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someone says 'north star metric,' 'NSM,' 'OMTM,' or 'one metric that matters'; a dashboard h...\n\nTags: latest:1.0.5\n\nVersion history:\n\nv1.0.5 | 2026-07-16T18:08:59.762Z | user\n\nDescription tail link + agents machine-readable metadata line (deciqai.com/s/north-star-metric.json)\n\nv1.0.4 | 2026-07-09T11:19:28.135Z | user\n\nRefresh: 2024-2026 AI-era worked examples added (strategy/leadership + systems/game-theory batch)\n\nv1.0.3 | 2026-07-08T11:12:23.965Z | user\n\nFooter now uses /c/<slug> short link (fixes UTM truncation when SKILL.md is read in a terminal)\n\nv1.0.2 | 2026-07-08T00:57:08.538Z | user\n\nRefreshed content + GitHub star link in footer\n\nv1.0.1 | 2026-07-07T22:30:07.286Z | user\n\nAdd catalog categories and topics\n\nv1.0.0 | 2026-06-30T13:18:33.070Z | user\n\nInitial publish\n\nArchive index:\n\nArchive v1.0.5: 6 files, 12878 bytes\n\nFiles: examples/ai-product-activated-value-vs-vanity-metrics-2023-2026.md (7192b), examples/facebook-seven-friends-in-ten-days-2007-2010.md (4675b), references/sources.md (1977b), skill-card.md (2913b), SKILL.md (8341b), _meta.json (136b)\n\nFile v1.0.5:SKILL.md\n\n---\nname: north-star-metric\ndescription: \"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?'\n  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\"\n---\n\n# North Star Metric\n\n## Overview\n\nThe **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.\n\n**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.\n\n## When to Use\n\n**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.\n\n**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).\n\n## Coaching Novices (Adaptive Front Door)\n\n- **Engine mode:** user has a concrete case → run The Process directly.\n- **Coach mode:** user is unfamiliar or has no concrete case → guide step by step.\n\nIn Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.\n\n1. 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.\n2. Check fit against When to Use / When NOT to use. No customers → redirect to lean-startup. Single mature team → wrong scope.\n3. Elicit the product's core value in customer units (time saved, problem solved) — not the product feature.\n> **[WAIT — do not advance until user responds]**\n4. Walk: value → metric candidate → 3-criteria check → test against company strategy → pick. Pause at each.\n> **[WAIT — do not advance until user responds]**\n5. 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.\n> **[WAIT — do not advance until user responds]**\n\n## The Process\n\nRun the **NSM Audit**:\n\n1. **Articulate customer value in customer units** — the customer's outcome, not your product's mechanism.\n2. **Generate 3–5 NSM candidates** — each proxies that value as a measurable metric.\n3. **Apply the 3 criteria** (Amplitude §2): (a) Customer value? (b) Strategy fit? (c) Leads revenue? All three required; two-of-three = supporting metric only.\n4. **Time-shifted correlation** — does the candidate *lead* revenue over 6–12 months?\n5. **Perverse-incentive stress test** — could the team game this in a way that hurts customers?\n6. **Pick one. Put it on the wall.** Explicitly name supporting metrics as supporting, not NSMs.\n7. **Re-evaluate quarterly** — early stage → engagement; growth → retention; scale → revenue-adjacent.\n\n### Output: the NSM Audit\n\n```\nNSM Audit: <product>\nCustomer value: \"<customer experiences ___, measured in ___>\"\nCandidates: (1) <metric> (2) <metric> (3) <metric>\n3-Criteria check: | Candidate | Value? | Strategy? | Leads rev? | Verdict |\nTime-shifted correlation lead: <weeks/months>\nPerverse-incentive risk: <what could go wrong> | Mitigation: <guardrail>\nChosen NSM: <NSM> — measured as <operational def>, reported <weekly/monthly>\nSupporting (NOT NSM): <metric> — <why supporting>\nRe-evaluation date: <next quarter>\n```\n\n*→ Method in Action: [Facebook's \"Seven Friends in Ten Days\" (2007–2010)](examples/facebook-seven-friends-in-ten-days-2007-2010.md)*\n\n*→ 2026 lens: [AI product NSM — activated value vs. vanity metrics (2023–2026)](examples/ai-product-activated-value-vs-vanity-metrics-2023-2026.md)*\n\n## NSM Selection Packs\n\nTypical NSMs by domain: **content platforms** → minutes streamed per active user; **B2B SaaS** → active value moments per account (deals closed, tickets resolved); **marketplaces** → successful transactions; **freemium consumer apps** → core action completions. Adding a pack for your domain (typical NSMs, value-to-revenue mechanism, common mis-picks) is the easiest way to contribute.\n\n## Applying It Well\n\n- NSM is a leading indicator, not the goal. Revenue is the goal; NSM predicts it early enough to act.\n- NSM measures customer value, not company convenience. Easy-to-measure ≠ right NSM.\n- One metric only. Multiple \"north stars\" = strategy conflict, not a metric problem.\n- Stress-test for perverse incentives. The team will optimize whatever the NSM measures.\n- NSM evolves. Pre-commit to quarterly review.