--- name: kpi-tracker-a0b6ebe41e7f description: KPI definition, calculation, and tracking skill for business intelligence dashboards allowed-tools: - Read - Write - Glob - Grep - Bash metadata: specialization: decision-intelligence domain: business category: visualization priority: high shared-candidate: true tools-libraries: - pandas - polars - great_expectations - pandera graph: domains: [domain:business-intelligence] skillAreas: [skill-area:data-analytics, skill-area:data-analysis, skill-area:business-analysis] roles: [role:data-analyst, role:business-analyst, role:bi-developer] --- # KPI Tracker ## Overview The KPI Tracker skill provides comprehensive capabilities for defining, calculating, and monitoring Key Performance Indicators. It supports the full KPI lifecycle from definition through tracking, alerting, and reporting for business intelligence and performance management. ## Capabilities - KPI formula definition and validation - Target and threshold management - Traffic light status calculation - Trend analysis and forecasting - Drill-down hierarchy configuration - Benchmark comparison - Variance analysis - Automated alert generation ## Used By Processes - KPI Framework Development - Executive Dashboard Development - Operational Reporting System Design ## Usage ### KPI Definition ```python # Define KPI structure kpi_definition = { "name": "Customer Acquisition Cost", "code": "CAC", "category": "Marketing", "description": "Total cost to acquire a new customer", "formula": "total_marketing_spend / new_customers_acquired", "unit": "currency", "polarity": "lower_is_better", "frequency": "monthly", "owner": "Marketing Director", "data_sources": [ {"name": "marketing_spend", "source": "finance_system", "table": "expenses"}, {"name": "new_customers", "source": "crm", "table": "customers"} ] } ``` ### Target Configuration ```python # Define targets and thresholds targets = { "kpi": "CAC", "period": "2024-Q1", "target": 150, "thresholds": { "green": {"max": 150}, "yellow": {"min": 150, "max": 200}, "red": {"min": 200} }, "benchmark": { "industry_average": 180, "best_in_class": 100, "previous_period": 175 } } ``` ### Hierarchy Configuration ```python # Define drill-down hierarchy hierarchy = { "kpi": "Revenue", "levels": [ {"name": "Total", "aggregation": "sum"}, {"name": "Region", "dimension": "geography", "aggregation": "sum"}, {"name": "Product Line", "dimension": "product", "aggregation": "sum"}, {"name": "Sales Rep", "dimension": "salesperson", "aggregation": "sum"} ] } ``` ### Alert Configuration ```python # Configure automated alerts alert_config = { "kpi": "CAC", "conditions": [ { "type": "threshold_breach", "threshold": "red", "consecutive_periods": 2, "notification": ["email", "slack"] }, { "type": "trend", "direction": "increasing", "periods": 3, "min_change_percent": 10, "notification": ["email"] }, { "type": "forecast_breach", "horizon": 3, "probability": 0.8, "notification": ["email", "dashboard"] } ] } ``` ## KPI Categories | Category | Example KPIs | |----------|-------------| | Financial | Revenue, Profit Margin, ROI, CAC, LTV | | Customer | NPS, Churn Rate, CSAT, Retention | | Operational | Cycle Time, Defect Rate, Utilization | | Growth | MRR Growth, User Growth, Market Share | | Efficiency | Cost per Unit, Revenue per Employee | ## Input Schema ```json { "operation": "define|calculate|track|alert", "kpi_definition": { "name": "string", "formula": "string", "unit": "string", "polarity": "higher_is_better|lower_is_better", "frequency": "string" }, "targets": { "value": "number", "thresholds": "object" }, "data": { "source": "string", "period": "string", "values": "object" }, "analysis_options": { "trend_analysis": "boolean", "forecast": "boolean", "variance_analysis": "boolean" } } ``` ## Output Schema ```json { "kpi_values": { "current_value": "number", "previous_value": "number", "target": "number", "variance": "number", "variance_percent": "number", "status": "green|yellow|red" }, "trend_analysis": { "direction": "improving|stable|declining", "change_percent": "number", "periods_analyzed": "number" }, "forecast": { "next_period": "number", "confidence_interval": ["number", "number"], "will_breach_target": "boolean" }, "drill_down": { "dimension_values": "object" }, "alerts": [ { "type": "string", "severity": "string", "message": "string" } ] } ``` ## Best Practices 1. Limit KPIs to 5-7 per dashboard (avoid metric overload) 2. Define clear ownership for each KPI 3. Set SMART targets (Specific, Measurable, Achievable, Relevant, Time-bound) 4. Include leading indicators, not just lagging 5. Validate formulas with business stakeholders 6. Document data lineage and calculation logic 7. Review and retire obsolete KPIs regularly ## Data Quality The skill validates: - Data completeness (missing values) - Data freshness (last update time) - Formula validity (division by zero, null handling) - Reasonable ranges (outlier detection) ## Integration Points - Feeds into Decision Visualization for dashboards - Connects with Data Storytelling for narratives - Supports Time Series Forecaster for predictions - Integrates with Alert systems for notifications --- Source attribution: a5c-ai Original project: https://github.com/a5c-ai/babysitter License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 Gabriel Moreira Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: a5c-ai Original project: https://github.com/a5c-ai/babysitter Attribution basis: Upstream repository explicitly recorded in package mirror path Fields of work: Data & Analytics, Marketing, Product Management, Sales Roles: Business Development, Data Analyst, Growth Marketer, Marketing Analyst, Product Manager Specialties: API, Database Tools: Great_Expectations, Pandas, Pandera, Polars Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: a08b183398d27dc403b3889ad6684d0351d43a74e4ea395701769fee2814ffca