---

name: metric-tree-builder
description: "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."

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