Individual skill
kpi-framework
Use when a team must decide what to measure before building anything — picking one north-star metric, separating leading input drivers from lagging outputs, adding guardrails so a number cannot be gamed, and setting a target that is not arbitrary. NOT the live dashboard that displays them (that is `dashboard`), NOT instrumenting the events (that is `analytics`), NOT the recurring board report (that is `reporting`).
skills/kpi-framework/SKILL.md
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- Nov 4, 2026
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- MIT
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- agent-skills Repository topic
- agentic-ai Repository topic
- ai-agents Repository topic
- ai-coding Repository topic
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- cli Repository topic
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- north-star-metric Declared skill tag
- leading-vs-lagging Declared skill tag
- input-metrics Declared skill tag
- guardrail-metrics Declared skill tag
- target-setting Declared skill tag
- vanity-metrics Declared skill tag
- kpi-tree Declared skill tag
- data Derived category
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SKILL.mdcore · 11,750 bytesSKILL.mdcore · 11,750 bytesSKILL.mdcore · 11,750 bytesSKILL.mdcore · 11,750 bytesevals/README.mddoc · 777 bytesevals/cases.yamlasset · 3,216 bytesreferences/definition-and-targets.mddoc · 3,529 bytesreferences/metric-catalog.mddoc · 4,734 bytes
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# KPI framework
You decide **what** to measure. You do not build the dashboard, you do not wire up the
events, you do not write the monthly report. Your deliverable is a **metric definition
document**: one north-star metric, a small set of input drivers that causally feed it,
paired guardrails, and a calibrated target with a baseline and a date.
Most measurement work fails upstream, before any chart exists. Teams instrument 40 KPIs
and none of them lead anywhere. They optimize a lagging output nobody can move. They
celebrate a vanity number. They set "double it this month" and watch it get gamed. Your
job is to kill those failures at the source by forcing four decisions:
1. **What single output predicts long-term value?** (the north star)
2. **Which 3-5 controllable inputs cause it?** (the driver set)