\n\n*→ Primary sources: [references/sources.md](references/sources.md)*\n\n## Common Rationalizations\n\n**[D] = designed upfront | [O] = observed in real use. [O] entries are more valuable.**\n\n| Fake move | Reality |\n|---|---|\n| [D] **Revenue as the NSM** | Revenue is the goal, not the leading indicator — lagging dashboard, not a steering wheel. |\n| [D] **Multiple \"north stars\"** | Multiple north stars defeat cross-team alignment; if you genuinely need multiple, you have a strategy conflict. |\n| [D] **Vanity metric promoted to NSM** | Page views, signups, app downloads move with marketing spend; they don't predict revenue. |\n| [D] **NSM fails perverse-incentive test** | If ruthless optimization of this metric hurts customers, the NSM is mis-chosen. Stress-test first. |\n| [D] **Treating the NSM as eternal** | Stage 1 → engagement; growth → retention; scale → revenue-adjacent. Re-evaluate quarterly. |\n| [D] **No operational definition** | \"Engagement\" is not an NSM. \"Users completing ≥3 core actions per week\" is. Specify the event. |\n| [D] **NSM as marketing/PR claim** | \"Making the world better\" is positioning, not a metric. NSM must be a number that goes up or down. |\n| *→ Add [O] entries here after each real use — paste the actual failure pattern* | *What went wrong and why* |\n\n## Red Flags\n\n- The NSM is revenue itself (lagging indicator)\n- The team has 3+ \"north star\" candidates and no decision among them\n- The NSM is a vanity metric (signups, page views, app downloads)\n- No perverse-incentive stress test was done\n- No time-shifted correlation with revenue has been examined\n- The NSM has no operational definition (e.g., \"engagement\" without specifying what counts)\n- The same NSM has been in place for 2+ years across very different business stages with no review\n\n## Verification\n\n- [ ] The customer value is articulated in customer units (not product mechanism)\n- [ ] 3–5 candidate metrics were generated\n- [ ] Each candidate passes all three criteria (customer value + strategy + leading indicator)\n- [ ] Time-shifted correlation with revenue is examined\n- [ ] Perverse-incentive stress test is done with named mitigations\n- [ ] Exactly one NSM is chosen with operational definition\n- [ ] Supporting metrics are explicitly named *as* supporting, not as NSMs\n- [ ] Quarterly re-evaluation is scheduled\n\n---\n\n*Part of **deciqAI Knowledge Skills** — 227 open-source thinking skills that make rigor executable for AI agents. The same skills power every deciqAI agent, which runs them autonomously to operate your company. **See it run → https://www.deciqai.com/c/north-star-metric** · ⭐ Star the repo → https://github.com/deciqAI/knowledge-skills · Contributions welcome.*\n\n*Agents: latest version & machine-readable metadata → https://www.deciqai.com/s/north-star-metric.json*\n\nFile v1.0.5:_meta.json\n\n{\n  \"ownerId\": \"kn754b8sk22s8c6gjxt02bftbn88q7ye\",\n  \"slug\": \"north-star-metric\",\n  \"version\": \"1.0.5\",\n  \"publishedAt\": 1784225339762\n}\n\nFile v1.0.5:references/sources.md\n\n# Sources — north-star-metric\n\n> *Primary sources for the [north-star-metric](../SKILL.md) skill.*\n\n- **Ellis, Sean.** \"The North Star Metric for Sustainable Growth.\" *GrowthHackers*, 2017. https://growthhackers.com/articles/north-star-metric **Primary source for the term**.\n- **Amplitude.** *The North Star Playbook* (2nd ed., Amplitude Inc., 2022). https://amplitude.com/north-star **Operational framework with the 3-criteria test**.\n- **Croll, Alistair & Yoskovitz, Benjamin.** *Lean Analytics: Use Data to Build a Better Startup Faster*. O'Reilly, 2013. **The \"One Metric That Matters\" formulation**.\n- **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**.\n- **Chen, Andrew.** Growth writings: https://andrewchen.com/ — useful secondary source for NSM patterns across companies.\n- **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.\n- **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.\n- 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.\n\nFile v1.0.5:examples/ai-product-activated-value-vs-vanity-metrics-2023-2026.md\n\n# Method in Action: Choosing a North Star for an AI Product — Activated Value vs. Vanity Metrics (2023–2026)\n\n> *Example for the [north-star-metric](../SKILL.md) skill.*\n\nA 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.\n\n**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.\n\nRun the **NSM Audit** on a representative AI agent/copilot product:\n\n1. **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.\n\n2. **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.\n\n3. **Apply the 3 criteria (Step 3):**\n   - **Sign-ups:** customer value? weak — a signup is intent, not value delivered. Moves with marketing spend. **Reject as NSM** (classic vanity metric).\n   - **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.**\n   - **Demo plays / feature engagement:** customer value? weak — novelty-driven, decays after launch. **Reject as NSM.