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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O(log n) sublinear coverage gap detection with risk-weighted analysis and intelligent test prioritization.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Guide the use of v3's advanced coverage analysis capabilities including sublinear gap detection algorithms, risk-weighted coverage scoring, and intelligent test prioritization based on code criticality.
# Analyze coverage with gap detection
aqe coverage analyze --source src/ --tests tests/
# Find high-risk uncovered code
aqe coverage gaps --risk-weighted --threshold 80
# Generate coverage report
aqe coverage report --format html --output coverage-report/
# Compare coverage between branches
aqe coverage diff --base main --head feature-branch
// Comprehensive coverage analysis
Task("Analyze coverage gaps", `
Perform O(log n) coverage analysis on src/:
- Calculate statement, branch, function coverage
- Identify uncovered critical paths
- Risk-weight gaps by code complexity and change frequency
- Recommend tests to write for maximum coverage impact
`, "qe-coverage-specialist")
// Risk-based prioritization
Task("Prioritize coverage effort", `
Analyze coverage gaps and prioritize by:
- Business criticality (payment, auth, data)
- Code complexity (cyclomatic > 10)
- Recent bug history
- Change frequency
Output prioritized list of files needing tests.
`, "qe-coverage-analyzer")
await coverageAnalyzer.detectGaps({
algorithm: 'sublinear', // O(log n) complexity
source: 'src/**/*.ts',
metrics: ['statement', 'branch', 'function'],
sampling: {
enabled: true,
confidence: 0.95,
maxSamples: 1000
}
});
await coverageAnalyzer.riskWeightedAnalysis({
coverage: coverageReport,
riskFactors: {
complexity: { weight: 0.3, threshold: 10 },
changeFrequency: { weight: 0.25, window: '90d' },
bugHistory: { weight: 0.25, window: '180d' },
criticality: { weight: 0.2, tags: ['payment', 'auth'] }
},
output: {
riskScore: true,
prioritizedGaps: true
}
});
await coverageAnalyzer.diffCoverage({
base: 'main',
head: 'feature-branch',
requirements: {
newCode: 80, // New code must have 80% coverage
modifiedCode: 'maintain', // Don't decrease existing
deletedCode: 'ignore'
}
});
thresholds:
global:
statements: 80
branches: 75
functions: 85
lines: 80
per_file:
min_statements: 70
critical_paths: 90
new_code:
statements: 85
branches: 80
exceptions:
- path: "src/migrations/**"
reason: "Database migrations"
- path: "src/generated/**"
reason: "Auto-generated code"
interface CoverageAnalysis {
summary: {
statements: { covered: number; total: number; percentage: number };
branches: { covered: number; total: number; percentage: number };
functions: { covered: number; total: number; percentage: number };
};
gaps: {
file: string;
uncoveredLines: number[];
uncoveredBranches: BranchInfo[];
riskScore: number;
suggestedTests: string[];
}[];
trends: {
period: string;
coverageChange: number;
newGaps: number;
closedGaps: number;
};
recommendations: {
priority: 'critical' | 'high' | 'medium' | 'low';
file: string;
action: string;
expectedImpact: number;
}[];
}
quality_gates:
coverage:
block_merge:
- new_code_coverage < 80
- coverage_regression > 5
- critical_path_uncovered
warn:
- overall_coverage < 75
- branch_coverage < 70
metrics:
- track_trends: true
- alert_on_decline: 3 # consecutive PRs
Primary Agents: qe-coverage-specialist, qe-coverage-analyzer, qe-gap-detector Coordinator: qe-coverage-coordinator Related Skills: qe-test-generation, qe-quality-assessment
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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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。