Incremental Static Rendering logo

Incremental Static Rendering

Organization
PatternsDev
incremental-static-rendering

Teaches Incremental Static Regeneration (ISR) for updating static content post-build. Use when you have static pages that need periodic updates without a full site rebuild.

Overview

PublisherPatternsDev
Repositoryskills
Skill nameincremental-static-rendering
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 Incremental Static Rendering 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/incremental-static-rendering .claude/skills/incremental-static-rendering
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Incremental Static Rendering 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 Incremental Static Rendering 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 Incremental Static Rendering 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.

Incremental Static Generation

Static Generation (SSG) addresses most of the concerns of SSR and CSR but is suitable for rendering mostly static content. It poses limitations when the content to be rendered is dynamic or changing frequently.

Think of a growing blog with multiple posts. You wouldn't possibly want to rebuild and redeploy the site just because you want to correct a typo in one of the posts. Similarly, one new blog post should also not require a rebuild for all the existing pages. Thus, SSG on its own is not enough for rendering large websites or applications.

When to Use

  • Use this when you have mostly static pages that need periodic data updates without full rebuilds
  • This is helpful for large sites (blogs, e-commerce) where rebuilding every page on each change is impractical

When NOT to Use

  • When content changes in real-time and stale data is unacceptable (e.g., live scores, stock tickers)
  • For pages that are fully dynamic and personalized per user — SSR is a better fit
  • When the revalidation window creates a confusing experience where different users see different content versions

Instructions

  • Use revalidate in getStaticProps to set a time interval for background page regeneration
  • Use fallback: true in getStaticPaths to lazily generate pages on first request
  • Consider on-demand revalidation (revalidatePath, revalidateTag) for immediate updates after content changes
  • In Next.js 13+ App Router, use generateStaticParams and async components for ISR

Details

The Incremental Static Generation (iSSG) pattern was introduced as an upgrade to SSG, to help solve the dynamic data problem and help static sites scale for large amounts of frequently changing data. iSSG allows you to update existing pages and add new ones by pre-rendering a subset of pages in the background even while fresh requests for pages are coming in.

Sample Code

iSSG works on two fronts to incrementally introduce updates to an existing static site after it has been built.

  1. Allows addition of new pages
  2. Allows updates to existing pages also known as Incremental Static "Re"generation
Adding New pages

The lazy loading concept is used to include new pages on the website after the build. This means that the new page is generated immediately on the first request. While the generation takes place, a fallback page or a loading indicator can be shown to the user on the front-end.

js
export async function getStaticPaths() {
  const products = await getProductsFromDatabase();

  const paths = products.map((product) => ({
     params: { id: product.id }
  }));

  // fallback: true means that the missing pages
  // will not 404, and instead can render a fallback.
  return { paths, fallback: true };
}

// params will contain the id for each generated page.
export async function getStaticProps({ params }) {
  return {
    props: {
      product: await getProductFromDatabase(params.id)
    }
  }
}

export default function Product({ product }) {
  const router = useRouter();

  if (router.isFallback) {
    return <div>Loading...</div>;
  }

  // Render product
}

Here, we have used fallback: true. Now if the page corresponding to a specific product is unavailable, we show a fallback version of the page, eg., a loading indicator as shown in the Product function above. Meanwhile, Next.js will generate the page in the background. Once it is generated, it will be cached and shown instead of the fallback page. The cached version of the page will now be shown to any subsequent visitors immediately upon request.

Update Existing pages

To re-render an existing page, a suitable timeout is defined for the page. This will ensure that the page is revalidated whenever the defined timeout period has elapsed. The user will continue to see the previous version of the page, till the page has finished revalidation. Thus, iSSG uses the stale-while-revalidate strategy where the user receives the cached or stale version while the revalidation takes place. The revalidation takes place completely in the background without the need for a full rebuild.

js
// This function runs at build time on the build server
export async function getStaticProps() {
  return {
    props: {
      products: await getProductsFromDatabase(),
      revalidate: 60, // This will force the page to revalidate after 60 seconds
    }
  }
}

// The page component receives products prop from getStaticProps at build time
export default function Products({ products }) {
  return (
    <>
      <h1>Products</h1>
      <ul>
        {products.map((product) => (
          <li key={product.id}>{product.name}</li>
        ))}
      </ul>
    </>
  )
}

The code to revalidate the page after 60 seconds is included in the getStaticProps() function. When a request comes in the available static page is served first. Every one minute the static page gets refreshed in the background with new data. Once generated, the new version of the static file becomes available and will be served for any new requests in the subsequent minute. This feature is available in Next.js 9.5 and above.

Advantages

iSSG provides all the advantages of SSG and then some more:

  1. Dynamic data: Its ability to support dynamic data without a need to rebuild the site.
  2. Speed: iSSG is at least as fast as SSG because data retrieval and rendering still takes place in the background. There is little processing required on the client or the server.
  3. Availability: A fairly recent version of any page will always be available online for users to access. Even if the regeneration fails in the background, the old version remains unaltered.
  4. Consistent: As the regeneration takes place on the server one page at a time, the load on the database and the backend is low and performance is consistent. As a result, there are no spikes in latency.
  5. Ease of Distribution: Just like SSG sites, iSSG sites can also be distributed through a network of CDN's used to serve pre-rendered web pages.

Source

Frequently asked questions

What does the Incremental Static Rendering AI skill do?

Teaches Incremental Static Regeneration (ISR) for updating static content post-build. Use when you have static pages that need periodic updates without a full site rebuild.

Why use Incremental Static Rendering on TypingMind?

Because you install it once and use it with any model. Incremental Static Rendering 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 Incremental Static Rendering in TypingMind?

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

Which AI models can use Incremental Static Rendering?

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 Incremental Static Rendering?

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

Is the Incremental Static Rendering 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