**\n   - **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 generating responses); leads revenue ✓ (accepted work predicts retention and willingness to pay). **NSM ✓.**\n\n4. **Time-shifted correlation (Step 4).** Apply the framework's logic: the behavior to test as a leading indicator is *repeated successful task completion*, not raw usage. The hypothesis — consistent with the NSM/activation literature and worth validating against your own cohort data — is that accounts which complete accepted work early keep coming back, while accounts that only \"tried the demo\" churn regardless of how many prompts they fired. Where this holds, successful-task-completion behaves like a clean leading indicator of week-4 and month-3 retention, expansion, and free-to-paid conversion; signups and prompt volume do not. Treat the correlation as something you must confirm in your data, not assume.\n\n5. **Perverse-incentive stress test (Step 5).** Could the team game \"tasks completed\"? Yes — several ways, each with a guardrail:\n   - Count a task \"complete\" the moment the model *responds* (regardless of correctness) → **guardrail:** completion requires an explicit acceptance signal (user keeps/merges/ships the output, or does not immediately retry/undo).\n   - Shrink tasks into trivial micro-actions to inflate the count → **guardrail:** define the task unit at the level of genuine customer value, and monitor accepted-action rate *per account*, not absolute volume.\n   - Reward high prompt volume, which in an AI product literally means rewarding failure (re-prompting after bad answers) and burning inference budget → **guardrail:** track *acceptance rate* and cost-per-accepted-task alongside the NSM.\n\n6. **Pick one. Put it on the wall (Step 6).** Chosen NSM: **weekly tasks successfully completed and accepted per active account.** Operational definition: a task the user explicitly accepts (keeps, merges, ships, or does not reverse within the session), counted per active account, reported weekly. Explicitly named as **supporting, NOT the NSM:** sign-ups, prompts submitted, demo plays, tokens generated, model latency, gross MAU — all useful for diagnosing the funnel (see [aarrr-pirate-metrics](../aarrr-pirate-metrics/SKILL.md)) but none is the steering wheel.\n\n7. **Re-evaluate quarterly (Step 7).** Early stage → does the agent complete *any* accepted task (activation)? Growth stage → *repeat* accepted tasks per account (retention). Scale → accepted-value moments tied to expansion revenue and cost-per-accepted-task (the AI capex reality forces unit economics into the NSM conversation earlier than in zero-marginal-cost software). The NSM is not eternal.\n\n**The lesson generalizes the timeless core.** This is the same discipline Facebook's growth team applied with \"7 friends in 10 days\" — resist the loud, marketing-sensitive vanity number; find the operationally-defined behavior that *predicts retention and revenue*. What changed in 2023–2026 is only the specific trap: AI products make it unusually easy to mistake *usage* (prompts, demo plays, sign-ups riding a viral moment) for *value* (accepted work), and unusually expensive to get it wrong, because every wasted attempt carries real inference cost.\n\n**Sources:** 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* (Amplitude Inc.). https://amplitude.com/north-star — **the 3-criteria operational framework**. Croll, Alistair & Yoskovitz, Benjamin. *Lean Analytics*. O'Reilly, 2013 — **the \"One Metric That Matters\" and vanity-vs-actionable-metric framing**. On ChatGPT's early adoption scale (widely reported, ~100M monthly users within roughly two months of the Nov-2022 launch): Reuters, \"ChatGPT sets record for fastest-growing user base — analyst note,\" Feb 2, 2023. https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01/ — the *AI-agent activated-value NSM* above is a synthesized application of the framework to the 2023–2026 product wave, not a claim about any single company's internal metric.\n\nFile v1.0.5:examples/facebook-seven-friends-in-ten-days-2007-2010.md\n\n# Method in Action: Facebook's \"Seven Friends in Ten Days\" (2007–2010)\n\n> *Example for the [north-star-metric](../SKILL.md) skill.*\n\nA worked example. Not Silicon Valley legend — discussed publicly by Chamath Palihapitiya, head of Facebook's growth team 2007–2011, in lectures and interviews.\n\nWhen 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.\n\nThe 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.\n\nPalihapitiya, speaking publicly years later:\n\n> \"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.\"\n> — 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\n\nWalk the NSM Audit on Facebook 2007:\n\n- **Customer value (Step 1):** Users connected to their existing social network and using it as their primary social communication tool.\n- **Candidate metrics (Step 2):** Signups, DAU/MAU ratio, time spent, photos posted, messages sent, friends-added per user, friends-added-in-first-N-days.