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Presentational Container Pattern

Organization
PatternsDev
presentational-container-pattern

Teaches the presentational/container pattern for separating view and logic. Use when you want to isolate data fetching and business logic from UI rendering for better testability and reuse.

Overview

PublisherPatternsDev
Repositoryskills
Skill namepresentational-container-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 Presentational Container 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/react/presentational-container-pattern .claude/skills/presentational-container-pattern
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Presentational Container 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 Presentational Container 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 Presentational Container 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.

Container/Presentational Pattern

In React, one way to enforce separation of concerns is by using the Container/Presentational pattern. With this pattern, we can separate the view from the application logic.

When to Use

  • Use this when you want a clear separation between data-fetching logic and UI rendering
  • This is helpful for making presentational components reusable and easy to test

When NOT to Use

  • For small components where the separation into two files adds overhead without meaningful benefit
  • When hooks already encapsulate the data logic, making a separate container component redundant
  • When the component is a one-off view with no reuse potential for either layer

Instructions

  • Container components handle data fetching and state; presentational components handle rendering
  • Prefer custom Hooks over container components in modern React for the same separation of concerns
  • Keep presentational components as pure functions that receive data through props
  • Use this pattern when it genuinely simplifies your architecture — avoid it for small components

Details

Let's say we want to create an application that fetches 6 dog images, and renders these images on the screen.

Ideally, we want to enforce separation of concerns by separating this process into two parts:

  1. Presentational Components: Components that care about how data is shown to the user. In this example, that's the rendering the list of dog images.
  2. Container Components: Components that care about what data is shown to the user. In this example, that's fetching the dog images.

Fetching the dog images deals with application logic, whereas displaying the images only deals with the view.

Presentational Component

A presentational component receives its data through props. Its primary function is to simply display the data it receives the way we want them to, including styles, without modifying that data.

Let's take a look at the example that displays the dog images. When rendering the dog images, we simply want to map over each dog image that was fetched from the API, and render those images. In order to do so, we can create a functional component that receives the data through props, and renders the data it received.

The DogImages component is a presentational component. Presentational components are usually stateless: they do not contain their own React state, unless they need a state for UI purposes. The data they receive, is not altered by the presentational components themselves.

Presentational components receive their data from container components.

Container Components

The primary function of container components is to pass data to presentational components, which they contain. Container components themselves usually don't render any other components besides the presentational components that care about their data. Since they don't render anything themselves, they usually do not contain any styling either.

In our example, we want to pass dog images to the DogsImages presentational component. Before being able to do so, we need to fetch the images from an external API. We need to create a container component that fetches this data, and passes this data to the presentational component DogImages in order to display it on the screen.

Combining these two components together makes it possible to separate handling application logic with the view.

Hooks

In many cases, the Container/Presentational pattern can be replaced with React Hooks. The introduction of Hooks made it easy for developers to add statefulness without needing a container component to provide that state.

Instead of having the data fetching logic in the DogImagesContainer component, we can create a custom hook that fetches the images, and returns the array of dogs.

js
export default function useDogImages() {
  const [dogs, setDogs] = useState([]);

  useEffect(() => {
    fetch("https://dog.ceo/api/breed/labrador/images/random/6")
      .then((res) => res.json())
      .then(({ message }) => setDogs(message));
  }, []);

  return dogs;
}

By using this hook, we no longer need the wrapping DogImagesContainer container component to fetch the data, and send this to the presentational DogImages component. Instead, we can use this hook directly in our presentational DogImages component!

By using the useDogImages hook, we still separated the application logic from the view. We're simply using the returned data from the useDogImages hook, without modifying that data within the DogImages component.

Hooks make it easy to separate logic and view in a component, just like the Container/Presentational pattern. It saves us the extra layer that was necessary in order to wrap the presentational component within the container component.

Pros

There are many benefits to using the Container/Presentational pattern.

The Container/Presentational pattern encourages the separation of concerns. Presentational components can be pure functions which are responsible for the UI, whereas container components are responsible for the state and data of the application. This makes it easy to enforce the separation of concerns.

Presentational components are easily made reusable, as they simply display data without altering this data. We can reuse the presentational components throughout our application for different purposes.

Since presentational components don't alter the application logic, the appearance of presentational components can easily be altered by someone without knowledge of the codebase, for example a designer. If the presentational component was reused in many parts of the application, the change can be consistent throughout the app.

Testing presentational components is easy, as they are usually pure functions. We know what the components will render based on which data we pass, without having to mock a data store.

Cons

The Container/Presentational pattern makes it easy to separate application logic from rendering logic. However, Hooks make it possible to achieve the same result without having to use the Container/Presentational pattern, and without having to rewrite a stateless functional component into a class component. Note that today, we don't need to create class components to use state anymore.

Although we can still use the Container/Presentational pattern, even with React Hooks, this pattern can easily be an overkill in smaller sized application.

Source

References

Frequently asked questions

What does the Presentational Container Pattern AI skill do?

Teaches the presentational/container pattern for separating view and logic. Use when you want to isolate data fetching and business logic from UI rendering for better testability and reuse.

Why use Presentational Container Pattern on TypingMind?

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

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

Which AI models can use Presentational Container 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 Presentational Container Pattern?

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

Is the Presentational Container 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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