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。
Parallel test execution orchestration with intelligent scheduling, retry logic, and comprehensive result aggregation.
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。
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.
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。