/qe-quality-assessment
Evaluates code quality through complexity analysis, lint results, code smell detection, and test health metrics. Use when assessing deployment readiness, configuring quality gates, scoring a codebase for release, or generating quality reports with pass/fail verdicts.
$ npx -y skills add proffesor-for-testing/agentic-qe --skill qe-quality-assessment --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/qe-quality-assessment
Context preview
The summary Claude sees to decide when to auto-load this skill.
Evaluates code quality through complexity analysis, lint results, code smell detection, and test health metrics. Use when assessing deployment readiness, configuring quality gates, scoring a codebase for release, or generating quality reports with pass/fail verdicts.
SKILL.md
qe-quality-assessment.SKILL.mdname: "qe-quality-assessment"
description: "Evaluates code quality through complexity analysis, lint results, code smell detection, and test health metrics. Use when assessing deployment readiness, configuring quality gates, scoring a codebase for release, or generating quality reports with pass/fail verdicts."
trust_tier: 3
validation:
schema_path: schemas/output.json
validator_path: scripts/validate-config.json
eval_path: evals/qe-quality-assessment.yaml
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 },
// Fault detection — coverage is necessary but NOT sufficient (ADR-113).
// A suite can hit 90% coverage and catch no bugs; mutation score does not lie.
mutationScore: { min: 0.6, blocking: false }, // warn-by-default; opt-in blocking
regenerability: { min: 0.5, blocking: false }, // Deletion Test: durable oracle backing per module
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'
}
});2a. Regenerability gate (ADR-113)
Coverage measures lines executed; **mutation score** measures whether the tests would *notice a bug*, and **regenerability** answers the Deletion Test — "if this module were deleted and regenerated, would a wrong rebuild be caught?" Regenerability = mutation score discounted by the durability tier backing the module (durable > live > ephemeral > none); ephemeral-only tests score low because they don't survive a reimplementation.
import { evaluateRegenerabilityGate } from '../../../src/feedback/regenerability-gate.js';
const verdict = evaluateRegenerabilityGate(moduleProfiles, {
mutationScoreMin: 0.6,
regenerabilityMin: 0.5,
mode: 'warn', // 'block' to fail CI; warn-by-default so adoption never breaks pipelines
});
// verdict.passed (thresholds met) · verdict.blocking (should fail CI) · verdict.failures[]Surface it next to coverage in reports: `Coverage 88% ✅ / Mutation 41% ⚠️ / Regenerability: ephemeral-only ⚠️`.
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: 0-59Quality 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:Read more
name: "qe-quality-assessment" description: "Evaluates code quality through complexity analysis, lint results, code smell detection, and test health metrics. Use when assessing deployment readiness, configuring quality gates, scoring a codebase for release, or generating quality reports with pass/fail verdicts." trust_tier: 3 validation: schema_path: schemas/output.json validator_path: scripts/validate-config.json eval_path: evals/qe-quality-assessment.yaml
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 },
// Fault detection — coverage is necessary but NOT sufficient (ADR-113).
// A suite can hit 90% coverage and catch no bugs; mutation score does not lie.
mutationScore: { min: 0.6, blocking: false }, // warn-by-default; opt-in blocking
regenerability: { min: 0.5, blocking: false }, // Deletion Test: durable oracle backing per module
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'
}
});2a. Regenerability gate (ADR-113)
Coverage measures lines executed; **mutation score** measures whether the tests would *notice a bug*, and **regenerability** answers the Deletion Test — "if this module were deleted and regenerated, would a wrong rebuild be caught?" Regenerability = mutation score discounted by the durability tier backing the module (durable > live > ephemeral > none); ephemeral-only tests score low because they don't survive a reimplementation.
import { evaluateRegenerabilityGate } from '../../../src/feedback/regenerability-gate.js';
const verdict = evaluateRegenerabilityGate(moduleProfiles, {
mutationScoreMin: 0.6,
regenerabilityMin: 0.5,
mode: 'warn', // 'block' to fail CI; warn-by-default so adoption never breaks pipelines
});
// verdict.passed (thresholds met) · verdict.blocking (should fail CI) · verdict.failures[]Surface it next to coverage in reports: `Coverage 88% ✅ / Mutation 41% ⚠️ / Regenerability: ephemeral-only ⚠️`.
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: 0-59Quality 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:AI-powered quality engineering agents that generate tests, find coverage gaps, detect flaky tests, and learn your codebase patterns — across 11 coding agent platforms.
Repo: proffesor-for-testing/agentic-qe
Other skills on agentic-qe.
- /a11y-ally
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.
Open skill - /accessibility-testing
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.
Open skill - /agentdb-advanced
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.
Open skill - /agentdb-learning
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.
Open skill - /agentdb-memory-patterns
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.
Open skill - /agentdb-optimization
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.
Open skill

