uuid(),
  email: z.string().email(),
  name: z.string().min(1),
  createdAt: z.string().transform((s) => new Date(s)),
});

type User = z.infer<typeof UserSchema>;

Return parse results to callers (never swallow errors):

import { z, SafeParseReturnType } from "zod";

export function parseUserInput(raw: unknown): SafeParseReturnType<unknown, User> {
  return UserSchema.safeParse(raw);
}

// Caller handles both success and error:
const result = parseUserInput(formData);
if (!result.success) {
  setErrors(result.error.flatten().fieldErrors);
  return;
}
await submitUser(result.data);

Strict parsing at trust boundaries:

export async function fetchUser(id: string): Promise<User> {
  const response = await fetch(`/api/users/${id}`);
  if (!response.ok) {
    throw new Error(`fetch user ${id} failed: ${response.status}`);
  }
  const data = await response.json();
  return UserSchema.parse(data); // throws if API contract violated
}

Schema composition:

const CreateUserSchema = UserSchema.omit({ id: true, createdAt: true });
const UpdateUserSchema = CreateUserSchema.partial();
const UserWithPostsSchema = UserSchema.extend({
  posts: z.array(PostSchema),
});

Configuration

• Load config from environment variables at startup; validate with Zod before use. Invalid config should crash immediately.
• Define a typed config object as single source of truth; avoid accessing process.env throughout the codebase.
• Use sensible defaults for development; require explicit values for production secrets.

Examples

Typed config with Zod validation:

import { z } from "zod";

const ConfigSchema = z.object({
  PORT: z.coerce.number().default(3000),
  DATABASE_URL: z.string().url(),
  API_KEY: z.string().min(1),
  NODE_ENV: z.enum(["development", "production", "test"]).default("development"),
});

export const config = ConfigSchema.parse(process.env);

Access config values (not process.env directly):

import { config } from "./config";

const server = app.listen(config.PORT);
const db = connect(config.DATABASE_URL);

Optional: type-fest

For advanced type utilities beyond TypeScript builtins, consider type-fest:

• Opaque<T, Token> - cleaner branded types than manual & { __brand } pattern
• PartialDeep<T> - recursive partial for nested objects
• ReadonlyDeep<T> - recursive readonly for immutable data
• LiteralUnion<Literals, Fallback> - literals with autocomplete + string fallback
• SetRequired<T, K> / SetOptional<T, K> - targeted field modifications
• Simplify<T> - flatten complex intersection types in IDE tooltips

import type { Opaque, PartialDeep, SetRequired } from 'type-fest';

// Branded type (cleaner than manual approach)
type UserId = Opaque<string, 'UserId'>;

// Deep partial for patch operations
type UserPatch = PartialDeep<User>;

// Make specific fields required
type UserWithEmail = SetRequired<Partial<User>, 'email'>;