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。
O(log n) sublinear coverage gap detection with risk-weighted analysis and intelligent test prioritization.
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。
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.
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。