\n- **3-criteria check (Step 3):**\n  - Signups: customer value? weak (signup ≠ value). **Reject as NSM.**\n  - Time spent: customer value? mixed (could be addiction-y, not genuine value). **Supporting, not NSM.**\n  - 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 ✓.**\n- **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.\n- **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 on what counted.\n- **Pick (Step 6):** \"7 friends in 10 days\" became the growth team's official cross-team NSM for the 2007–2010 period. Every product change, signup flow optimization, friend-recommendation algorithm, and onboarding tweak was evaluated against this single number.\n- **Re-evaluation (Step 7):** As Facebook scaled past ~500M users and saturated the addressable graph, the NSM evolved — toward DAU/MAU ratio, then engagement quality, then mobile-DAU specifically, then ad-impressions-per-DAU. The NSM is not eternal.\n\nFacebook grew from ~50M users in 2007 to 1 billion in 2012. The growth team consistently credited the \"7 in 10\" NSM as the single most important focusing tool — *not because it was the most important business number* (revenue was), *but because it was the leading indicator that predicted revenue early enough to course-correct*.\n\n**Sources:** 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**. Croll, Alistair & Yoskovitz, Benjamin. *Lean Analytics: Use Data to Build a Better Startup Faster*. O'Reilly, 2013. **The \"OMTM\" framing**. Palihapitiya, Chamath. \"How We Put Facebook On The Path To 1 Billion Users.\" Stanford GSB lecture, ca. 2013. Public excerpts: https://www.businessinsider.com/the-secret-to-facebooks-explosive-growth-2014-12 **Primary source for the Facebook NSM case**. Andrew Chen's writings on growth metrics: https://andrewchen.com/\n\nFile v1.0.5:skill-card.md\n\n## Description:\n\nHelps teams choose a single North Star Metric by auditing customer value, candidate metrics, revenue-leading behavior, and perverse-incentive risks.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[deciqai](https://clawhub.ai/user/deciqai)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nEmployees, external teams, and product leaders use this skill to align around the one metric that best captures customer value and predicts revenue. It is most useful when teams have too many metrics, conflicting optimization targets, or need to distinguish activated value from vanity metrics.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: The skill provides advisory business-metrics guidance and cites case studies and AI-market claims that may not fit a user's company or data.\n\nMitigation: Verify cited examples and validate any proposed North Star Metric against the user's own cohort, retention, revenue, and guardrail data before making high-stakes strategic decisions.\n\nRisk: A chosen metric can create perverse incentives if teams optimize the count while harming customer value.\n\nMitigation: Use the skill's perverse-incentive stress test and define guardrail metrics before adopting the North Star Metric.\n\n## Reference(s):\n\n- [Sources - north-star-metric](references/sources.md)\n- [Facebook's Seven Friends in Ten Days example](examples/facebook-seven-friends-in-ten-days-2007-2010.md)\n- [AI product activated value example](examples/ai-product-activated-value-vs-vanity-metrics-2023-2026.md)\n- [Sean Ellis - The North Star Metric for Sustainable Growth](https://growthhackers.com/articles/north-star-metric)\n- [Amplitude - The North Star Playbook](https://amplitude.com/north-star)\n- [Business Insider - Facebook growth lecture excerpt](https://www.businessinsider.com/the-secret-to-facebooks-explosive-growth-2014-12)\n- [Reuters - ChatGPT fastest-growing user base report](https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01/)\n- [ClawHub skill page](https://clawhub.ai/deciqai/skills/north-star-metric)\n- [deciqAI North Star Metric page](https://www.deciqai.com/c/north-star-metric)\n- [Machine-readable skill metadata](https://www.deciqai.com/s/north-star-metric.json)\n\n## Skill Output:\n\n**Output Type(s):** [Analysis, Markdown, Guidance]\n\n**Output Format:** [Markdown with structured audit fields and coaching questions]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [May pause for user input during novice coaching mode.]\n\n## Skill Version(s):\n\n1.0.5 (source: server release evidence)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.\n\nArchive v1.0.4: 6 files, 12794 bytes\n\nFiles: examples/ai-product-activated-value-vs-vanity-metrics-2023-2026.md (7192b), examples/facebook-seven-friends-in-ten-days-2007-2010.md (4675b), references/sources.md (1977b), skill-card.md (2926b), SKILL.md (8196b), _meta.json (136b)\n\nFile v1.0.4:SKILL.md\n\n---\nname: north-star-metric\ndescription: \"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?'\n  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.\"\n---\n\n# North Star Metric\n\n## Overview\n\nThe **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.