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kpi-tracker KPI definition, calculation, and tracking skill for business intelligence dashboards
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Baixar Zip Baixando... Mais deste repositório Reference for querying the Atlas knowledge graph through its MCP tools — the SECONDARY enrichment/comparison layer that adds best-practice context to systems you have ALREADY scanned from your real sources (`az`, repos, dirs). Use when you need to look up nodes, edges, kinds, clusters, stats, or wiki pages in Atlas to compare against your real inventory. (atlas graph, query atlas, atlas mcp, search the graph, graph neighbors, atlas record, atlas kinds, enrichment layer)
Atlas turns your STATED NEED into a real systems atlas by SCANNING your actual sources (Azure via `az`, git repos, local dirs) and process/data mining them, THEN enriching against the Atlas knowledge graph. Use this skill when asked to inventory/map your real systems, scan your cloud + repos + directories, mine the real processes or data they contain, or collect their real constraints/gotchas. (atlas, scan my systems, inventory our azure account, map my repos, real systems atlas, process mining, data mining, collect nuances, system discovery)
assimilate-popular-workflows This skill should be used when the user asks to "find skills in the wild", "assimilate popular workflows", "discover SKILL.md files in repos", "research external skills", "find workflow patterns", "survey the skill landscape", "what skills exist out there", or wants to investigate public repositories for extractable processes, babysitter plugins, and reusable procedural insights. Searches GitHub for SKILL.md files, classifies repos by archetype, and maintains structured research under docs/reference-repos/.
Ocupações relacionadas SOC
Baseado na classificação ocupacional SOC
name kpi-tracker 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
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
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
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
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 {
"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 {
"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
Limit KPIs to 5-7 per dashboard (avoid metric overload)
Define clear ownership for each KPI
Set SMART targets (Specific, Measurable, Achievable, Relevant, Time-bound)
Include leading indicators, not just lagging
Validate formulas with business stakeholders
Document data lineage and calculation logic
Review and retire obsolete KPIs regularly
Data Quality
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
