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Jaspr Pre Rendering And Hydration

Community
rodydavis
jaspr-pre-rendering-and-hydration

Use when working in a **static** or **server** mode Jaspr project. Covers SSR/SSG, async data fetching, the @client annotation, hydration, and handling dual entrypoints (.server.dart vs .client.dart).

Overview

Publisherrodydavis
Repositorysignals.dart
Skill namejaspr-pre-rendering-and-hydration
Stars
820
Forks
86
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 rodydavis on GitHub. Read the source before you install it.

Installation

Install the Jaspr Pre Rendering And Hydration 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.
    https://github.com/rodydavis/signals.dart/tree/main/.agents/skills/jaspr-pre-rendering-and-hydration
  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/rodydavis/signals.dart.git /tmp/signals.dart
mkdir -p .claude/skills
cp -r /tmp/signals.dart/.agents/skills/jaspr-pre-rendering-and-hydration .claude/skills/jaspr-pre-rendering-and-hydration
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Jaspr Pre Rendering And Hydration 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 Jaspr Pre Rendering And Hydration 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 Jaspr Pre Rendering And Hydration 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.

This skill is relevant for static and server mode Jaspr projects. You can check the mode of a Jaspr project in pubspec.yaml under jaspr: mode: <mode>.

Core Rules for Pre-rendering (SSR/SSG)

Jaspr pre-renders components at request/build-time on the server. Interactive browser features require hydration.

  1. Dual Entrypoints:
    • Server: lib/main.server.dart -> Calls runApp(Document(body: MyApp()))
    • Client: lib/main.client.dart -> Calls runApp(ClientApp())
  2. Library Constraints:
    • Importing: You MUST NOT import dart:js_interop or package:web in any code imported by the server entrypoint.
    • Importing: You MUST NOT import dart:io or dart:ffi in any code imported by the client entrypoint.
    • Using: You can import package:universal_web in shared code, but you MUST wrap any access to its browser APIs inside an if (kIsWeb) check to prevent crashing during server-side rendering.
  3. Interactivity / Hydration: Components are server-rendered by default. To add client interactivity (e.g., event listeners, state, using browser APIs), you MUST annotate a component with @client.

The @client Annotation and ClientApp

When you annotate a component with @client, Jaspr renders its initial HTML on the server and "hydrates" it in the browser.

  • ClientApp() (called in the client entrypoint) automatically looks for and hydrates all @client components that were pre-rendered on the server.
  • Rule: Only the uppermost component of a subtree that needs client interactivity needs to be annotated with @client. Do not annotate child components in that subtree, they will automatically be hydrated as part of the parent.

Passing Data to @client Components

Use parameters of @client components to pass data from the server to the client. These are automatically serialized on the server and deserialized in the browser.

  • Rule 1: All parameters MUST be initializing fields (e.g., const MyClientComponent({required this.title});).
  • Rule 2: All parameters MUST be serializable. They must be primitive types (int, String, double, bool, List, Map) OR custom types that define @encoder and @decoder methods.
    • Parameters MUST NOT be functions, components, or any other non-serializable types.

Fetching Data on the Server (Async Components)

When pre-rendering in server or static mode, you often need to fetch data asynchronously (e.g., from a database or API) before rendering the HTML. Jaspr provides special server-only components to delay the build phase until the data is ready.

  • Rule 1: You MUST use AsyncStatelessComponent or AsyncBuilder to perform async work during the build phase.
  • Rule 2: These components MUST ONLY be used on the server (import package:jaspr/server.dart).
  • Rule 3: Once the data is fetched on the server, you should pass it to a @client component as a parameter if you need that data to be available for client-side interactivity.

Example Usage:

dart
import 'package:jaspr/server.dart'; // Note the server-only import!
import 'package:jaspr/dom.dart';

import 'my_interactive_button.dart';

// This component fetches data before server-rendering the HTML.
class MyServerComponent extends AsyncStatelessComponent {
  const MyServerComponent();

  @override
  Future<Component> build(BuildContext context) async {
    // 1. Fetch data asynchronously on the server
    final databaseCount = await loadCountFromDatabase();

    return div([
      .text('This is static.'),
      // 2. Pass the fetched data to the client component
      MyInteractiveButton(initialCount: databaseCount), 
    ]);
  }
}
dart
import 'package:jaspr/jaspr.dart';
import 'package:jaspr/dom.dart';

// This component runs in the browser (interactive).
@client
class MyInteractiveButton extends StatefulComponent {
  const MyInteractiveButton({this.initialCount = 0});

  final int initialCount;

  @override
  State<MyInteractiveButton> createState() => _MyInteractiveButtonState();
}

class _MyInteractiveButtonState extends State<MyInteractiveButton> {
  late int count = component.initialCount;

  @override
  Component build(BuildContext context) {
    return button(
      onClick: () => setState(() => count++),
      [.text('Clicked $count times')],
    );
  }
}

Frequently asked questions

What does the Jaspr Pre Rendering And Hydration AI skill do?

Use when working in a **static** or **server** mode Jaspr project. Covers SSR/SSG, async data fetching, the @client annotation, hydration, and handling dual entrypoints (.server.dart vs .client.dart).

Why use Jaspr Pre Rendering And Hydration on TypingMind?

Because you install it once and use it with any model. Jaspr Pre Rendering And Hydration 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 Jaspr Pre Rendering And Hydration in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rodydavis/signals.dart/tree/main/.agents/skills/jaspr-pre-rendering-and-hydration. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Jaspr Pre Rendering And Hydration?

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 Jaspr Pre Rendering And Hydration?

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

Is the Jaspr Pre Rendering And Hydration AI skill free?

Yes. It is published on GitHub by rodydavis 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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