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.
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Generates durable-first tests — invariants, contracts, and property-based tests at boundaries that survive a reimplementation — plus unit, integration, and e2e coverage. Use when creating tests for new or changed code, filling coverage gaps, or migrating test suites between Jest, Vitest, and Playwright.
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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.
When AI makes code cheap to regenerate, the durable asset is the test that still holds after the implementation is thrown away and rewritten. Generate tests in durability tiers, and lead with the durable ones (ADR-113):
| Tier | What it is | Survives a rewrite? | When to write |
|---|---|---|---|
| Durable | Invariants, contracts/schemas, property-based tests, behavioral e2e — specified at the module's public boundary | Yes | Always — ≥1 per target |
| Ephemeral | Example-based unit tests, mock-call/interaction tests (TDD-London style) | No (coupled to impl) | For the red-green loop; label them, delete freely |
| Live | Monitoring / drift / cost assertions that run against reality | Continuously | For deployed behavior |
The language-swap heuristic: if reimplementing this module in another language would invalidate the test, the test is at the wrong boundary. Push it up a tier — assert on the observable contract, not on how the current code happens to work.
Every generated target MUST include at least one durable assertion (an invariant,
a contract check, or a property). Mock-call assertions (toHaveBeenCalledWith) are
ephemeral by definition — never let them be the only thing testing a target. Tag each
generated test // @tier durable|ephemeral|live so its lifetime is explicit.
These tests are graded as oracles: a good test passes against the real code and
fails against a seeded bug (mutant). A test that asserts nothing, or only the happy
path, kills no mutants and is rejected — see /mutation-testing and ADR-113.
# 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 — durable-first
Task("Generate durable-first tests", `
Analyze src/services/PaymentService.ts and generate Jest tests in tier order.
1. DURABLE (write these first, >=1 per public method):
- Invariants ("a refund never makes a balance negative")
- Contract/schema checks on inputs and outputs crossing the boundary
- Property-based tests (fast-check) over input ranges, not single examples
2. EPHEMERAL (the red-green loop; tag '// @tier ephemeral'):
- Specific happy-path examples and error paths
- Mock external dependencies ONLY — never let a mock-call assertion be the
only test for a method
Apply the language-swap check: if a Python rewrite of PaymentService would break
the test, move it up to the durable tier.
Output to tests/unit/services/PaymentService.test.ts
`, "qe-test-architect")
// Property + contract generation (first-class, not opt-in)
Task("Generate property and contract tests", `
For src/repositories/, derive:
- Properties: round-trip (write→read returns same), idempotence, ordering invariants
- Contracts: the repository interface schema, enforced on every CRUD result
These survive a storage-engine swap; example-based CRUD tests do not.
`, "qe-property-tester")
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:
durability: # the primary check (ADR-113)
durable_assertions_per_target: 1 # >=1 invariant/contract/property each
language_swap_safe: true # would survive a reimplementation
tier_tags_present: true # every test tagged durable|ephemeral|live
fault_detection: # do the tests actually catch bugs?
mutation_score_min: 0.6 # kill rate against seeded mutants
no_assertionless_tests: true # reject tests that kill 0 mutants
assertions:
minimum_per_test: 1
meaningful: true
isolation:
no_shared_state: true
proper_setup_teardown: true
naming:
descriptive: true
follows_convention: true
coverage: # necessary but NOT sufficient — see fault_detection
branches: 80
statements: 85
/mutation-testing to verify test quality/test-automation-strategy to choose framework and patterns/qe-coverage-analysis to find where tests are needed mostaqe health to diagnose, or aqe init to re-initialize if you get "Fleet not initialized"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.
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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.
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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.
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