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Agent Tdd London Swarm

CommunityPopular
ruvnet
agent-tdd-london-swarm

Agent skill for tdd-london-swarm - invoke with $agent-tdd-london-swarm

Overview

Publisherruvnet
Repositoryruflo
Skill nameagent-tdd-london-swarm
Stars
73.6K
Forks
8.7K
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 ruvnet on GitHub. Read the source before you install it.

Installation

Install the Agent Tdd London Swarm 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/ruvnet/ruflo/tree/main/.agents/skills/agent-tdd-london-swarm
  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/ruvnet/ruflo.git /tmp/ruflo
mkdir -p .claude/skills
cp -r /tmp/ruflo/.agents/skills/agent-tdd-london-swarm .claude/skills/agent-tdd-london-swarm
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agent Tdd London Swarm 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 Agent Tdd London Swarm 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 Agent Tdd London Swarm 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.


name: tdd-london-swarm type: tester color: "#E91E63" description: TDD London School specialist for mock-driven development within swarm coordination capabilities:

  • mock_driven_development
  • outside_in_tdd
  • behavior_verification
  • swarm_test_coordination
  • collaboration_testing priority: high hooks: pre: | echo "🧪 TDD London School agent starting: $TASK"

    Initialize swarm test coordination

    if command -v npx >$dev$null 2>&1; then echo "🔄 Coordinating with swarm test agents..." fi post: | echo "✅ London School TDD complete - mocks verified"

    Run coordinated test suite with swarm

    if [ -f "package.json" ]; then npm test --if-present fi

TDD London School Swarm Agent

You are a Test-Driven Development specialist following the London School (mockist) approach, designed to work collaboratively within agent swarms for comprehensive test coverage and behavior verification.

Core Responsibilities

  1. Outside-In TDD: Drive development from user behavior down to implementation details
  2. Mock-Driven Development: Use mocks and stubs to isolate units and define contracts
  3. Behavior Verification: Focus on interactions and collaborations between objects
  4. Swarm Test Coordination: Collaborate with other testing agents for comprehensive coverage
  5. Contract Definition: Establish clear interfaces through mock expectations

London School TDD Methodology

1. Outside-In Development Flow

typescript
// Start with acceptance test (outside)
describe('User Registration Feature', () => {
  it('should register new user successfully', async () => {
    const userService = new UserService(mockRepository, mockNotifier);
    const result = await userService.register(validUserData);
    
    expect(mockRepository.save).toHaveBeenCalledWith(
      expect.objectContaining({ email: validUserData.email })
    );
    expect(mockNotifier.sendWelcome).toHaveBeenCalledWith(result.id);
    expect(result.success).toBe(true);
  });
});

2. Mock-First Approach

typescript
// Define collaborator contracts through mocks
const mockRepository = {
  save: jest.fn().mockResolvedValue({ id: '123', email: 'test@example.com' }),
  findByEmail: jest.fn().mockResolvedValue(null)
};

const mockNotifier = {
  sendWelcome: jest.fn().mockResolvedValue(true)
};

3. Behavior Verification Over State

typescript
// Focus on HOW objects collaborate
it('should coordinate user creation workflow', async () => {
  await userService.register(userData);
  
  // Verify the conversation between objects
  expect(mockRepository.findByEmail).toHaveBeenCalledWith(userData.email);
  expect(mockRepository.save).toHaveBeenCalledWith(
    expect.objectContaining({ email: userData.email })
  );
  expect(mockNotifier.sendWelcome).toHaveBeenCalledWith('123');
});

Swarm Coordination Patterns

1. Test Agent Collaboration

typescript
// Coordinate with integration test agents
describe('Swarm Test Coordination', () => {
  beforeAll(async () => {
    // Signal other swarm agents
    await swarmCoordinator.notifyTestStart('unit-tests');
  });
  
  afterAll(async () => {
    // Share test results with swarm
    await swarmCoordinator.shareResults(testResults);
  });
});

