Apply context-driven testing principles where practices are chosen based on project context, not universal 'best practices'. Use when making testing decisions, questioning dogma, or adapting approaches to specific project needs.
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<default_to_action> When making testing decisions or adapting approaches:
Quick Context Analysis:
Critical Success Factors:
| Heuristic | Application |
|---|---|
| SFDIPOT | Structure, Function, Data, Interfaces, Platform, Operations, Time |
| Oracles | Consistency with history, similar products, expectations, docs |
| Tours | Business District, Historical, Bad Neighborhood, Tourist, Museum |
Startup Context:
Enterprise Context:
Regulated (FDA/medical):
Fast-paced startup:
// Agent analyzes context and recommends approach
const context = await Task("Analyze Context", {
project: 'e-commerce-platform',
stage: 'startup',
constraints: ['timeline: tight', 'budget: limited'],
risks: ['payment-security', 'high-volume']
}, "qe-fleet-commander");
// Context-aware agent selection
// - qe-security-scanner (critical risk)
// - qe-performance-tester (high volume)
// - Skip: qe-visual-tester (low priority in startup context)
// Adaptive testing strategy
await Task("Generate Tests", {
context: 'startup',
focus: 'critical-paths-only',
depth: 'smoke-tests',
automation: 'minimal'
}, "qe-test-generator");
aqe/context-driven/
├── context-analysis/* - Project context snapshots
├── decisions/* - Testing decisions with rationale
├── discoveries/* - What was learned during testing
└── adaptations/* - How approach changed over time
const contextFleet = await FleetManager.coordinate({
strategy: 'context-driven',
context: {
type: 'greenfield-saas',
stage: 'growth',
compliance: 'gdpr-only'
},
agents: ['qe-test-generator', 'qe-security-scanner', 'qe-performance-tester'],
exclude: ['qe-visual-tester', 'qe-requirements-validator'] // Not priority
});
With Agents: Agents analyze context, adapt strategies, and learn what works in your situation. Use agents to scale context-driven thinking while maintaining human judgment for critical decisions.
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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。