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North Star Metric

Activate when: teams are fighting over which metric to optimize; someone says 'north star metric,' 'NSM,' 'OMTM,' or 'one metric that matters'; a dashboard h... Skill: North Star Metric Owner: deciqai Summary: Activate when: teams are fighting over which metric to optimize; someone says 'north star metric,' 'NSM,' 'OMTM,' or 'one metric that matters'; a dashboard h... Tags: latest:1.0.5 Version history: v1.0.5 | 2026-07-16T18:08:59.762Z | user Description tail link + agents machine-readable metadata line (deciqai.com/s/north-star-metric.json) v1.0.4 | 2026-07-09T11:19:28.135

OpenClaw

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Rank

Safety

Downloads

1.1k

Updated

Oct 11, 2026

Version

1.0.5

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.1K downloads reported by the source. Last updated 10/11/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 1.1K downloadsadoption · observed Oct 11, 2026
Latest release: 1.0.5release · observed Jul 16, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17a4mqcnk515kvaca5ze55d0x88pfpx:north-star-metric

Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-deciqai-north-star-metric/snapshot"

Documentation

CLAWHUB

119,918 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: north-star-metric
description: "Activate when: teams are fighting over which metric to optimize; someone says 'north star metric,' 'NSM,' 'OMTM,' or 'one metric that matters'; a dashboard has 30+ metrics with no clear priority; a leading indicator is needed that predicts revenue before it moves; or someone asks 'what should we optimize?'
  Do NOT activate when: the product has no customers yet (no value to measure — use lean-startup instead); a single team in a mature business needs execution KPIs rather than cross-team alignment. More: deciqai.com/c/north-star-metric"
---

# North Star Metric

## Overview

The **North Star Metric (NSM)** is the single metric that most directly measures *value delivered to customers* and predicts revenue over time. Popularized by Sean Ellis and Amplitude. Revenue is the goal; the NSM is the *leading indicator* that predicts it early enough to act — picking revenue itself produces a lagging dashboard, not a steering wheel.

**Compose:** aarrr-pirate-metrics instruments the full funnel; NSM elevates one funnel metric to cross-team primacy. first-principles clarifies what value the product actually delivers. pmf-crossing-the-chasm — the NSM is typically an Activation- or Retention-stage metric.

## When to Use

**Use when:** teams are optimizing conflicting metrics; dashboard has 30+ metrics with no priority; a leading indicator of revenue is needed; someone says "NSM," "OMTM," "what should we optimize," or "we measure too many things"; an AI-native product is chasing sign-ups / prompts / demo plays and needs an activated-value metric that survives high inference/capex costs and AI-adoption churn.

**Do NOT use when:** product has no customers (pre-PMF → use lean-startup); single-team execution in a mature business; genuinely conflicting strategic objectives (the strategy needs work, not a metric).

## Coaching Novices (Adaptive Front Door)

- **Engine mode:** user has a concrete case → run The Process directly.
- **Coach mode:** user is unfamiliar or has no concrete case → guide step by step.

In Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.

1. One-line what-it-is: the NSM is the single metric that best captures the value your product delivers to customers — when it grows, revenue grows behind it.
2. Check fit against When to Use / When NOT to use. No customers → redirect to lean-startup. Single mature team → wrong scope.
3. Elicit the product's core value in customer units (time saved, problem solved) — not the product feature.
> **[WAIT — do not advance until user responds]**
4. Walk: value → metric candidate → 3-criteria check → test against company strategy → pick. Pause at each.
> **[WAIT — do not advance until user responds]**
5. Close by naming the chosen NSM + the supporting metrics it should not be confused with. One number on the wall; a list of "useful but not the NSM" metrics.
> **[WAIT — do not advance until user responds]**

_meta.json

{
  "ownerId": "kn754b8sk22s8c6gjxt02bftbn88q7ye",
  "slug": "north-star-metric",
  "version": "1.0.5",
  "publishedAt": 1784225339762
}

references/sources.md

# Sources — north-star-metric

> *Primary sources for the [north-star-metric](../SKILL.md) skill.*

- **Ellis, Sean.** "The North Star Metric for Sustainable Growth." *GrowthHackers*, 2017. https://growthhackers.com/articles/north-star-metric **Primary source for the term**.
- **Amplitude.** *The North Star Playbook* (2nd ed., Amplitude Inc., 2022). https://amplitude.com/north-star **Operational framework with the 3-criteria test**.
- **Croll, Alistair & Yoskovitz, Benjamin.** *Lean Analytics: Use Data to Build a Better Startup Faster*. O'Reilly, 2013. **The "One Metric That Matters" formulation**.
- **Palihapitiya, Chamath.** "How We Put Facebook On The Path To 1 Billion Users." Stanford Graduate School of Business lecture, ca. 2013. Public excerpts: https://www.businessinsider.com/the-secret-to-facebooks-explosive-growth-2014-12 **Primary source for the Facebook "7 friends in 10 days" case**.
- **Chen, Andrew.** Growth writings: https://andrewchen.com/ — useful secondary source for NSM patterns across companies.
- **Reuters.** "ChatGPT sets record for fastest-growing user base — analyst note." February 2, 2023. https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01/ — widely-cited reference for the scale of AI-product sign-up growth (~100M monthly users within roughly two months of launch) that set the vanity-metric expectation of the 2023–2026 wave.
- **Amplitude.** *The North Star Playbook* (Amplitude Inc.). https://amplitude.com/north-star — the operational 3-criteria framework applied to modern (including AI-native) products; distinguishes activation/value metrics from vanity metrics.
- The popular framing "what gets measured gets managed" (often attributed to Peter Drucker) is **not** cited as a source — by this skill's own rule, an aphorism is not evidence. Drucker did not actually write this line; it is a misattribution. The framework here is operational, not aphoristic.

examples/ai-product-activated-value-vs-vanity-metrics-2023-2026.md

# Method in Action: Choosing a North Star for an AI Product — Activated Value vs. Vanity Metrics (2023–2026)

> *Example for the [north-star-metric](../SKILL.md) skill.*

A worked example applying the NSM Audit to the class of AI-native products (assistants, copilots, and agents) that proliferated after ChatGPT's late-2022 launch. The composite pattern below reflects widely-reported dynamics of the 2023–2026 AI product wave, not the internal dashboard of any one named company.