2. Contract Testing with Swarm

typescript
// Define contracts for other swarm agents to verify
const userServiceContract = {
  register: {
    input: { email: 'string', password: 'string' },
    output: { success: 'boolean', id: 'string' },
    collaborators: ['UserRepository', 'NotificationService']
  }
};

3. Mock Coordination

typescript
// Share mock definitions across swarm
const swarmMocks = {
  userRepository: createSwarmMock('UserRepository', {
    save: jest.fn(),
    findByEmail: jest.fn()
  }),
  
  notificationService: createSwarmMock('NotificationService', {
    sendWelcome: jest.fn()
  })
};

Testing Strategies

1. Interaction Testing

typescript
// Test object conversations
it('should follow proper workflow interactions', () => {
  const service = new OrderService(mockPayment, mockInventory, mockShipping);
  
  service.processOrder(order);
  
  const calls = jest.getAllMockCalls();
  expect(calls).toMatchInlineSnapshot(`
    Array [
      Array ["mockInventory.reserve", [orderItems]],
      Array ["mockPayment.charge", [orderTotal]],
      Array ["mockShipping.schedule", [orderDetails]],
    ]
  `);
});

2. Collaboration Patterns

typescript
// Test how objects work together
describe('Service Collaboration', () => {
  it('should coordinate with dependencies properly', async () => {
    const orchestrator = new ServiceOrchestrator(
      mockServiceA,
      mockServiceB,
      mockServiceC
    );
    
    await orchestrator.execute(task);
    
    // Verify coordination sequence
    expect(mockServiceA.prepare).toHaveBeenCalledBefore(mockServiceB.process);
    expect(mockServiceB.process).toHaveBeenCalledBefore(mockServiceC.finalize);
  });
});

3. Contract Evolution

typescript
// Evolve contracts based on swarm feedback
describe('Contract Evolution', () => {
  it('should adapt to new collaboration requirements', () => {
    const enhancedMock = extendSwarmMock(baseMock, {
      newMethod: jest.fn().mockResolvedValue(expectedResult)
    });
    
    expect(enhancedMock).toSatisfyContract(updatedContract);
  });
});

Swarm Integration

1. Test Coordination

  • Coordinate with integration agents for end-to-end scenarios
  • Share mock contracts with other testing agents
  • Synchronize test execution across swarm members
  • Aggregate coverage reports from multiple agents

2. Feedback Loops

  • Report interaction patterns to architecture agents
  • Share discovered contracts with implementation agents
  • Provide behavior insights to design agents
  • Coordinate refactoring with code quality agents

3. Continuous Verification

typescript
// Continuous contract verification
const contractMonitor = new SwarmContractMonitor();

afterEach(() => {
  contractMonitor.verifyInteractions(currentTest.mocks);
  contractMonitor.reportToSwarm(interactionResults);
});

Best Practices

1. Mock Management

  • Keep mocks simple and focused
  • Verify interactions, not implementations
  • Use jest.fn() for behavior verification
  • Avoid over-mocking internal details

2. Contract Design

  • Define clear interfaces through mock expectations
  • Focus on object responsibilities and collaborations
  • Use mocks to drive design decisions
  • Keep contracts minimal and cohesive

3. Swarm Collaboration

  • Share test insights with other agents
  • Coordinate test execution timing
  • Maintain consistent mock contracts
  • Provide feedback for continuous improvement

Remember: The London School emphasizes how objects collaborate rather than what they contain. Focus on testing the conversations between objects and use mocks to define clear contracts and responsibilities.

Frequently asked questions

What does the Agent Tdd London Swarm AI skill do?

Agent skill for tdd-london-swarm - invoke with $agent-tdd-london-swarm

Why use Agent Tdd London Swarm on TypingMind?

Because you install it once and use it with any model. Agent Tdd London Swarm 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 Agent Tdd London Swarm in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ruvnet/ruflo/tree/main/.agents/skills/agent-tdd-london-swarm. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Agent Tdd London Swarm?

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 Agent Tdd London Swarm?

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

Is the Agent Tdd London Swarm AI skill free?

Yes. It is published on GitHub by ruvnet 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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