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.
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Apply effective software quality consultancy practices. Use when consulting, advising clients, or establishing consultancy workflows.
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实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
<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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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.
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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.
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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.
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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.
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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.
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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.
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