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 effective software quality consultancy practices. Use when consulting, advising clients, or establishing consultancy workflows.
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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<default_to_action> When consulting on quality:
Engagement Types:
Key Questions:
What they say: "We need test automation" What they mean: "Manual testing is too slow/expensive"
Discovery: How long is regression? What's deployment frequency?
Typical Finding: They need faster feedback, not "automation"
Recommendation:
What they say: "We have too many bugs" What they mean: "Something is broken but we don't know what"
Discovery: Where found? What types? When introduced?
Typical Finding: No test strategy, testing too late, poor feedback loops
Recommendation:
What they say: "Growing fast, quality can't keep up" What they mean: "Can't hire enough QA fast enough"
Discovery: Current QA:Dev ratio? Where's QA spending time?
Typical Finding: QA is bottleneck - manual regression, gatekeeping
Recommendation:
"We already tried that" → "Tell me what you tried and what didn't work" (learn from their experience)
"Our context is special" → "Help me understand what makes yours special" (they might be right, or making excuses)
"We don't have budget/time" → "What's the cost of not fixing this? Let's start small" (show ROI)
"That won't work here" → "What specific constraints? Let's adapt" (find what WILL work)
// Automated codebase assessment
const assessment = await Task("Assess Codebase", {
scope: 'client-project/',
depth: 'comprehensive',
reportFormat: 'executive-summary'
}, "qe-quality-analyzer");
// Returns: { qualityScore, testCoverage, technicalDebt, recommendations }
// ROI analysis for quality initiatives
const roi = await Task("Calculate ROI", {
currentState: { defectEscapeRate: 0.15, mttr: 48 },
proposedImprovements: ['test-automation', 'ci-cd-pipeline'],
timeframe: '6-months'
}, "qe-quality-analyzer");
// Returns: { estimatedCost, estimatedSavings, paybackPeriod }
aqe/consultancy/
├── assessments/* - Client assessments
├── recommendations/* - Prioritized recommendations
├── roi-analysis/* - ROI calculations
└── progress/* - Implementation tracking
const consultingFleet = await FleetManager.coordinate({
strategy: 'client-engagement',
agents: [
'qe-quality-analyzer', // Assess current state
'qe-regression-risk-analyzer', // Risk assessment
'qe-quality-gate', // Define quality gates
'qe-deployment-readiness' // Deployment maturity
],
topology: 'hierarchical'
});
Good consulting is about empowering teams, not creating dependency. Your success is measured by them not needing you anymore - while still wanting to work with you again.
Best compliment: "We've got this now, but when we tackle X next year, we're calling you."
Be honest. Be helpful. Be context-driven. Leave them better.
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ログインなしで書き込めます。使ってみた感想、質問、動かなかった環境などを気軽にどうぞ。
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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。