\n\n**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.\n\n## When to Use\n\n**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.\n\n**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).\n\n## Coaching Novices (Adaptive Front Door)\n\n- **Engine mode:** user has a concrete case → run The Process directly.\n- **Coach mode:** user is unfamiliar or has no concrete case → guide step by step.\n\nIn Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.\n\n1. 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.\n2. Check fit against When to Use / When NOT to use. No customers → redirect to lean-startup. Single mature team → wrong scope.\n3. Elicit the product's core value in customer units (time saved, problem solved) — not the product feature.\n> **[WAIT — do not advance until user responds]**\n4. Walk: value → metric candidate → 3-criteria check → test against company strategy → pick. Pause at each.\n> **[WAIT — do not advance until user responds]**\n5. 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.\n> **[WAIT — do not advance until user responds]**\n\n## The Process\n\nRun the **NSM Audit**:\n\n1. **Articulate customer value in customer units** — the customer's outcome, not your product's mechanism.\n2. **Generate 3–5 NSM candidates** — each proxies that value as a measurable metric.\n3. **Apply the 3 criteria** (Amplitude §2): (a) Customer value? (b) Strategy fit? (c) Leads revenue? All three required; two-of-three = supporting metric only.\n4. **Time-shifted correlation** — does the candidate *lead* revenue over 6–12 months?\n5. **Perverse-incentive stress test** — could the team game this in a way that hurts customers?\n6. **Pick one. Put it on the wall.** Explicitly name supporting metrics as supporting, not NSMs.\n7. **Re-evaluate quarterly** — early stage → engagement; growth → retention; scale → revenue-adjacent.\n\n### Output: the NSM Audit\n\n```\nNSM Audit: <product>\nCustomer value: \"<customer experiences ___, measured in ___>\"\nCandidates: (1) <metric> (2) <metric> (3) <metric>\n3-Criteria check: | Candidate | Value? | Strategy? | Leads rev? | Verdict |\nTime-shifted correlation lead: <weeks/months>\nPerverse-incentive risk: <what could go wrong> | Mitigation: <guardrail>\nChosen NSM: <NSM> — measured as <operational def>, reported <weekly/monthly>\nSupporting (NOT NSM): <metric> — <why supporting>\nRe-evaluation date: <next quarter>\n```\n\n*→ Method in Action: [Facebook's \"Seven Friends in Ten Days\" (2007–2010)](examples/facebook-seven-friends-in-ten-days-2007-2010.md)*\n\n*→ 2026 lens: [AI product NSM — activated value vs. vanity metrics (2023–2026)](examples/ai-product-activated-value-vs-vanity-metrics-2023-2026.md)*\n\n## NSM Selection Packs\n\nTypical NSMs by domain: **content platforms** → minutes streamed per active user; **B2B SaaS** → active value moments per account (deals closed, tickets resolved); **marketplaces** → successful transactions; **freemium consumer apps** → core action completions. Adding a pack for your domain (typical NSMs, value-to-revenue mechanism, common mis-picks) is the easiest way to contribute.\n\n## Applying It Well\n\n- NSM is a leading indicator, not the goal. Revenue is the goal; NSM predicts it early enough to act.\n- NSM measures customer value, not company convenience. Easy-to-measure ≠ right NSM.\n- One metric only. Multiple \"north stars\" = strategy conflict, not a metric problem.\n- Stress-test for perverse incentives. The team will optimize whatever the NSM measures.\n- NSM evolves. Pre-commit to quarterly review.\n\n*→ Primary sources: [references/sources.md](references/sources.md)*\n\n## Common Rationalizations\n\n**[D] = designed upfront | [O] = observed in real use. [O] entries are more valuable.**\n\n| Fake move | Reality |\n|---|---|\n| [D] **Revenue as the NSM** | Revenue is the goal, not the leading indicator — lagging dashboard, not a steering wheel. |\n| [D] **Multiple \"north stars\"** | Multiple north stars defeat cross-team alignment; if you genuinely need multiple, you have a strategy conflict. |\n| [D] **Vanity metric promoted to NSM** | Page views, signups, app downloads move with marketing spend; they don't predict revenue. |\n| [D] **NSM fails perverse-incentive test** | If ruthless optimization of this metric hurts customers, the NSM is mis-chosen. Stress-test first. |\n| [D] **Treating the NSM as eternal** | Stage 1 → engagement; growth → retention; scale → revenue-adjacent. Re-evaluate quarterly. |\n| [D] **No operational definition** | \"Engagement\" is not an NSM. \"Users completing ≥3 core actions per week\" is. Specify the event. |\n| [D] **NSM as marketing/PR claim** | \"Making the world better\" is positioning, not a metric. NSM must be a number that goes up or down. |\n| *→ Add [O] entries here after each real use — paste the actual failure pattern* | *What went wrong and why* |\n\n## Red Flags\n\n- The NSM is revenue itself (lagging indicator)\n- The team has 3+ \"north star\" candidates and no decision among them\n- The NSM is a vanity metric (signups, page views, app downloads)\n- No perverse-incentive stress test was done\n- No time-shifted correlation with revenue has been examined\n- The NSM has no operational definition (e.g., \"engagement\" without specifying what counts)\n- The same NSM has been in place for 2+ years across very different business stages with no review\n\n## Verification\n\n- [ ] The customer value is articulated in customer units (not product mechanism)\n- [ ] 3–5 candidate metrics were generated\n- [ ] Each candidate passes all three criteria (customer value + strategy + leading indicator)\n- [ ] Time-shifted correlation with revenue is examined\n- [ ] Perverse-incentive stress test is done with named mitigations\n- [ ] Exactly one NSM is chosen with operational definition\n- [ ] Supporting metrics are explicitly named *as* supporting, not as NSMs\n- [ ] Quarterly re-evaluation is scheduled\n\n---\n\n*Part of **deciqAI Knowledge Skills** — 189 open-source thinking skills that make rigor executable for AI agents. The same skills power every deciqAI agent, which runs them autonomously to operate your company. **See it run → https://www.deciqai.com/c/north-star-metric** · ⭐ Star the repo → https://github.com/deciqAI/knowledge-skills · Contributions welcome.*\n\nFile v1.0.4:_meta.json\n\n{\n  \"ownerId\": \"kn754b8sk22s8c6gjxt02bftbn88q7ye\",\n  \"slug\": \"north-star-metric\",\n  \"version\": \"1.0.4\",\n  \"publishedAt\": 1783595968135\n}\n\nFile v1.0.4:references/sources.md\n\n# Sources — north-star-metric\n\n> *Primary sources for the [north-star-metric](../SKILL.md) skill.*\n\n- **Ellis, Sean.** \"The North Star Metric for Sustainable Growth.\" *GrowthHackers*, 2017. https://growthhackers.com/articles/north-star-metric **Primary source for the term**.\n- **Amplitude.** *The North Star Playbook* (2nd ed., Amplitude Inc., 2022). https://amplitude.com/north-star **Operational framework with the 3-criteria test**.\n- **Croll, Alistair & Yoskovitz, Benjamin.** *Lean Analytics: Use Data to Build a Better Startup Faster*. O'Reilly, 2013. **The \"One Metric That Matters\" formulation**.\n- **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**.\n- **Chen, Andrew.** Growth writings: https://andrewchen.com/ — useful secondary source for NSM patterns across companies.\n- **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.\n- **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.\n- 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.\n\nFile v1.0.4:examples/ai-product-activated-value-vs-vanity-metrics-2023-2026.md\n\n# Method in Action: Choosing a North Star for an AI Product — Activated Value vs. Vanity Metrics (2023–2026)\n\n> *Example for the [north-star-metric](../SKILL.md) skill.*\n\nA 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.\n\n**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.\n\nRun the **NSM Audit** on a representative AI agent/copilot product:\n\n1. **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.\n\n2. **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.\n\n3. **Apply the 3 criteria (Step 3):**\n   - **Sign-ups:** customer value? weak — a signup is intent, not value delivered. Moves with marketing spend. **Reject as NSM** (classic vanity metric).\n   - **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.**\n   - **Demo plays / feature engagement:** customer value? weak — novelty-driven, decays after launch. **Reject as NSM.**\n   - **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 generating responses); leads revenue ✓ (accepted work predicts retention and willingness to pay). **NSM ✓.**\n\n4. **Time-shifted correlation (Step 4).** Apply the framework's logic: the behavior to test as a leading indicator is *repeated successful task completion*, not raw usage. The hypothesis — consistent with the NSM/activation literature and worth validating against your own cohort data — is that accounts which complete accepted work early keep coming back, while accounts that only \"tried the demo\" churn regardless of how many prompts they fired. Where this holds, successful-task-completion behaves like a clean leading indicator of week-4 and month-3 retention, expansion, and free-to-paid conversion; signups and prompt volume do not. Treat the correlation as something you must confirm in your data, not assume.\n\n5. **Perverse-incentive stress test (Step 5).** Could the team game \"tasks completed\"? Yes — several ways, each with a guardrail:\n   - Count a task \"complete\" the moment the model *responds* (regardless of correctness) → **guardrail:** completion requires an explicit acceptance signal (user keeps/merges/ships the output, or does not immediately retry/undo).\n   - Shrink tasks into trivial micro-actions to inflate the count → **guardrail:** define the task unit at the level of genuine customer value, and monitor accepted-action rate *per account*, not absolute volume.\n   - Reward high prompt volume, which in an AI product literally means rewarding failure (re-prompting after bad answers) and burning inference budget → **guardrail:** track *acceptance rate* and cost-per-accepted-task alongside the NSM.