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Typescript

OrganizationPopular
prowler-cloud
typescript

TypeScript strict patterns and best practices. Trigger: When implementing or refactoring TypeScript in .ts/.tsx (types, interfaces, generics, const maps, type guards, removing any, tightening unknown).

Overview

Publisherprowler-cloud
Repositoryprowler
Skill nametypescript
Stars
14.8K
Forks
2.4K
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by prowler-cloud on GitHub. Read the source before you install it.

Installation

Install the Typescript AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/prowler-cloud/prowler.git /tmp/prowler
mkdir -p .claude/skills
cp -r /tmp/prowler/skills/typescript .claude/skills/typescript
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Typescript in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Typescript on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Typescript is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Const Types Pattern (REQUIRED)

typescript
// ✅ ALWAYS: Create const object first, then extract type
const STATUS = {
  ACTIVE: "active",
  INACTIVE: "inactive",
  PENDING: "pending",
} as const;

type Status = (typeof STATUS)[keyof typeof STATUS];

// ❌ NEVER: Direct union types
type Status = "active" | "inactive" | "pending";

Why? Single source of truth, runtime values, autocomplete, easier refactoring.

Flat Interfaces (REQUIRED)

typescript
// ✅ ALWAYS: One level depth, nested objects → dedicated interface
interface UserAddress {
  street: string;
  city: string;
}

interface User {
  id: string;
  name: string;
  address: UserAddress;  // Reference, not inline
}

interface Admin extends User {
  permissions: string[];
}

// ❌ NEVER: Inline nested objects
interface User {
  address: { street: string; city: string };  // NO!
}

Never Use any

typescript
// ✅ Use unknown for truly unknown types
function parse(input: unknown): User {
  if (isUser(input)) return input;
  throw new Error("Invalid input");
}

// ✅ Use generics for flexible types
function first<T>(arr: T[]): T | undefined {
  return arr[0];
}

// ❌ NEVER
function parse(input: any): any { }

Utility Types

typescript
Pick<User, "id" | "name">     // Select fields
Omit<User, "id">              // Exclude fields
Partial<User>                 // All optional
Required<User>                // All required
Readonly<User>                // All readonly
Record<string, User>          // Object type
Extract<Union, "a" | "b">     // Extract from union
Exclude<Union, "a">           // Exclude from union
NonNullable<T | null>         // Remove null/undefined
ReturnType<typeof fn>         // Function return type
Parameters<typeof fn>         // Function params tuple

Type Guards

typescript
function isUser(value: unknown): value is User {
  return (
    typeof value === "object" &&
    value !== null &&
    "id" in value &&
    "name" in value
  );
}

Coupled Optional Props (REQUIRED)

Do not model semantically coupled props as independent optionals — this allows invalid half-states that compile but break at runtime. Use discriminated unions with never to make invalid combinations impossible.

typescript
// ❌ BEFORE: Independent optionals — half-states allowed
interface PaginationProps {
  onPageChange?: (page: number) => void;
  pageSize?: number;
  currentPage?: number;
}

// ✅ AFTER: Discriminated union — shape is all-or-nothing
type ControlledPagination = {
  controlled: true;
  currentPage: number;
  pageSize: number;
  onPageChange: (page: number) => void;
};

type UncontrolledPagination = {
  controlled: false;
  currentPage?: never;
  pageSize?: never;
  onPageChange?: never;
};

type PaginationProps = ControlledPagination | UncontrolledPagination;

Key rule: If two or more props are only meaningful together, they belong to the same discriminated union branch. Mixing them as independent optionals shifts correctness responsibility from the type system to runtime guards.

Import Types

typescript
import type { User } from "./types";
import { createUser, type Config } from "./utils";

Frequently asked questions

What does the Typescript AI skill do?

TypeScript strict patterns and best practices. Trigger: When implementing or refactoring TypeScript in .ts/.tsx (types, interfaces, generics, const maps, type guards, removing any, tightening unknown).

Why use Typescript on TypingMind?

Because you install it once and use it with any model. Typescript is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Typescript in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/prowler-cloud/prowler/tree/master/skills/typescript. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Typescript?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Typescript?

As many as you like. As long as a model supports skills, you can use Typescript with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Typescript AI skill free?

Yes. It is published on GitHub by prowler-cloud under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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