sf-agent-framework

# Quality Score Calculator Utility - Agent Instructions ## Purpose This utility provides instructions for AI agents to generate comprehensive quality score calculation solutions for Salesforce implementations, enabling organizations to measure and track quality across multiple dimensions. ## Agent Instructions ### When to Generate Quality Score Calculation Generate quality score calculation components when: - Overall system quality needs measurement - Code quality metrics require aggregation - Data quality scores need calculation - Process quality needs assessment - Documentation quality requires tracking - Testing quality needs evaluation - Compliance scores need computation ### Core Components to Generate #### 1. Quality Score Engine Generate calculation components that: - Calculate multi-dimensional quality scores - Apply weighted scoring algorithms - Normalize different metric types - Aggregate component scores - Track score trends - Generate quality grades Key quality dimensions: - Code quality - Data quality - Testing quality - Documentation quality - Security quality - Performance quality #### 2. Scoring Algorithm Framework Create algorithms for: - Linear scoring models - Weighted average calculations - Percentile-based scoring - Composite score generation - Trend-adjusted scoring - Benchmark comparisons #### 3. Quality Dashboard Generator Implement dashboards showing: - Overall quality score - Dimension breakdown - Trend analysis - Component scores - Improvement areas - Action recommendations ### Configuration Requirements #### Custom Objects ```yaml Quality_Dimension__c: - Name (Text) - Weight__c (Number) - Description__c (Text Area) - Active__c (Checkbox) - Calculation_Method__c (Picklist) - Target_Score__c (Number) Quality_Metric__c: - Quality_Dimension__c (Master-Detail) - Metric_Name__c (Text) - Current_Value__c (Number) - Target_Value__c (Number) - Weight__c (Number) - Unit__c (Text) Quality_Score__c: - Calculation_Date__c (DateTime) - Overall_Score__c (Number) - Code_Quality__c (Number) - Data_Quality__c (Number) - Test_Quality__c (Number) - Documentation_Quality__c (Number) - Security_Quality__c (Number) - Grade__c (Text) ``` ### Scoring Formulas to Implement #### Overall Quality Score ``` Overall Score = Σ(Dimension Score × Dimension Weight) / Σ(Dimension Weights) Where each Dimension Score = Σ(Metric Score × Metric Weight) / Σ(Metric Weights) ``` #### Code Quality Score ``` Code Quality = (Test Coverage × 0.3) + (Code Complexity × 0.2) + (Maintainability × 0.2) + (Technical Debt × 0.2) + (Standards Compliance × 0.1) ``` #### Data Quality Score ``` Data Quality = (Completeness × 0.3) + (Accuracy × 0.25) + (Consistency × 0.2) + (Uniqueness × 0.15) + (Timeliness × 0.1) ``` #### Test Quality Score ``` Test Quality = (Coverage Percentage × 0.4) + (Test Success Rate × 0.3) + (Test Execution Time × 0.2) + (Test Maintenance × 0.1) ``` ### Implementation Patterns #### Real-time Calculation Pattern 1. Capture metric updates 2. Recalculate affected dimensions 3. Update overall score 4. Check thresholds 5. Trigger notifications 6. Update dashboards #### Scheduled Calculation Pattern 1. Define calculation schedule 2. Collect all metrics 3. Apply scoring algorithms 4. Store historical scores 5. Generate reports 6. Track improvements #### Incremental Update Pattern 1. Track metric changes 2. Calculate score deltas 3. Update rolling averages 4. Maintain history 5. Optimize performance ### Quality Dimensions #### Code Quality Metrics - Test coverage percentage - Cyclomatic complexity - Code duplication - Technical debt ratio - Coding standards violations - Documentation coverage #### Data Quality Metrics - Record completeness - Field accuracy - Duplicate records - Data consistency - Referential integrity - Data age/freshness #### Process Quality Metrics - Process adherence - Cycle time - Error rates - Automation level - Efficiency metrics - Compliance rate #### Documentation Quality Metrics - Documentation coverage - Update frequency - Accuracy/relevance - Accessibility - Completeness - User feedback ### Scoring Algorithms #### Linear Scoring ``` Score = (Actual Value / Target Value) × 100 Capped at 100 for values exceeding target ``` #### Inverse Scoring ``` Score = (Target Value / Actual Value) × 100 For metrics where lower is better ``` #### Range-Based Scoring ``` Score = ((Value - Min) / (Max - Min)) × 100 For metrics with defined ranges ``` #### Threshold Scoring ``` If Value >= Excellent: Score = 100 Else If Value >= Good: Score = 80 Else If Value >= Fair: Score = 60 Else: Score = 40 ``` ### Dashboard Components to Generate #### Executive Quality Dashboard Display: - Overall quality score gauge - Quality grade (A-F) - Dimension radar chart - Trend line graph - YoY comparison - Improvement recommendations #### Detailed Quality Dashboard Show: - Dimension breakdowns - Metric-level scores - Historical trends - Benchmark comparisons - Root cause analysis - Action items #### Team Quality Dashboard Include: - Team-specific scores - Individual contributions - Improvement tracking - Best practices - Recognition metrics - Training needs ### Grade Calculation #### Grading Scale ``` A+: 95-100 (Exceptional) A: 90-94 (Excellent) B: 80-89 (Good) C: 70-79 (Satisfactory) D: 60-69 (Needs Improvement) F: <60 (Failing) ``` #### Grade Modifiers - Consistent improvement: + - Declining trend: - - Meeting all targets: + - Critical failures: - ### Best Practices to Implement 1. **Scoring Design** - Use meaningful metrics - Balance dimensions - Set realistic targets - Regular calibration - Stakeholder buy-in 2. **Data Collection** - Automate collection - Validate accuracy - Handle missing data - Ensure consistency - Track lineage 3. **Communication** - Clear visualizations - Actionable insights - Regular updates - Improvement focus - Celebrate wins 4. **Continuous Improvement** - Review metrics regularly - Adjust weights - Update targets - Add new dimensions - Remove obsolete metrics ### Advanced Features to Consider 1. **Predictive Scoring** - Trend forecasting - Risk prediction - Improvement modeling - What-if analysis - Goal achievement probability 2. **Machine Learning Integration** - Anomaly detection - Pattern recognition - Weight optimization - Correlation analysis - Recommendation engine 3. **Gamification** - Achievement badges - Team competitions - Improvement challenges - Recognition system - Leaderboards ### Error Handling Instructions Handle these scenarios: 1. Missing metric data 2. Calculation errors 3. Invalid weights 4. Performance issues 5. Data quality problems Recovery strategies: - Default values - Historical averages - Partial calculations - Error notifications - Manual overrides ### Testing Requirements Generate test classes for: 1. Scoring algorithms 2. Weight calculations 3. Grade assignments 4. Trend analysis 5. Dashboard accuracy ### Success Metrics Track and measure: - Score accuracy - Calculation performance - User adoption - Quality improvements - Business impact - ROI demonstration