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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Parallel test execution orchestration with intelligent scheduling, retry logic, and comprehensive result aggregation.
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 test execution capabilities including parallel orchestration, smart test selection, flaky test handling, and distributed execution across multiple environments.
# Run all tests with parallelization
aqe test run --parallel --workers 4
# Run affected tests only
aqe test run --affected --since HEAD~1
# Run with retry for flaky tests
aqe test run --retry 3 --retry-delay 1000
# Run specific test types
aqe test run --type unit,integration --exclude e2e
// Orchestrate test execution
Task("Execute test suite", `
Run the full test suite with:
- 4 parallel workers
- Retry flaky tests up to 3 times
- Generate JUnit report
- Fail fast on critical tests
Report results and any failures.
`, "qe-test-executor")
// Smart test selection
Task("Run affected tests", `
Analyze changes in PR #123 and:
- Identify affected test files
- Run only relevant tests
- Include integration tests for changed modules
- Report coverage delta
`, "qe-test-selector")
await testExecutor.runParallel({
suites: ['unit', 'integration'],
workers: 4,
distribution: 'by-file', // or 'by-test', 'by-duration'
isolation: 'process',
sharding: {
enabled: true,
total: 4,
index: process.env.SHARD_INDEX
}
});
await testExecutor.runAffected({
changes: gitChanges,
selection: {
direct: true, // Tests for changed files
transitive: true, // Tests for dependents
integration: true // Integration tests touching changed code
},
fallback: 'full-suite' // If analysis fails
});
await testExecutor.handleFlaky({
detection: {
enabled: true,
threshold: 0.1, // 10% flake rate
window: 100 // Last 100 runs
},
strategy: {
retry: 3,
quarantine: true,
notify: ['#flaky-tests']
}
});
execution:
parallel:
workers: auto # CPU cores - 1
timeout: 30000
bail: false
retry:
count: 2
delay: 1000
only_failed: true
reporting:
formats: [junit, json, html]
include_timing: true
include_logs: true
environments:
- name: node-18
image: node:18-alpine
- name: node-20
image: node:20-alpine
# GitHub Actions example
test:
runs-on: ubuntu-latest
strategy:
matrix:
shard: [1, 2, 3, 4]
steps:
- uses: actions/checkout@v4
- name: Run tests
run: |
aqe test run \
--shard ${{ matrix.shard }}/4 \
--parallel \
--report junit
- name: Upload results
uses: actions/upload-artifact@v4
with:
name: test-results-${{ matrix.shard }}
path: reports/
interface ExecutionResults {
summary: {
total: number;
passed: number;
failed: number;
skipped: number;
flaky: number;
duration: number;
};
shards: ShardResult[];
failures: TestFailure[];
flakyTests: FlakyTest[];
coverage: CoverageReport;
timing: TimingAnalysis;
}
Primary Agents: qe-test-executor, qe-test-selector, qe-flaky-detector Coordinator: qe-test-execution-coordinator Related Skills: qe-test-generation, qe-coverage-analysis
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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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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。