**The trap of the moment.** In the 2023–2026 AI boom, capital and attention flooded toward signup and usage vanity metrics. A demo would go viral, sign-ups would spike, a "5-day-to-1-million-users" headline would follow (ChatGPT itself reached roughly 100 million monthly users within about two months of launch, per widely-reported estimates — a genuine outlier, but it set an expectation everyone chased). Teams optimized for the metrics that made the headline: registered accounts, prompts submitted, demo plays, "AI feature engagement." The problem, visible by 2024–2025, was that many of these numbers moved with launch marketing and novelty, then decayed — high trial, low durable value. With AI inference and capex costs high, a metric that counts *attempts* rather than *successful outcomes* actively misleads: every ungrounded, hallucinated, or rejected model output still increments "prompts submitted" and still burns compute.

Run the **NSM Audit** on a representative AI agent/copilot product:

1. **Articulate customer value in customer units (Step 1).** The customer's outcome is *a real task completed to their satisfaction* — a support ticket resolved, a document drafted and kept, a code change merged, an agent action executed and accepted. Not "the model responded." The unit is a **successfully completed, accepted piece of work**, not a token generated or a session opened.

2. **Generate 3–5 NSM candidates (Step 2).** (1) Sign-ups / registered accounts; (2) prompts or messages submitted per user; (3) demo plays / feature "engagement"; (4) **tasks successfully completed (or agent actions accepted) per active user**; (5) weekly active users returning to complete a task.

3. **Apply the 3 criteria (Step 3):**
- **Sign-ups:** customer value? weak — a signup is intent, not value delivered. Moves with marketing spend. **Reject as NSM** (classic vanity metric).
- **Prompts submitted:** customer value? mixed — a prompt is an *attempt*, and a failed/hallucinated answer still counts. Optimizing it can reward churny frustration (users re-prompting because the first answer was wrong). **Supporting at best; fails the perverse-incentive test.**
- **Demo plays / feature engagement:** customer value? weak — novelty-driven, decays after launch. **Reject as NSM.**
- **Tasks successfully completed / accepted agent actions per active user:** customer value ✓ (this *is* the outcome the user hired the product for); strategy fit ✓ (an agent company's strategy is doing real work, not

examples/facebook-seven-friends-in-ten-days-2007-2010.md

# Method in Action: Facebook's "Seven Friends in Ten Days" (2007–2010)

> *Example for the [north-star-metric](../SKILL.md) skill.*

A worked example. Not Silicon Valley legend — discussed publicly by Chamath Palihapitiya, head of Facebook's growth team 2007–2011, in lectures and interviews.

When Facebook's growth team formed in **2007**, the site had ~50 million users and was facing competition from MySpace, Friendster, and emerging social products. The company had many metrics — signups, MAU, time spent, photos uploaded, messages sent, friends added. Each team optimized different ones.

The growth team, looking at retention data, identified a **load-bearing leading indicator**: users who connected with **7 friends in their first 10 days** retained at multiples of the rate of users who did not. The "7 friends in 10 days" metric was not the most-counted, the easiest to grow, or the most "viral-feeling." It was the metric that *predicted long-term retention* most strongly — which itself predicted long-term revenue.

Palihapitiya, speaking publicly years later:

> "We figured out a way to identify, with very high accuracy and statistical relevance, who was actually likely to be retained… It was 'within the first ten days, did this person register a sufficient number of friends?' And the threshold was seven. So our number, the only number that mattered, was making sure that as many users as possible got to seven friends within ten days."
> — Chamath Palihapitiya, "How We Put Facebook On The Path To 1 Billion Users," lecture at Stanford GSB, ca. 2013. Public excerpts archived at: https://www.businessinsider.com/the-secret-to-facebooks-explosive-growth-2014-12

Walk the NSM Audit on Facebook 2007:

- **Customer value (Step 1):** Users connected to their existing social network and using it as their primary social communication tool.
- **Candidate metrics (Step 2):** Signups, DAU/MAU ratio, time spent, photos posted, messages sent, friends-added per user, friends-added-in-first-N-days.
- **3-criteria check (Step 3):**
  - Signups: customer value? weak (signup ≠ value). **Reject as NSM.**
  - Time spent: customer value? mixed (could be addiction-y, not genuine value). **Supporting, not NSM.**
  - Friends added in first 10 days ≥ 7: customer value ✓ (network-effect value); strategy fit ✓ (Facebook bet on social graph density); leads revenue ✓ (retention → DAU → ad inventory). **NSM ✓.**
- **Time-shifted correlation (Step 4):** Users hitting "7 in 10" predicted day-30, day-60, day-180 retention at materially higher rates than those who did not — a clean leading-indicator profile.
- **Perverse-incentive stress test (Step 5):** Could the team game "friends added" by spammy auto-suggest? Yes — and they did push social discovery aggressively, but balanced against quality signals (was the friend reciprocated, did messages flow). The NSM's main risk was that *quantity of friend connections* could be cheap-grown; Facebook addressed this with quality guardrails

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