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Mocktail

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evanca
mocktail

Use when creating mocks, stubbing methods, verifying interactions, registering fallback values, or choosing between mocks, fakes, and real objects (Mocktail).

Overview

Publisherevanca
Repositoryflutter-ai-rules
Skill namemocktail
Stars
651
Forks
67
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 evanca on GitHub. Read the source before you install it.

Installation

Install the Mocktail 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/evanca/flutter-ai-rules/tree/main/skills/mocktail
  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/evanca/flutter-ai-rules.git /tmp/flutter-ai-rules
mkdir -p .claude/skills
cp -r /tmp/flutter-ai-rules/skills/mocktail .claude/skills/mocktail
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Mocktail 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 Mocktail 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 Mocktail 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.

Mocktail Skill

This skill defines how to correctly use the mocktail package for mocking in Dart and Flutter tests.


1. Mock vs. Fake vs. Real Object

UseWhen
Real objectPrefer over mocks when practical.
Fake (extends Fake)Lightweight custom implementation; override only the methods you need. Prefer over mocks when you don't need interaction verification.
Mock (extends Mock)Only when you need to verify interactions (call counts, arguments) or stub dynamic responses.
  • Never add @override methods or implementations to a class extending Mock.
  • Only use mocks if your test has verify assertions; otherwise prefer real or fake objects.

2. Creating Mocks

dart
class MockMyService extends Mock implements MyService {}
class FakeMyEvent extends Fake implements MyEvent {}

No code generation required — unlike Mockito, Mocktail uses noSuchMethod at runtime.


3. Registering Fallback Values

Register fallback values before using custom types with argument matchers. Do this in setUpAll or at the top of your test:

dart
setUpAll(() {
  registerFallbackValue(FakeMyEvent());
});
  • Required for non-nullable custom types used with any(), captureAny(), or captureThat().
  • Register fallback values for any custom type used with argument matchers.

4. Stubbing

dart
final mock = MockMyService();

// Return a value
when(() => mock.fetchData()).thenReturn('result');

// Throw an error
when(() => mock.fetchData()).thenThrow(Exception('error'));

// Dynamic/async response
when(() => mock.fetchData()).thenAnswer((_) async => 'result');

// Future<void>
when(() => mock.doWork()).thenAnswer((_) async {});
  • Always stub async methods (returning Future or Future<void>) with thenAnswer.
  • Stub every method you expect to be called, even if it's not the focus of your test.

5. Named Parameters

Always include all named parameters in both when and verify calls. Use any(named: 'paramName') for those you don't care about:

dart
when(() => mock.fetch(
  id: any(named: 'id'),
  headers: any(named: 'headers'),
)).thenReturn(response);
  • If a method has default values for named parameters, Mocktail still expects them all to be matched.

6. Verification

dart
verify(() => mock.fetchData());             // called at least once
verifyNever(() => mock.fetchData());        // never called
verify(() => mock.fetchData()).called(2);   // called exactly twice

7. Argument Matchers

dart
// Any positional argument
when(() => mock.process(any())).thenReturn(true);

// Capture arguments for later assertions
final captured = verify(() => mock.process(captureAny())).captured;
print(captured.last);
  • Use any() for positional parameters when you don't care about the exact value.
  • Use captureThat() for conditional capturing.
  • When matching string output, be aware of what .toString() returns for the type.

References

Frequently asked questions

What does the Mocktail AI skill do?

Use when creating mocks, stubbing methods, verifying interactions, registering fallback values, or choosing between mocks, fakes, and real objects (Mocktail).

Why use Mocktail on TypingMind?

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

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

Which AI models can use Mocktail?

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 Mocktail?

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

Is the Mocktail AI skill free?

Yes. It is published on GitHub by evanca 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.

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