Systematic test design with boundary value analysis, equivalence partitioning, decision tables, state transition testing, and combinatorial testing. Use when designing comprehensive test cases, reducing redundant tests, or ensuring systematic coverage.
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。
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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 designing test cases, select technique by input type:
// Auto-generate BVA tests
await Task("Generate BVA Tests", {
field: 'age',
dataType: 'integer',
constraints: { min: 18, max: 120 }
}, "qe-test-generator");
// Returns: 6 boundary test cases
// Auto-generate pairwise tests
await Task("Generate Pairwise Tests", {
parameters: {
browser: ['Chrome', 'Firefox', 'Safari'],
os: ['Windows', 'Mac', 'Linux'],
screen: ['Desktop', 'Tablet', 'Mobile']
}
}, "qe-test-generator");
// Returns: 9-12 tests (vs 27 full combination)
aqe/test-design/
├── bva-analysis/* - Boundary value tests
├── partitions/* - Equivalence partitions
├── decision-tables/* - Decision table tests
└── pairwise/* - Combinatorial reduction
const designFleet = await FleetManager.coordinate({
strategy: 'systematic-test-design',
agents: [
'qe-test-generator', // Apply design techniques
'qe-coverage-analyzer', // Analyze coverage
'qe-quality-analyzer' // Assess test quality
],
topology: 'sequential'
});
With Agents: qe-test-generator applies these techniques automatically, generating optimal test suites with maximum coverage and minimum redundancy. Agents identify boundaries, partitions, and combinations from code 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。