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Signals Dart

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rodydavis
signals-dart

Advanced reactive state primitives, collections, mixins, and utilities of signals_core.

Overview

Publisherrodydavis
Repositorysignals.dart
Skill namesignals-dart
Stars
820
Forks
86
Bundled files
24
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.

  • 24 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by rodydavis on GitHub. Read the source before you install it.

Installation

Install the Signals Dart 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/skills/signals-dart
  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/skills/signals-dart .claude/skills/signals-dart
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Signals Dart 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 Signals Dart 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 Signals Dart 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.

Signals Core Advanced Primitives

This skill details advanced state management primitives, reactive collections, async features, and value options semantics in signals_core.

Related Documentation & Items

Core Primitives

Related FileDescription
signal.mdStandard writeable reactive state primitive containing value accessors, mutation mechanics, and basic custom options.
computed.mdLazy, memoized read-only derived state signal tracking reactive dependencies dynamically.
effect.mdSynchronous observer managing active subscription loops, dynamic dependency updates, and lifecycle teardown.
readonly.mdRead-only signal views ensuring unidirectional data access flows.
batch.mdTransactional state mutation blocks optimizing reactive computations and preventing rendering/recompute churn.

Asynchronous Operations

Related FileDescription
future_signal.mdSingle-evaluation asynchronous future state representation resolving the double-evaluation issue.
stream_signal.mdDynamic, lifecycle-safe, self-disposing stream listener mapping to async loading/data/error states.
async_signal.mdUnifying async state container mapping loading, success data, and failure error states cleanly.
computed_async.mdComposable asynchronous operations built over reactive signals with active race condition protection.
computed_from.mdState aggregator signal merging multiple async signals into a single unified result.

Reactive Collections

Related FileDescription
list_signal.mdOptimized list wrapper proxying standard operations to support element-level reactivity.
set_signal.mdCustom set wrapper ignoring duplicates and notifying on unique mutations.
map_signal.mdGranular map wrapper supporting isolated dictionary element lookup triggers.
iterable_signal.mdIterable wrapper mapping custom lazy traversals to dynamic subscription layers.
queue_signal.mdDouble-ended queue wrapper supporting optimized reactive pipeline structures.

Utility Primitives

Related FileDescription
linked_signal.mdWritable computed signals supporting manual override resets and custom equivalence checks.
tracked_signal.mdState history wrapper enabling clean out-of-the-box undo/redo mechanisms.
timer_signal.mdPeriodically emitting stopwatch signal with built-in pause, resume, and reset.
connect.mdDynamic event connector feeding multiple external data pipelines into single targets.

Core Primitives Quick Start Guide

The signals library exposes five core functions which are the building blocks to model any reactive business logic.

1. signal(initialValue)

Creates a new mutable signal container. You read a signal's value or subscribe to updates by accessing .value.

dart
final counter = signal(0);
print(counter.value); // 0
counter.value = 1; // Mutates value and schedules dependent updates
.peek()

Reads a signal's current value without subscribing to its mutations.

dart
final counter = signal(0);
final logCount = signal(0);

effect(() {
  print(counter.value);
  // Read using peek to avoid subscribing/triggering loop
  logCount.value = logCount.peek() + 1;
});

2. untracked(fn)

Executes a callback that reads signals without subscribing to any of them.

dart
final counter = signal(0);
final count = signal(0);

effect(() {
  print(counter.value);
  count.value = untracked(() => count.value + 1);
});

3. computed(fn)

Combines the values of multiple signals into a lazy, memoized derived signal.

dart
final first = signal('John');
final last = signal('Doe');
final fullName = computed(() => '${first.value} ${last.value}');
print(fullName.value); // John Doe

4. effect(fn)

Orchestrates immediate synchronous side effects by running a callback and subscribing to any signals read within it.

dart
final name = signal('Jane');
final dispose = effect(() => print('Hello $name'));
dispose(); // Clean up subscription

5. batch(fn)

Groups multiple signal writes into a single transaction, executing all dependent computed evaluations and effects exactly once at the end.

dart
final a = signal(0);
final b = signal(0);

batch(() {
  a.value = 1;
  b.value = 2;
});

1. Writable Computed Signal (linkedSignal)

A writable computed signal (LinkedSignal) provides a derived default value that can be overridden manually, resetting back to the derived default whenever its upstream dependencies mutate.

Usage

dart
// Basic reset shorthand:
final size = signal('M');
final selection = linkedSignal(() => size.value);

// Advanced structural comparison:
final user = signal((id: 1, name: 'Alice'));
final name = linkedSignal<String, ({int id, String name})>(
  () => user.value,
  options: LinkedSignalOptions(
    computation: (u, prev) {
      if (prev != null && prev.source.id == u.id) {
        return prev.value; // Retain manual override
      }
      return u.name; // Reset on ID changes
    },
  ),
);

2. Collections and Async Primitives

A. Collections

Always use specialized collections (listSignal, setSignal, mapSignal, etc.) to wrap collections to ensure optimized mutations (only triggering updates when elements actually change).

dart
final list = listSignal([1, 2], options: ListSignalOptions(autoDispose: true));
list.add(3); // Reacts to changes

B. Optimized FutureSignal

FutureSignal inherits directly from AsyncSignal to bypass Stream subscription overhead and tracks its dependencies manually via computed execution, executing future computations exactly once (avoiding double-evaluation loops).

dart
final search = signal('');
final results = futureSignal(() => fetchResults(search.value));

3. Options Classes Value Semantics

All signal option objects support full value semantics (copyWith, custom equality, and hashCode overrides) for flexible copying and accurate comparisons:

  • SignalOptions
  • ComputedOptions
  • AsyncSignalOptions
  • LinkedSignalOptions
  • ListSignalOptions / SetSignalOptions / MapSignalOptions

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Signals Dart AI skill do?

Advanced reactive state primitives, collections, mixins, and utilities of signals_core.

Why use Signals Dart on TypingMind?

Because you install it once and use it with any model. Signals Dart 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 Signals Dart in TypingMind?

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

Which AI models can use Signals Dart?

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 Signals Dart?

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

Is the Signals Dart 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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