القائمة
Enterprise code quality orchestrator with TRUST 5 validation, proactive analysis, and automated best practices enforcement
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| name | moai-foundation-quality |
| description | Enterprise code quality orchestrator with TRUST 5 validation, proactive analysis, and automated best practices enforcement |
| version | 2.0.0 |
| modularized | true |
| scripts_enabled | true |
| allowed-tools | Read, Write, Edit, Bash, Grep, Glob, TodoWrite, mcp__context7__resolve-library-id, mcp__context7__get-library-docs |
| last_updated | "2025-11-30T00:00:00.000Z" |
| compliance_score | 90 |
| auto_trigger_keywords | ["foundation","quality"] |
| scripts | [{"name":"quality-gate.sh","purpose":"Enterprise Code Quality Gate Script","type":"shell","command":"bash .claude/skills/moai-foundation-quality/scripts/quality-gate.sh","zero_context":false,"version":"1.0.0","last_updated":"2025-11-30T00:00:00.000Z"}] |
| color | red |
Enterprise-grade code quality management system that combines systematic code review, proactive improvement suggestions, and automated best practices enforcement. Provides comprehensive quality assurance through TRUST 5 framework validation with Context7 integration for real-time best practices.
Core Capabilities:
Key Patterns:
When to Use:
Quick Access:
Basic Quality Validation:
# Initialize quality orchestrator
quality_orchestrator = QualityOrchestrator(
trust5_enabled=True,
proactive_analysis=True,
best_practices_enforcement=True,
context7_integration=
)
result = quality_orchestrator.analyze_codebase(
path=,
languages=[, , ],
quality_threshold=
)
quality_gate = QualityGate()
validation_result = quality_gate.validate_trust5(
codebase_path=,
test_coverage_threshold=,
complexity_threshold=
)
Proactive Quality Analysis:
# Initialize proactive scanner
proactive_scanner = ProactiveQualityScanner(
context7_client=context7_client,
rule_engine=BestPracticesEngine()
)
# Scan for improvement opportunities
improvements = await proactive_scanner.scan_codebase(
path="src/",
scan_types=["security", "performance", "maintainability", "testing"]
)
# Generate improvement recommendations
recommendations = await proactive_scanner.generate_recommendations(
issues=improvements,
priority="high",
auto_fix=True
)
class QualityOrchestrator:
"""Enterprise quality orchestration with TRUST 5 framework"""
def __init__(self, config: QualityConfig):
self.trust5_validator = TRUST5Validator()
self.proactive_scanner = ProactiveScanner()
self.best_practices_engine = BestPracticesEngine()
self.context7_client = Context7Client()
self.metrics_collector = QualityMetricsCollector()
async def analyze_codebase(self, request: QualityAnalysisRequest) -> QualityResult:
"""Comprehensive codebase quality analysis"""
# Phase 1: TRUST 5 Validation
trust5_result = await self.trust5_validator.validate(
codebase=request.path,
thresholds=request.quality_thresholds
)
# Phase 2: Proactive Analysis
proactive_result = await self.proactive_scanner.scan(
codebase=request.path,
focus_areas=request.focus_areas
)
# Phase 3: Best Practices Check
practices_result = await self.best_practices_engine.validate(
codebase=request.path,
languages=request.languages,
context7_docs=True
)
# Phase 4: Metrics Collection
metrics = await self.metrics_collector.collect_comprehensive_metrics(
codebase=request.path,
analysis_results=[trust5_result, proactive_result, practices_result]
)
return QualityResult(
trust5_validation=trust5_result,
proactive_analysis=proactive_result,
best_practices=practices_result,
metrics=metrics,
overall_score=self._calculate_overall_quality_score([
trust5_result, proactive_result, practices_result
])
)
Detailed implementations:
Quality Configuration:
# quality-config.yaml
quality_orchestration:
trust5_framework:
enabled: true
thresholds:
overall: 0.85
testable: 0.90
readable: 0.80
unified: 0.85
secured: 0.90
trackable: 0.80
proactive_analysis:
enabled: true
scan_frequency: "daily"
focus_areas:
- "performance"
- "security"
- "maintainability"
- "technical_debt"
auto_fix:
enabled: true
severity_threshold: "medium"
confirmation_required: true
best_practices:
enabled: true
context7_integration: true
auto_update_standards: true
compliance_target: 0.85
language_rules:
python:
style_guide: "pep8"
formatter: "black"
linter: "ruff"
type_checker: "mypy"
javascript:
style_guide: "airbnb"
formatter: "prettier"
linter: "eslint"
typescript:
style_guide: "google"
formatter: "prettier"
linter: "eslint"
reporting:
enabled: true
metrics_retention_days: 90
trend_analysis: true
executive_dashboard: true
notifications:
quality_degradation: true
security_vulnerabilities: true
technical_debt_increase: true
Integration Examples:
See Integration Patterns for:
class CustomQualityRule:
"""Define custom quality validation rules"""
def __init__(self, name: str, validator: Callable, severity: str = "medium"):
self.name = name
self.validator = validator
self.severity = severity
async def validate(self, codebase: str) -> RuleResult:
"""Execute custom rule validation"""
try:
result = await self.validator(codebase)
return RuleResult(
rule_name=self.name,
passed=result.passed,
severity=self.severity,
details=result.details,
recommendations=result.recommendations
)
except Exception as e:
return RuleResult(
rule_name=self.name,
passed=False,
severity="error",
details={"error": str(e)},
recommendations=["Fix rule implementation"]
)
See Best Practices - Custom Rules for complete examples.
ML-powered quality issue prediction using code feature extraction and predictive models.
See Proactive Analysis - ML Prediction for implementation details.
Continuous quality monitoring with automated alerting for quality degradation and security vulnerabilities.
See Proactive Analysis - Real-time Monitoring for implementation details.
Compare project quality metrics against similar projects in your industry.
See Integration Patterns - Benchmarking for implementation details.
TRUST 5 Validation:
TRUST5Validator - Five-pillar quality validationTestableValidator - Test coverage and qualitySecuredValidator - Security and OWASP complianceProactive Analysis:
ProactiveQualityScanner - Automated issue detectionQualityPredictionEngine - ML-powered predictionsRealTimeQualityMonitor - Continuous monitoringBest Practices:
BestPracticesEngine - Standards validationIntegration Patterns:
Essential library mappings for quality analysis tools and frameworks.
See Best Practices - Library Mappings for complete list.
Agents:
Skills:
Commands:
/moai:2-run - TDD validation integration/moai:3-sync - Documentation quality checks/moai:9-feedback - Quality improvement feedbackCore Capabilities: TRUST 5 validation, proactive scanning, Context7-powered best practices, multi-language support, enterprise integration
Key Classes: QualityOrchestrator, TRUST5Validator, ProactiveQualityScanner, BestPracticesEngine, QualityMetricsCollector
Essential Methods: analyze_codebase(), validate_trust5(), scan_for_issues(), validate_best_practices(), generate_quality_report()
Integration Ready: CI/CD pipelines, GitHub Actions, REST APIs, real-time monitoring, cross-project benchmarking
Enterprise Features: Custom rules, ML prediction, real-time monitoring, benchmarking, comprehensive reporting
Quality Standards: OWASP compliance, TRUST 5 framework, Context7 integration, automated improvement recommendations