metrics-tree_skill

This skill guides you in defining a North Star, decomposing metrics, and prioritizing experiments to drive measurable product outcomes.

30

GitHub Stars

1

Bundled Files

2 months ago

Catalog Refreshed

4 months ago

First Indexed

Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

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npx veilstrat add skill lyndonkl/claude --skill metrics-tree

  • SKILL.md9.9 KB

Overview

This skill helps teams define a clear North Star metric and decompose it into actionable sub-metrics and leading indicators. It maps strategy to measurable outcomes, identifies which metrics to move through experiments, and clarifies causal relationships between metrics. Use it to prioritize metric-improvement opportunities and align teams around what truly drives your business.

How this skill works

It guides you to pick a single North Star, break it into 3–5 input drivers, and map specific user actions that move those drivers. You then identify early (leading) and late (lagging) indicators, score opportunities by impact/confidence/ease, and select experiments to validate causal links. The process emphasizes measurable behaviors, causal testing, and iterative refinement based on data.

When to use it

  • Setting or validating a North Star metric for product or business strategy
  • Decomposing complex KPIs into driver metrics and user actions
  • Identifying leading indicators that predict future performance
  • Prioritizing experiments to move high-leverage metrics
  • Diagnosing why a key metric is declining or stagnating

Best practices

  • Choose a North Star that captures delivered value, predicts revenue, and is team-controllable
  • Limit decomposition to 3–5 inputs and 3–5 actions per input to stay actionable
  • Distinguish leading vs lagging indicators and test timing empirically
  • Rank opportunities by impact, confidence, and ease; run 1–3 focused experiments
  • Guard against vanity metrics and unintended gaming by adding quality/safety metrics

Example use cases

  • A SaaS PM defines MRR drivers: new signups, conversion rate, and retention, then maps onboarding actions to conversion
  • A marketplace decomposes GMV into active buyers, frequency, and average order value to prioritize growth experiments
  • A social product targets time spent by improving onboarding activation and first-session content creation
  • A product team investigating a drop in weekly active users traces issues through activation, retention, and notification delivery
  • Leadership aligning multiple teams around one North Star and distributing ownership of input metrics

FAQ

Keep depth to about 3–4 levels (North Star → inputs → actions → leading indicators) to avoid analysis paralysis while remaining actionable.

How do I know if an indicator is leading or just correlated?

Test timing and causality: check whether changes in the indicator reliably precede North Star movement and validate with experiments to rule out confounders.

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metrics-tree skill by lyndonkl/claude | VeilStrat