Flutter Implement Json Serialization logo

Flutter Implement Json Serialization

OrganizationPopular
flutter
flutter-implement-json-serialization

Create model classes with `fromJson` and `toJson` methods using `dart:convert`. Use when manually mapping JSON keys to class properties for simple data structures.

Overview

Publisherflutter
Repositoryagent-plugins
Skill nameflutter-implement-json-serialization
Stars
3K
Forks
182
Bundled files
Instructions only
LicenseBSD-3-Clause
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 flutter on GitHub. Read the source before you install it.

Installation

Install the Flutter Implement Json Serialization 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/flutter/agent-plugins/tree/main/skills/flutter-implement-json-serialization
  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/flutter/agent-plugins.git /tmp/agent-plugins
mkdir -p .claude/skills
cp -r /tmp/agent-plugins/skills/flutter-implement-json-serialization .claude/skills/flutter-implement-json-serialization
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Flutter Implement Json Serialization 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 Flutter Implement Json Serialization 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 Flutter Implement Json Serialization 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.

Serializing JSON Manually in Flutter

Contents

Core Guidelines

  • Import dart:convert: Utilize Flutter's built-in dart:convert library for manual JSON encoding (jsonEncode) and decoding (jsonDecode).
  • Enforce Type Safety: Always cast the dynamic result of jsonDecode() to the expected type, typically Map<String, dynamic> for objects or List<dynamic> for arrays.
  • Encapsulate Serialization Logic: Define plain model classes containing properties corresponding to the JSON structure. Implement a fromJson factory constructor and a toJson method within the model.
  • Handle Background Parsing: If parsing large JSON documents (execution time > 16ms), offload the parsing logic to a separate isolate using Flutter's compute() function to prevent UI jank.
  • Throw Exceptions on Failure: When handling HTTP responses, throw an exception if the status code is not successful (e.g., not 200 OK or 201 Created). Do not return null.

Workflow: Implementing a Serializable Model

Use this checklist to implement manual JSON serialization for a data model.

Task Progress:

  • Define the plain model class with final properties.
  • Implement the factory Model.fromJson(Map<String, dynamic> json) constructor.
  • Implement the Map<String, dynamic> toJson() method.
  • Write unit tests for both serialization methods.
  • Run validator -> review type mismatch errors -> fix casting logic.
  1. Define the Model: Create a class with properties matching the JSON keys.
  2. Implement fromJson: Extract values from the Map and cast them to the appropriate Dart types. Use pattern matching or explicit casting.
  3. Implement toJson: Return a Map<String, dynamic> mapping the class properties back to their JSON string keys.
  4. Validate: Execute unit tests to ensure type safety, autocompletion, and compile-time exception handling function correctly.

Workflow: Fetching and Parsing JSON

Use this conditional workflow when retrieving and parsing JSON from a network request.

Task Progress:

  • Execute the HTTP request.
  • Validate the response status code.
  • Determine parsing strategy (Synchronous vs. Isolate).
  • Decode and map the JSON to the model.
  1. Execute Request: Use the http package to perform the network call.
  2. Validate Response:
    • If response.statusCode == 200 (or 201 for POST), proceed to parsing.
    • If the status code indicates failure, throw an Exception.
  3. Determine Parsing Strategy:
    • If parsing a small payload (e.g., a single object), parse synchronously on the main thread.
    • If parsing a large payload (e.g., an array of thousands of objects), use compute(parseFunction, response.body) to parse in a background isolate.
  4. Decode and Map: Pass the decoded JSON to your model's fromJson constructor.

Examples

High-Fidelity Model Implementation

dart
import 'dart:convert';

class User {
  final int id;
  final String name;
  final String email;

  const User({
    required this.id,
    required this.name,
    required this.email,
  });

  // Factory constructor for deserialization
  factory User.fromJson(Map<String, dynamic> json) {
    return switch (json) {
      {
        'id': int id,
        'name': String name,
        'email': String email,
      } => 
        User(
          id: id,
          name: name,
          email: email,
        ),
      _ => throw const FormatException('Failed to load User.'),
    };
  }

  // Method for serialization
  Map<String, dynamic> toJson() {
    return {
      'id': id,
      'name': name,
      'email': email,
    };
  }
}

Synchronous Parsing (Small Payload)

dart
import 'dart:convert';
import 'package:http/http.dart' as http;

Future<User> fetchUser(http.Client client, int userId) async {
  final response = await client.get(
    Uri.parse('https://api.example.com/users/$userId'),
    headers: {'Accept': 'application/json'},
  );

  if (response.statusCode == 200) {
    // Decode returns dynamic, cast to Map<String, dynamic>
    final Map<String, dynamic> jsonMap = jsonDecode(response.body) as Map<String, dynamic>;
    return User.fromJson(jsonMap);
  } else {
    throw Exception('Failed to load user');
  }
}

Background Parsing (Large Payload)

dart
import 'dart:convert';
import 'package:flutter/foundation.dart';
import 'package:http/http.dart' as http;

// Top-level function required for compute()
List<User> parseUsers(String responseBody) {
  final parsed = (jsonDecode(responseBody) as List<dynamic>).cast<Map<String, dynamic>>();
  return parsed.map<User>((json) => User.fromJson(json)).toList();
}

Future<List<User>> fetchUsers(http.Client client) async {
  final response = await client.get(
    Uri.parse('https://api.example.com/users'),
    headers: {'Accept': 'application/json'},
  );

  if (response.statusCode == 200) {
    // Offload expensive parsing to a background isolate
    return compute(parseUsers, response.body);
  } else {
    throw Exception('Failed to load users');
  }
}

Frequently asked questions

What does the Flutter Implement Json Serialization AI skill do?

Create model classes with `fromJson` and `toJson` methods using `dart:convert`. Use when manually mapping JSON keys to class properties for simple data structures.

Why use Flutter Implement Json Serialization on TypingMind?

Because you install it once and use it with any model. Flutter Implement Json Serialization 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 Flutter Implement Json Serialization in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/flutter/agent-plugins/tree/main/skills/flutter-implement-json-serialization. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Flutter Implement Json Serialization?

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 Flutter Implement Json Serialization?

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

Is the Flutter Implement Json Serialization AI skill free?

Yes. It is published on GitHub by flutter under the BSD-3-Clause 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 👇