Skip to content
cccskills
FreePublished on GitHub

qe-quality-assessment

Comprehensive quality gates, metrics analysis, and deployment readiness assessment for continuous quality assurance.

The English summary is being prepared. Showing the original description.

proffesor-for-testing/agentic-qe515 stars(GitHub 515 + cccskills 0)

Run score Not tested

See how to install

Files included (1)

  • SKILL.md4.5 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

QE Quality Assessment

Purpose

Guide the use of v3's quality assessment capabilities including automated quality gates, metrics aggregation, trend analysis, and deployment readiness evaluation.

Activation

  • When evaluating code quality
  • When setting up quality gates
  • When assessing deployment readiness
  • When tracking quality metrics
  • When generating quality reports

Quick Start

# 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

Agent Workflow

// 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")

Quality Dimensions

1. Code Quality Metrics

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 }
    }
  }
});

2. Quality Gates

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'
  }
});

3. Deployment Readiness

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 Calculation

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

Quality Dashboard

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[];
}

CI/CD Integration

# 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

Coordination

Primary Agents: qe-quality-analyzer, qe-deployment-advisor, qe-metrics-collector Coordinator: qe-quality-coordinator Related Skills: qe-coverage-analysis, qe-security-compliance

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

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.

The English summary is being prepared. Showing the original description.

proffesor-for-testing/agentic-qe515Updated Oct 26, 2026

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.

The English summary is being prepared. Showing the original description.

proffesor-for-testing/agentic-qe515Updated Oct 26, 2026

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.

The English summary is being prepared. Showing the original description.

proffesor-for-testing/agentic-qe515Updated Oct 26, 2026

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.

The English summary is being prepared. Showing the original description.

proffesor-for-testing/agentic-qe515Updated Oct 26, 2026

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.

The English summary is being prepared. Showing the original description.

proffesor-for-testing/agentic-qe515Updated Oct 26, 2026

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.

The English summary is being prepared. Showing the original description.

proffesor-for-testing/agentic-qe515Updated Oct 26, 2026

See all skills by proffesor-for-testing

Report a problem with this skill