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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Comprehensive quality gates, metrics analysis, and deployment readiness assessment for continuous quality assurance.
中文简介正在准备中。当前显示原文说明。
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。
Guide the use of v3's quality assessment capabilities including automated quality gates, metrics aggregation, trend analysis, and deployment readiness evaluation.
# Run quality assessment
aqe quality assess --scope src/ --gates all
# Check deployment readiness
aqe quality deploy-ready --environment production
# Generate quality report
aqe quality report --format dashboard --period 30d
# Compare quality between releases
aqe quality compare --from v1.0 --to v2.0
// Comprehensive quality assessment
Task("Assess code quality", `
Evaluate quality for src/:
- Code complexity (cyclomatic, cognitive)
- Test coverage and mutation score
- Security vulnerabilities
- Code smells and technical debt
- Documentation coverage
Generate quality score and recommendations.
`, "qe-quality-analyzer")
// Deployment readiness check
Task("Check deployment readiness", `
Evaluate if release v2.1.0 is ready for production:
- All tests passing
- Coverage thresholds met
- No critical vulnerabilities
- Performance benchmarks passed
- Documentation updated
Provide go/no-go recommendation.
`, "qe-deployment-advisor")
await qualityAnalyzer.assessCode({
scope: 'src/**/*.ts',
metrics: {
complexity: {
cyclomatic: { max: 15, warn: 10 },
cognitive: { max: 20, warn: 15 }
},
maintainability: {
index: { min: 65 },
duplication: { max: 3 } // percent
},
documentation: {
publicAPIs: { min: 80 },
complexity: { min: 70 }
}
}
});
await qualityGate.evaluate({
gates: {
coverage: { min: 80, blocking: true },
complexity: { max: 15, blocking: false },
vulnerabilities: { critical: 0, high: 0, blocking: true },
duplications: { max: 3, blocking: false },
techDebt: { maxRatio: 5, blocking: false }
},
action: {
onPass: 'proceed',
onFail: 'block-merge',
onWarn: 'notify'
}
});
await deploymentAdvisor.assess({
release: 'v2.1.0',
criteria: {
testing: {
unitTests: 'all-pass',
integrationTests: 'all-pass',
e2eTests: 'critical-pass',
performanceTests: 'baseline-met'
},
quality: {
coverage: 80,
noNewVulnerabilities: true,
noRegressions: true
},
documentation: {
changelog: true,
apiDocs: true,
releaseNotes: true
}
}
});
quality_score:
components:
test_coverage:
weight: 0.25
metrics: [statement, branch, function]
code_quality:
weight: 0.20
metrics: [complexity, maintainability, duplication]
security:
weight: 0.25
metrics: [vulnerabilities, dependencies]
reliability:
weight: 0.20
metrics: [bug_density, flaky_tests, error_rate]
documentation:
weight: 0.10
metrics: [api_coverage, readme, changelog]
scoring:
A: 90-100
B: 80-89
C: 70-79
D: 60-69
F: 0-59
interface QualityDashboard {
overallScore: number; // 0-100
grade: 'A' | 'B' | 'C' | 'D' | 'F';
dimensions: {
name: string;
score: number;
trend: 'improving' | 'stable' | 'declining';
issues: Issue[];
}[];
gates: {
name: string;
status: 'pass' | 'fail' | 'warn';
value: number;
threshold: number;
}[];
trends: {
period: string;
scores: number[];
alerts: Alert[];
};
recommendations: Recommendation[];
}
# Quality gate in pipeline
quality_check:
stage: verify
script:
- aqe quality assess --gates all --output report.json
rules:
- if: $CI_PIPELINE_SOURCE == "merge_request_event"
artifacts:
reports:
quality: report.json
allow_failure:
exit_codes:
- 1 # Warnings only
Primary Agents: qe-quality-analyzer, qe-deployment-advisor, qe-metrics-collector Coordinator: qe-quality-coordinator Related Skills: qe-coverage-analysis, qe-security-compliance
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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.
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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.
中文简介正在准备中。当前显示原文说明。