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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.

5. The tree, drawn

Also render the decomposition as a Mermaid flowchart so the structure is visible at a glance (it renders live in the playground and exports as PNG/SVG). North star at the top, drivers below, input metrics as leaves; keep labels short.

flowchart TD
    NS[North star] --> D1[Driver A]
    NS --> D2[Driver B]
    D1 --> I1[Input metric]
    D1 --> I2[Input metric]
    D2 --> I3[Input metric]

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

Example Trigger Phrases

• "Build a metric tree."
• "Break down a north-star metric."
• "Map metric drivers."
• "Find the inputs behind an output metric."