metric-tree-builder
Decompose a north-star metric into a driver tree — the inputs and sub-inputs that actually move it — so a team knows which levers to pull. Use when asked to build a metric tree, break down a north-star metric, map metric drivers, or find the inputs behind an output metric. Produces a hierarchical tree from the top metric down to actionable input metrics, with the relationships, the highest-leverage levers, and what to instrument.
git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills /tmp/metric-tree-builder && cp -r /tmp/metric-tree-builder/plugins/pm-data/skills/metric-tree-builder ~/.claude/skills/metric-tree-builderSKILL.md
# Metric Tree Builder Skill A north-star metric you can't decompose is a number you can't move. This skill breaks it into the multiplicative/additive drivers beneath it, down to metrics a team can actually act on — and points at the highest-leverage levers. ## Working from a brief Given a top metric and a rough business model, **build the full tree anyway**, inferring the standard driver structure for that model and marking assumptions. Never stop at one level; push down to input metrics someone owns. ## Required Inputs Ask for (if not already provided): - **The north-star / top metric** (e.g. weekly active revenue, MRR, GMV, activated users) - **Business model** (subscription, marketplace, ads, transactional, freemium) - **Where the team can act** (which teams own which surfaces) - **Current pain** (the metric is flat / dropping — optional, focuses the tree) ## Output Format ### 1. The decomposition Express the top metric as an equation of its drivers, e.g.: `Revenue = New customers × Avg first order + Retained customers × Repeat rate × AOV` Then break each driver down a level or two, until you reach **input metrics a team can directly influence** (e.g. signup conversion, activation rate, email open→click, time-to-value). Show it as an indented tree or a table: | Level | Metric | Driven by | Owner / lever | |---|---|---|---| | 0 | North star | — | | | 1 | Driver | sub-inputs | | | 2 | Input metric | actions | team | ### 2. Relationships Note where drivers are **multiplicative** (a small % gain compounds) vs **additive**, and any that trade off against each other. ### 3. Highest-leverage levers The 2–3 input metrics where a realistic improvement moves the north star most — and why (sensitivity × how movable it is). ### 4. Instrumentation gaps Which input metrics aren't being measured yet but should be, to make the tree usable. ## Quality Checks - [ ] The top metric is expressed as an actual equation of its drivers - [ ] The tree bottoms out in input metrics a team can act on, not more outputs - [ ] Multiplicative vs additive relationships are noted - [ ] Identifies the highest-leverage levers with reasoning - [ ] Flags metrics that need to be instrumented ## Anti-Patterns - A "tree" that's just a flat list of unrelated KPIs - Stopping at output metrics no one can directly move - Ignoring how drivers combine (treating everything as additive) - No view on which lever actually matters most
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