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Outcome KPI definition methodology - synthesizes Who Does What By How Much (Gothelf/Seiden), Running Lean (Maurya), and Measure What Matters (Doerr) into a practical framework for measurable outcome KPIs
Passed 21 of 22 test cases with the skill, and 12 of 22 without it.
and 14 more in the full report
9 fixed12 passed either way1 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 9 test cases the skill fixed. Same model, same prompt, with and without the skill.
We are finalizing the requirements for our self-service checkout renewal feature and need to define outcome-focused metrics instead of just tracking feature completion. Please help us build our `outcome-kpis.md` file. We need an inspirational objective statement, a structured breakdown of user behavior metrics, a metric hierarchy identifying our primary focal metric along with leading indicators and safety metrics, a measurement plan for tracking the data, and a formal validation hypothesis.
| Checked | Without skill | With skill |
|---|---|---|
| Includes a markdown table with columns: #, Who, Does What, By How Much, Baseline, Measured By, Type | Missed | Passed |
| Includes a measurement plan table with columns: KPI, Data Source, Collection Method, Frequency, Owner | Missed | Passed |
| Includes a hypothesis using the structure "We believe that... We will know this is true when..." | Missed | Passed |
The instructions your model reads when the skill loads.
"Doing stuff isn't the point. Achieving stuff is." -- Jeff Gothelf
Defines measurable outcome KPIs for user stories and features. Loaded during Phase 4 (Requirements Crafting) to produce outcome-kpis.md. Synthesizes three frameworks: customer-centric OKRs, lean metrics, and OKR methodology.
Primary template from Gothelf/Seiden. Every KPI answers five questions:
| Component | Question | Example |
|---|---|---|
| Who | Which user segment? | Returning customers with 2+ orders |
| Does What | What observable behavior changes? | Complete checkout without contacting support |
| By How Much | What is the measurable target? | 40% reduction in support tickets |
| Measured By | How do we collect the data? | Support ticket system + checkout analytics |
| Timeframe | When do we measure? | 30 days post-release, then weekly |
Formula: [Who] [Does what] [By how much]
Apply as litmus test: if a KPI cannot answer all five components, it measures an output (feature delivery), not an outcome (behavior change).
| Bad (Output) | Good (Outcome) |
|---|---|
| Launch mobile app v2 | Mobile users complete purchases 40% more often |
| Build recommendation engine | Users purchase from recommendations, increasing from 10% to 25% |
| Deploy onboarding redesign | New users complete onboarding within 24 hours 30% more often |
| Ship CSV export | Analysts resolve data questions without engineering support 60% of the time |
From Gothelf/Seiden: business results are lagging -- teams cannot directly influence them. Target leading indicators instead.
| Type | Definition | Examples | Actionable? |
|---|---|---|---|
| Lagging (Impact) | Business results already happened | Revenue, NPS, market share, churn rate | No -- too slow, too many variables |
| Leading (Outcome) | Behavior changes predicting business results | Purchase completion rate, feature adoption, retention | Yes -- teams can run experiments |
| Leading (Secondary) | Behaviors predicting primary leading indicators | Page visits, trial starts, onboarding steps completed | Yes -- most granular, fastest signal |
test cases passed, vs 12 / 22 without
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