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.
中文简介正在准备中。当前显示原文说明。
AI-powered test generation using pattern recognition, code analysis, and intelligent test synthesis for comprehensive test coverage.
中文简介正在准备中。当前显示原文说明。
实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
Guide the use of v3's AI-powered test generation capabilities including pattern-based test synthesis, multi-framework support, and intelligent test case derivation from code analysis.
# Generate unit tests for a file
aqe test generate --file src/services/UserService.ts --framework jest
# Generate tests with coverage target
aqe test generate --scope src/api/ --coverage 90 --type unit
# Generate integration tests
aqe test generate --file src/controllers/AuthController.ts --type integration
# Generate from patterns
aqe test generate --pattern repository --target src/repositories/
// Spawn test generation agents
Task("Generate unit tests", `
Analyze src/services/PaymentService.ts and generate comprehensive Jest tests.
Include:
- Happy path tests for all public methods
- Edge cases and boundary conditions
- Error handling scenarios
- Mock external dependencies
Output to tests/unit/services/PaymentService.test.ts
`, "qe-test-generator")
// Pattern-based generation
Task("Apply test patterns", `
Scan src/repositories/ and apply repository test pattern:
- CRUD operation tests
- Query builder tests
- Transaction tests
- Connection error handling
`, "qe-pattern-matcher")
await testGenerator.analyzeAndGenerate({
source: 'src/services/OrderService.ts',
analysis: {
methods: true,
branches: true,
dependencies: true,
errorPaths: true
},
output: {
framework: 'jest',
style: 'describe-it',
assertions: 'expect'
}
});
await testGenerator.applyPattern({
pattern: 'service-layer',
targets: ['src/services/*.ts'],
customizations: {
mockStrategy: 'jest.mock',
asyncHandling: 'async-await',
errorAssertion: 'toThrow'
}
});
await testGenerator.fillCoverageGaps({
coverageReport: 'coverage/lcov.info',
targetCoverage: 90,
prioritize: ['uncovered-branches', 'error-paths'],
maxTests: 50
});
| Framework | Unit | Integration | E2E | Mocking |
|---|---|---|---|---|
| Jest | ✅ | ✅ | ⚠️ | jest.mock |
| Vitest | ✅ | ✅ | ⚠️ | vi.mock |
| Mocha | ✅ | ✅ | ❌ | sinon |
| Pytest | ✅ | ✅ | ❌ | pytest-mock |
| JUnit | ✅ | ✅ | ❌ | Mockito |
quality_checks:
assertions:
minimum_per_test: 1
meaningful: true
isolation:
no_shared_state: true
proper_setup_teardown: true
naming:
descriptive: true
follows_convention: true
coverage:
branches: 80
statements: 85
Primary Agents: qe-test-generator, qe-pattern-matcher, qe-test-architect Coordinator: qe-test-generation-coordinator Related Skills: qe-coverage-analysis, qe-test-execution
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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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
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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.
中文简介正在准备中。当前显示原文说明。
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.
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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.
中文简介正在准备中。当前显示原文说明。