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Flyweight Pattern

Organization
PatternsDev
flyweight-pattern

Teaches the flyweight pattern for memory optimization. Use when your application creates large numbers of similar objects and memory consumption is a concern.

Overview

PublisherPatternsDev
Repositoryskills
Skill nameflyweight-pattern
Stars
250
Forks
27
Bundled files
Instructions only
LicenseMIT
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 PatternsDev on GitHub. Read the source before you install it.

Installation

Install the Flyweight Pattern 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/PatternsDev/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/javascript/flyweight-pattern .claude/skills/flyweight-pattern
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Flyweight Pattern 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 Flyweight Pattern 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 Flyweight Pattern 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.

Flyweight Pattern

The flyweight pattern is a useful way to conserve memory when we're creating a large number of similar objects.

In our application, we want users to be able to add books. All books have a title, an author, and an isbn number! However, a library usually doesn't have just one copy of a book: it usually has multiple copies of the same book.

When to Use

  • Use this when creating a huge number of objects that could potentially drain available memory
  • This is helpful when many objects share the same intrinsic properties (e.g., books with the same ISBN)

When NOT to Use

  • When the number of objects is small enough that memory is not a concern
  • When objects have few or no shared intrinsic properties — the separation of intrinsic and extrinsic state adds complexity without savings
  • When the added lookup/management overhead outweighs the memory benefit

Instructions

  • Separate intrinsic (shared) state from extrinsic (unique) state
  • Use a Map or similar structure to cache and reuse shared object instances
  • Consider JavaScript's prototypal inheritance as a simpler alternative in many cases

Details

It wouldn't be very useful to create a new book instance each time if there are multiple copies of the exact same book. Instead, we want to create multiple instances of the Book constructor, that represent a single book.

js
class Book {
  constructor(title, author, isbn) {
    this.title = title;
    this.author = author;
    this.isbn = isbn;
  }
}

Let's create the functionality to add new books to the list. If a book has the same ISBN number, thus is the exact same book type, we don't want to create an entirely new Book instance. Instead, we should first check whether this book already exists.

js
const books = new Map();

const createBook = (title, author, isbn) => {
  const existingBook = books.has(isbn);

  if (existingBook) {
    return books.get(isbn);
  }
};

If it doesn't contain the book's ISBN number yet, we'll create a new book and add its ISBN number to the isbnNumbers set.

js
const createBook = (title, author, isbn) => {
  const existingBook = books.has(isbn);

  if (existingBook) {
    return books.get(isbn);
  }

  const book = new Book(title, author, isbn);
  books.set(isbn, book);

  return book;
};

The createBook function helps us create new instances of one type of book. However, a library usually contains multiple copies of the same book! Let's create an addBook function, which allows us to add multiple copies of the same book. It should invoke the createBook function, which returns either a newly created Book instance, or returns the already existing instance.

In order to keep track of the total amount of copies, let's create a bookList array that contains the total amount of books in the library.

js
const bookList = [];

const addBook = (title, author, isbn, availability, sales) => {
  const book = {
    ...createBook(title, author, isbn),
    sales,
    availability,
    isbn,
  };

  bookList.push(book);
  return book;
};

Perfect! Instead of creating a new Book instance each time we add a copy, we can effectively use the already existing Book instance for that particular copy. Let's create 5 copies of 3 books: Harry Potter, To Kill a Mockingbird, and The Great Gatsby.

js
addBook("Harry Potter", "JK Rowling", "AB123", false, 100);
addBook("Harry Potter", "JK Rowling", "AB123", true, 50);
addBook("To Kill a Mockingbird", "Harper Lee", "CD345", true, 10);
addBook("To Kill a Mockingbird", "Harper Lee", "CD345", false, 20);
addBook("The Great Gatsby", "F. Scott Fitzgerald", "EF567", false, 20);

Although there are 5 copies, we only have 3 Book instances!

The flyweight pattern is useful when you're creating a huge number of objects, which could potentially drain all available RAM. It allows us to minimize the amount of consumed memory.

In JavaScript, we can easily solve this problem through prototypal inheritance. Nowadays, hardware has GBs of RAM, which makes the flyweight pattern less important.

Source

References

Frequently asked questions

What does the Flyweight Pattern AI skill do?

Teaches the flyweight pattern for memory optimization. Use when your application creates large numbers of similar objects and memory consumption is a concern.

Why use Flyweight Pattern on TypingMind?

Because you install it once and use it with any model. Flyweight Pattern 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 Flyweight Pattern in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/PatternsDev/skills/tree/main/javascript/flyweight-pattern. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Flyweight Pattern?

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 Flyweight Pattern?

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

Is the Flyweight Pattern AI skill free?

Yes. It is published on GitHub by PatternsDev under the MIT 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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