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10 fichiers 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 Code Quality Orchestrator
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.
Quick Reference (30 seconds)
Core Capabilities :
TRUST 5 Validation : Testable, Readable, Unified, Secured, Trackable quality gatesProactive Analysis : Automated issue detection and improvement suggestionsBest Practices Enforcement : Context7-powered real-time standards validationMulti-Language Support : 25+ programming languages with specialized rulesEnterprise Integration : CI/CD pipelines, quality metrics, reporting
Key Patterns :
Quality Gate Pipeline → Automated validation with configurable thresholdsProactive Scanner → Continuous analysis with improvement recommendationsBest Practices Engine → Context7-driven standards enforcementQuality Metrics Dashboard → Comprehensive reporting and trend analysis
When to Use :
Code review automation and quality gate enforcement
Proactive code quality improvement and technical debt reduction
Enterprise coding standards enforcement and compliance validation
CI/CD pipeline integration with automated quality checks
Quick Access :
Implementation Guide
Getting Started
Basic Quality Validation :
quality_orchestrator = QualityOrchestrator(
trust5_enabled=True ,
proactive_analysis=True ,
best_practices_enforcement=True ,
context7_integration=True
await
"src/"
"python"
"javascript"
"typescript"
0.85
await
"src/"
0.90
10
Proactive Quality Analysis :
proactive_scanner = ProactiveQualityScanner(
context7_client=context7_client,
rule_engine=BestPracticesEngine()
)
improvements = await proactive_scanner.scan_codebase(
path="src/" ,
scan_types=["security" , "performance" , "maintainability" , "testing" ]
)
recommendations = await proactive_scanner.generate_recommendations(
issues=improvements,
priority="high" ,
auto_fix=True
)
Core Components
1. Quality Orchestration Engine 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"""
trust5_result = await self .trust5_validator.validate(
codebase=request.path,
thresholds=request.quality_thresholds
)
proactive_result = await self .proactive_scanner.scan(
codebase=request.path,
focus_areas=request.focus_areas
)
practices_result = await self .best_practices_engine.validate(
codebase=request.path,
languages=request.languages,
context7_docs=True
)
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:
Configuration and Customization
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
CI/CD Pipeline Integration
GitHub Actions Integration
Quality-as-Service REST API
Cross-Project Benchmarking
Advanced Patterns
1. Custom Quality Rules 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" ]
)
2. Machine Learning Quality Prediction ML-powered quality issue prediction using code feature extraction and predictive models.
3. Real-time Quality Monitoring Continuous quality monitoring with automated alerting for quality degradation and security vulnerabilities.
4. Cross-Project Quality Benchmarking Compare project quality metrics against similar projects in your industry.
Module Reference
Core Modules
Key Components by Module
TRUST5Validator - Five-pillar quality validationTestableValidator - Test coverage and qualitySecuredValidator - Security and OWASP complianceQuality gate pipeline integration
ProactiveQualityScanner - Automated issue detectionQualityPredictionEngine - ML-powered predictionsRealTimeQualityMonitor - Continuous monitoringPerformance and maintainability analysis
BestPracticesEngine - Standards validationContext7 integration for latest docs
Custom quality rules
Language-specific validators
CI/CD pipeline integration
GitHub Actions workflows
Quality-as-Service REST API
Cross-project benchmarking
Context7 Library Mappings Essential library mappings for quality analysis tools and frameworks.
Works Well With
core-planner - Quality requirements planningworkflow-tdd - TDD implementation validationsecurity-expert - Security vulnerability analysiscode-backend - Backend code qualitycode-frontend - Frontend code quality
moai-foundation-core - TRUST 5 framework referencemoai-tdd-implementation - TDD workflow validationmoai-security-owasp - Security compliancemoai-context7-integration - Context7 best practicesmoai-performance-optimization - Performance analysis
/moai:2-run - TDD validation integration/moai:3-sync - Documentation quality checks/moai:9-feedback - Quality improvement feedback
Quick Reference Summary Core 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
