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。
Apply XP practices including pair programming, ensemble programming, continuous integration, and sustainable pace. Use when implementing agile development practices, improving team collaboration, or adopting technical excellence practices.
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。
<default_to_action> When applying XP practices:
Core XP Practices (Prioritized):
| Practice | Start Here | Why First |
|---|---|---|
| TDD | ✅ Yes | Foundation for everything |
| Continuous Integration | ✅ Yes | Fast feedback |
| Pair Programming | ✅ Yes | Knowledge sharing |
| Collective Ownership | After CI+TDD | Needs safety net |
| Small Releases | After CI | Infrastructure dependent |
Pairing Quick Start:
Driver-Navigator (Classic):
- Driver: Writes code
- Navigator: Reviews, thinks ahead
- Rotate every 20-30 min
Ping-Pong (with TDD):
A: Write failing test
B: Make test pass + refactor
B: Write next failing test
A: Make test pass + refactor
</default_to_action>
| Context | Pair? | Why |
|---|---|---|
| Complex/risky code | ✅ Always | Needs multiple perspectives |
| New technology | ✅ Always | Learning accelerator |
| Onboarding | ✅ Always | Knowledge transfer |
| Critical bugs | ✅ Always | Two heads better |
| Simple tasks | ❌ Skip | Not worth overhead |
| Research spikes | ❌ Skip | Pair to discuss findings |
// Agent-human pair testing
const charter = "Test payment edge cases";
const tests = await Task("Generate Tests", { charter }, "qe-test-generator");
const reviewed = await human.review(tests);
await Task("Implement", { tests: reviewed }, "qe-test-generator");
// Continuous integration with agents
await Task("Risk Analysis", { prDiff }, "qe-regression-risk-analyzer");
await Task("Generate Tests", { changes: prDiff }, "qe-test-generator");
await Task("Execute Tests", { scope: 'affected' }, "qe-test-executor");
// Sustainable pace: agents handle grunt work
const agentWork = ['regression', 'data-generation', 'coverage-analysis'];
const humanWork = ['exploratory', 'risk-assessment', 'strategy'];
aqe/xp-practices/
├── pairing-sessions/* - Pair/ensemble session logs
├── ci-metrics/* - CI health metrics
├── velocity/* - Team velocity data
└── retrospectives/* - XP retrospective notes
const xpFleet = await FleetManager.coordinate({
strategy: 'xp-workflow',
agents: [
'qe-test-generator', // TDD support
'qe-test-executor', // CI integration
'qe-code-reviewer' // Collective ownership
],
topology: 'parallel'
});
XP practices work as a system - TDD enables collective ownership, CI enables small releases, pairing enables collective ownership. Don't cherry-pick randomly.
With Agents: Pair humans with agents. Agents handle repetitive work (regression, data generation, coverage analysis), humans provide judgment and creativity.
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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。