Use when running comprehensive WCAG accessibility audits with axe-core + pa11y + Lighthouse, generating context-aware remediation, or testing video accessibility. Supports 3-tier browser cascade with graceful degradation.
日本語の概要は準備中です。原文の説明を表示しています。
Apply London (mock-based) and Chicago (state-based) TDD schools. Use when practicing test-driven development or choosing testing style for your context.
日本語の概要は準備中です。原文の説明を表示しています。
実行スコア 未検証
インストール方法を見るインストールする前に、エージェントに与えられる指示の中身を確認できます。
<default_to_action> When implementing TDD or choosing testing style:
Quick Style Selection:
Critical Success Factors:
| Phase | Action | Discipline |
|---|---|---|
| Red | Write failing test | Verify it fails, check message is clear |
| Green | Minimal code to pass | No extra features, don't refactor |
| Refactor | Improve structure | Keep tests passing, no new functionality |
| Aspect | Chicago (Classicist) | London (Mockist) |
|---|---|---|
| Collaborators | Real objects | Mocks/stubs |
| Verification | State (assert outcomes) | Interaction (assert calls) |
| Isolation | Lower (integrated) | Higher (unit only) |
| Refactoring | Easier | Harder (mocks break) |
| Design feedback | Emerges from use | Explicit from start |
qe-test-generator: Generate tests in both schoolsqe-test-implementer: Implement minimal code (Green)qe-test-refactorer: Safe refactoring (Refactor)Philosophy: Test observable behavior through public API. Keep tests close to consumer usage.
// State verification - test final outcome
describe('Order', () => {
it('calculates total with tax', () => {
const order = new Order();
order.addItem(new Product('Widget', 10.00), 2);
order.addItem(new Product('Gadget', 15.00), 1);
expect(order.totalWithTax(0.10)).toBe(38.50);
});
});
When Chicago Shines:
Money, Email)Philosophy: Test each unit in isolation. Focus on how objects collaborate.
// Interaction verification - test method calls
describe('Order', () => {
it('delegates tax calculation', () => {
const taxCalculator = {
calculateTax: jest.fn().mockReturnValue(3.50)
};
const order = new Order(taxCalculator);
order.addItem({ price: 10 }, 2);
order.totalWithTax();
expect(taxCalculator.calculateTax).toHaveBeenCalledWith(20.00);
});
});
When London Shines:
// London for controller (external deps)
describe('OrderController', () => {
it('creates order and sends confirmation', async () => {
const orderService = { create: jest.fn().mockResolvedValue({ id: 123 }) };
const emailService = { send: jest.fn() };
const controller = new OrderController(orderService, emailService);
await controller.placeOrder(orderData);
expect(orderService.create).toHaveBeenCalledWith(orderData);
expect(emailService.send).toHaveBeenCalled();
});
});
// Chicago for domain logic
describe('OrderService', () => {
it('applies discount when threshold met', () => {
const service = new OrderService();
const order = service.create({ items: [...], total: 150 });
expect(order.discount).toBe(15); // 10% off > $100
});
});
// BAD - mocking everything
const product = { getName: jest.fn(), getPrice: jest.fn() };
Better: Only mock external dependencies.
// BAD - testing private methods
expect(order._calculateSubtotal).toHaveBeenCalled();
Better: Test public behavior only.
// Agent generates tests in both schools
await Task("Generate Tests", {
style: 'chicago', // or 'london'
target: 'src/domain/Order.ts',
focus: 'state-verification' // or 'collaboration-patterns'
}, "qe-test-generator");
// Agent-human ping-pong TDD
// Human writes test concept
const testIdea = "Order applies 10% discount when total > $100";
// Agent generates formal failing test (Red)
await Task("Create Failing Test", testIdea, "qe-test-generator");
// Human writes minimal code (Green)
// Agent suggests refactorings
await Task("Suggest Refactorings", { preserveTests: true }, "qe-test-refactorer");
aqe/tdd/
├── test-plan/* - TDD session plans
├── red-phase/* - Failing tests generated
├── green-phase/* - Implementation code
└── refactor-phase/* - Refactoring suggestions
const tddFleet = await FleetManager.coordinate({
workflow: 'red-green-refactor',
agents: {
testGenerator: 'qe-test-generator',
testExecutor: 'qe-test-executor',
qualityAnalyzer: 'qe-quality-analyzer'
},
mode: 'sequential'
});
Chicago: Test state, use real objects, refactor freely London: Test interactions, mock dependencies, design interfaces first Both: Write the test first, make it pass, refactor
Neither is "right." Choose based on context. Mix as needed. Goal: well-designed, tested code.
With Agents: Agents excel at generating tests, validating green phase, and suggesting refactorings. Use agents to maintain TDD discipline while humans focus on design decisions.
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Use when running comprehensive WCAG accessibility audits with axe-core + pa11y + Lighthouse, generating context-aware remediation, or testing video accessibility. Supports 3-tier browser cascade with graceful degradation.
日本語の概要は準備中です。原文の説明を表示しています。
WCAG 2.2 compliance testing, screen reader validation, and inclusive design verification. Use when ensuring legal compliance (ADA, Section 508), testing for disabilities, or building accessible applications for 1 billion disabled users globally.
日本語の概要は準備中です。原文の説明を表示しています。
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
日本語の概要は準備中です。原文の説明を表示しています。
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
日本語の概要は準備中です。原文の説明を表示しています。
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
日本語の概要は準備中です。原文の説明を表示しています。
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
日本語の概要は準備中です。原文の説明を表示しています。