\n\n6. **Pick one. Put it on the wall (Step 6).** Chosen NSM: **weekly tasks successfully completed and accepted per active account.** Operational definition: a task the user explicitly accepts (keeps, merges, ships, or does not reverse within the session), counted per active account, reported weekly. Explicitly named as **supporting, NOT the NSM:** sign-ups, prompts submitted, demo plays, tokens generated, model latency, gross MAU — all useful for diagnosing the funnel (see [aarrr-pirate-metrics](../aarrr-pirate-metrics/SKILL.md)) but none is the steering wheel.\n\n7. **Re-evaluate quarterly (Step 7).** Early stage → does the agent complete *any* accepted task (activation)? Growth stage → *repeat* accepted tasks per account (retention). Scale → accepted-value moments tied to expansion revenue and cost-per-accepted-task (the AI capex reality forces unit economics into the NSM conversation earlier than in zero-marginal-cost software). The NSM is not eternal.\n\n**The lesson generalizes the timeless core.** This is the same discipline Facebook's growth team applied with \"7 friends in 10 days\" — resist the loud, marketing-sensitive vanity number; find the operationally-defined behavior that *predicts retention and revenue*. What changed in 2023–2026 is only the specific trap: AI products make it unusually easy to mistake *usage* (prompts, demo plays, sign-ups riding a viral moment) for *value* (accepted work), and unusually expensive to get it wrong, because every wasted attempt carries real inference cost.\n\n**Sources:** 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* (Amplitude Inc.). https://amplitude.com/north-star — **the 3-criteria operational framework**. Croll, Alistair & Yoskovitz, Benjamin. *Lean Analytics*. O'Reilly, 2013 — **the \"One Metric That Matters\" and vanity-vs-actionable-metric framing**. On ChatGPT's early adoption scale (widely reported, ~100M monthly users within roughly two months of the Nov-2022 launch): Reuters, \"ChatGPT sets record for fastest-growing user base — analyst note,\" Feb 2, 2023. https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01/ — the *AI-agent activated-value NSM* above is a synthesized application of the framework to the 2023–2026 product wave, not a claim about any single company's internal metric.\n\nFile v1.0.4:examples/facebook-seven-friends-in-ten-days-2007-2010.md\n\n# Method in Action: Facebook's \"Seven Friends in Ten Days\" (2007–2010)\n\n> *Example for the [north-star-metric](../SKILL.md) skill.*\n\nA worked example. Not Silicon Valley legend — discussed publicly by Chamath Palihapitiya, head of Facebook's growth team 2007–2011, in lectures and interviews.\n\nWhen 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.\n\nThe 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.\n\nPalihapitiya, speaking publicly years later:\n\n> \"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.\"\n> — 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\n\nWalk the NSM Audit on Facebook 2007:\n\n- **Customer value (Step 1):** Users connected to their existing social network and using it as their primary social communication tool.\n- **Candidate metrics (Step 2):** Signups, DAU/MAU ratio, time spent, photos posted, messages sent, friends-added per user, friends-added-in-first-N-days.\n- **3-criteria check (Step 3):**\n  - Signups: customer value? weak (signup ≠ value). **Reject as NSM.**\n  - Time spent: customer value? mixed (could be addiction-y, not genuine value). **Supporting, not NSM.**\n  - 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 ✓.**\n- **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.\n- **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 on what counted.\n- **Pick (Step 6):** \"7 friends in 10 days\" became the growth team's official cross-team NSM for the 2007–2010 period. Every product change, signup flow optimization, friend-recommendation algorithm, and onboarding tweak was evaluated against this single number.\n- **Re-evaluation (Step 7):** As Facebook scaled past ~500M users and saturated the addressable graph, the NSM evolved — toward DAU/MAU ratio, then engagement quality, then mobile-DAU specifically, then ad-impressions-per-DAU. The NSM is not eternal.\n\nFacebook grew from ~50M users in 2007 to 1 billion in 2012. The growth team consistently credited the \"7 in 10\" NSM as the single most important focusing tool — *not because it was the most important business number* (revenue was), *but because it was the leading indicator that predicted revenue early enough to course-correct*.\n\n**Sources:** 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**. Croll, Alistair & Yoskovitz, Benjamin. *Lean Analytics: Use Data to Build a Better Startup Faster*. O'Reilly, 2013. **The \"OMTM\" framing**. Palihapitiya, Chamath. \"How We Put Facebook On The Path To 1 Billion Users.