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Update Preact

Community
rodydavis
update-preact

Instructions and guidelines for updating the pinned Preact JS git submodule and synchronizing upstream updates to the Dart preact_signals package.

Overview

Publisherrodydavis
Repositorysignals.dart
Skill nameupdate-preact
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 Update Preact 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/update-preact
  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/update-preact .claude/skills/update-preact
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Update Preact 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 Update Preact 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 Update Preact 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.

Updating Preact JS Submodule and preact_signals Package

This guide details the systematic process for updating the pinned Preact.js Signals JS/TS git submodule (third_party/preactjs_signals) and porting its structural updates, algorithmic changes, and optimizations into the Dart preact_signals package.


1. Upstream Submodule Management

The upstream JavaScript/TypeScript implementation is tracked via a Git submodule located at third_party/preactjs_signals.

A. Initializing and Synchronizing the Submodule

If the submodule is not initialized, run:

bash
git submodule update --init --recursive

B. Pinned Submodule Version Upgrade

To upgrade the pinned submodule version:

  1. Navigate to the submodule directory:
    bash
    cd third_party/preactjs_signals
  2. Fetch all latest upstream branches and tags:
    bash
    git fetch --tags origin
  3. Checkout the desired release tag or commit hash:
    bash
    git checkout tags/v1.3.0 # or specific tag/commit hash
  4. Return to the root workspace directory and stage the updated submodule pointer:
    bash
    cd ...
    git add third_party/preactjs_signals

2. Reviewing Upstream Core Architecture Changes

Once the submodule is checked out to the new version, review the differences inside Preact's TypeScript core codebase:

Compare changes to locate:

  1. Algorithmic Optimizations: Refinements in graph tracking, batch processing, or lazy evaluation cycles.
  2. Bug Fixes: Handling of edge-case memory leaks, exception propagates, or subscription-tracking re-entrancy issues.
  3. API Contracts: New methods or configuration parameters added to Signal, Computed, or Effect.

3. Porting Changes to Dart preact_signals

All ported logic must reside in the packages/preact_signals package.

A. Code Synchronization

Modify the corresponding Dart implementation files to accurately align with the updated Preact JS algorithms:

[!IMPORTANT] Keep the Dart implementation highly semantic and idiomatic. While replicating Preact's structural logic is necessary to preserve reactive correctness and avoid cycles, leverage Dart's language features (such as late initializers, WeakReference, Expando, or Extension types) appropriately.

B. Porting Upstream Tests

Ensure any new test cases introduced in Preact core's test suite are ported to:


4. Package Release & Changelog Alignment

When changes are ported, increment package versions and document changes.

  1. Semantic Versioning: Increase the version in packages/preact_signals/pubspec.yaml based on whether changes are major (breaking changes), minor (features), or patch (bug fixes).
  2. Changelog Documentation: Document all changes and the specific upstream version tag synced in packages/preact_signals/CHANGELOG.md.

5. Verification Checklist

Execute these verification commands prior to staging code changes:

bash
# 1. Update monorepo dependencies
melos bootstrap

# 2. Run static analysis
melos run analyze

# 3. Format files
dart format .

# 4. Execute all unit tests and verify 100% success
melos run test

# 5. Review combined coverage and verify high metrics
melos run coverage

Frequently asked questions

What does the Update Preact AI skill do?

Instructions and guidelines for updating the pinned Preact JS git submodule and synchronizing upstream updates to the Dart preact_signals package.

Why use Update Preact on TypingMind?

Because you install it once and use it with any model. Update Preact 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 Update Preact in TypingMind?

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

Which AI models can use Update Preact?

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 Update Preact?

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

Is the Update Preact 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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