# lean-analytics

Audits startup dashboards, cuts out vanity metrics, and helps teams select the One Metric That Matters for their business stage.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 3100 installs · ★ 2508
- Author: wondelai
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/wondelai/skills/tree/HEAD/lean-analytics/SKILL.md
- Updated: 2026-09-10
- HTML: https://affpapa.org/en/nevibecode/skills/lean-analytics

## What it does

This skill adapts principles from Alistair Croll and Benjamin Yoskovitz's book into practical prompt workflows. It scores your metrics setup from 0 to 10. Cumulative growth charts and total signup counts immediately trigger a low rating because they conceal actual user churn.

The logic centers on finding the One Metric That Matters (OMTM) for your current business model. It pairs that metric with a counter-metric to prevent gaming. For instance, pairing activation speed with 30-day retention keeps teams honest.

The framework strictly prohibits using the OMTM as personal performance quotas. Setting individual quotas on this metric encourages distorted reporting instead of genuine product learning.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add wondelai/skills --skill lean-analytics -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/lean-analytics/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/lean-analytics/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add wondelai/skills --skill lean-analytics -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/lean-analytics/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add wondelai/skills --skill lean-analytics -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/lean-analytics/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install wondelai/skills lean-analytics
```

2. **Manually: put the files in place**
- File: `.github/skills/lean-analytics/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add wondelai/skills --skill lean-analytics -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/wondelai/skills --path lean-analytics
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/lean-analytics/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add wondelai/skills --skill lean-analytics -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/wondelai/skills/tree/HEAD/lean-analytics/SKILL.md

## When to use it
- audit existing dashboards for vanity metrics
- select the One Metric That Matters for a product stage
- pair an OMTM with a protective counter-metric
- convert cumulative vanity graphs into cohort ratios

## Example prompts
- Audit our dashboard metrics against the Lean Analytics framework
- Help us choose an OMTM and a counter-metric for our retention stage
- Convert our total signup chart into an actionable cohort ratio

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 0c6ffe668a0abb67.

BEGIN THIRD-PARTY FILE 0c6ffe668a0abb67
```text
---
name: lean-analytics
description: 'Choose and audit startup metrics using Croll and Yoskovitz''s "Lean Analytics". Use when the user mentions "what metrics should we track", "KPIs", "north star metric", "One Metric That Matters (OMTM)", "vanity metrics", "analytics dashboard", "DAU/MAU", "churn benchmark", or "measure product-market fit". Also trigger when choosing metrics for a startup or feature, auditing a dashboard for vanity metrics, setting metric targets and baselines, or instrumenting a product by business model and stage. Covers good-vs-vanity metrics, the One Metric That Matters, metrics by business model, the five startup stages, and benchmarks. For the build-measure-learn loop, see lean-startup. For fixing activation and retention, see improve-retention.'
license: MIT
metadata:
  author: wondelai
  version: "1.2.0"
---

# Lean Analytics

A data discipline for startups distilled from Alistair Croll and Benjamin Yoskovitz's *Lean Analytics*: separate metrics that change decisions from numbers that merely flatter, then point the whole company at the One Metric That Matters for your business model and stage. Use it to choose metrics, audit dashboards, set targets, and plan instrumentation.

## Core Principle

**Focus on the one metric that matters right now — everything else is noise that feels like progress.** Startups die from lack of focus more often than lack of data. The discipline is knowing your business model, knowing your stage, and tracking the single number that tells you whether the riskiest part of the business is working. A metric earns attention only if it changes what you do next.

## Scoring

**Goal: 10/10.** Rate metric choices, dashboards, and instrumentation plans 0-10 against these principles. Report the current score and the specific changes needed to reach 10/10.

- **9-10:** One OMTM matched to model and stage, paired counter-metric, a line in the sand with a pre-committed miss response, cohorted and segmented data
- **7-8:** Mostly actionable ratios and a plausible OMTM, but no explicit target, weak cohorting, or too many "key" metrics
- **5-6:** Actionable and vanity metrics mixed; dashboard exists but rarely changes a decision; model and stage never named
- **3-4:** Vanity metrics dominate — totals, cumulative charts, blended averages; metrics copied from other companies
- **0-2:** No instrumentation, or numbers chosen to impress investors rather than drive decisions

## Framework

### 1. Good Metrics vs Vanity Metrics

**Core concept:** A good metric is comparative (versus last week, versus another cohort), understandable (the team can recall and debate it), a ratio or rate (not an ever-growing total), and behavior-changing — if a number won't change what you do, stop measuring it. Vanity metrics — total signups, page views, cumulative anything — only go up and only make you feel good.

**Why it works:** The output of analytics is decisions, not data. Ratios are inherently comparative and operable, while totals hide decay: total registered users rises even while the product bleeds actives. Forcing every metric through the "what will we do differently?" test converts reporting into learning.
```
END THIRD-PARTY FILE 0c6ffe668a0abb67

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