\" Stanford GSB lecture, ca. 2013. Public excerpts: https://www.businessinsider.com/the-secret-to-facebooks-explosive-growth-2014-12 **Primary source for the Facebook NSM case**. Andrew Chen's writings on growth metrics: https://andrewchen.com/\n\nFile v1.0.4:skill-card.md\n\n## Description: <br>\nGuides teams through choosing one North Star Metric that measures customer value, fits strategy, and acts as a leading indicator for revenue. <br>\n\nThis skill is ready for commercial/non-commercial use. <br>\n\n## Publisher: <br>\n[deciqai](https://clawhub.ai/user/deciqai) <br>\n\n### License/Terms of Use: <br>\nMIT-0 <br>\n\n\n## Use Case: <br>\nProduct leaders, founders, growth teams, and strategy operators use this skill to align teams around a single product metric, audit candidate metrics, reject vanity metrics, and define guardrails for measurement incentives. <br>\n\n### Deployment Geography for Use: <br>\nGlobal <br>\n\n## Known Risks and Mitigations: <br>\nRisk: Strategic metric guidance can be misapplied if examples or source claims are accepted without checking the user's product data. <br>\nMitigation: Validate candidate metrics against internal cohort, retention, revenue, and customer-value data before adopting an NSM. <br>\nRisk: A chosen NSM can create perverse incentives if teams optimize the count while harming customer value. <br>\nMitigation: Run the skill's perverse-incentive stress test, define what counts operationally, and pair the NSM with explicit guardrail metrics. <br>\n\n\n## Reference(s): <br>\n- [ClawHub skill page: North Star Metric](https://clawhub.ai/deciqai/skills/north-star-metric) <br>\n- [Sources - north-star-metric](references/sources.md) <br>\n- [The North Star Metric for Sustainable Growth](https://growthhackers.com/articles/north-star-metric) <br>\n- [The North Star Playbook](https://amplitude.com/north-star) <br>\n- [Facebook seven friends in ten days case excerpt](https://www.businessinsider.com/the-secret-to-facebooks-explosive-growth-2014-12) <br>\n- [Andrew Chen growth writings](https://andrewchen.com/) <br>\n- [Reuters ChatGPT adoption reference](https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01/) <br>\n- [Example: Facebook's seven friends in ten days](examples/facebook-seven-friends-in-ten-days-2007-2010.md) <br>\n- [Example: AI product activated value vs vanity metrics](examples/ai-product-activated-value-vs-vanity-metrics-2023-2026.md) <br>\n\n\n## Skill Output: <br>\n**Output Type(s):** [Guidance, Analysis, Markdown] <br>\n**Output Format:** [Markdown audit with candidate metrics, criteria checks, perverse-incentive risks, chosen metric, supporting metrics, and re-evaluation date] <br>\n**Output Parameters:** [1D] <br>\n**Other Properties Related to Output:** [May operate in stepwise coaching mode and stop for user input between prompts.] <br>\n\n## Skill Version(s): <br>\n1.0.4 (source: ClawHub release evidence) <br>\n\n## Ethical Considerations: <br>\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment. <br>\n\nArchive v1.0.3: 5 files, 8657 bytes\n\nFiles: examples/facebook-seven-friends-in-ten-days-2007-2010.md (4675b), references/sources.md (1331b), skill-card.md (2867b), SKILL.md (7876b), _meta.json (136b)\n\nFile v1.0.3:SKILL.md\n\n---\nname: north-star-metric\ndescription: \"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?'\n  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.\"\n---\n\n# North Star Metric\n\n## Overview\n\nThe **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.\n\n**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.\n\n## When to Use\n\n**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.\"\n\n**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).\n\n## Coaching Novices (Adaptive Front Door)\n\n- **Engine mode:** user has a concrete case → run The Process directly.\n- **Coach mode:** user is unfamiliar or has no concrete case → guide step by step.\n\nIn Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.\n\n1. 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.\n2. Check fit against When to Use / When NOT to use. No customers → redirect to lean-startup. Single mature team → wrong scope.\n3. Elicit the product's core value in customer units (time saved, problem solved) — not the product feature.\n> **[WAIT — do not advance until user responds]**\n4. Walk: value → metric candidate → 3-criteria check → test against company strategy → pick. Pause at each.\n> **[WAIT — do not advance until user responds]**\n5. 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.\n> **[WAIT — do not advance until user responds]**\n\n## The Process\n\nRun the **NSM Audit**:\n\n1. **Articulate customer value in customer units** — the customer's outcome, not your product's mechanism.\n2. **Generate 3–5 NSM candidates** — each proxies that value as a measurable metric.\n3. **Apply the 3 criteria** (Amplitude §2): (a) Customer value? (b) Strategy fit? (c) Leads revenue? All three required; two-of-three = supporting metric only.\n4. **Time-shifted correlation** — does the candidate *lead* revenue over 6–12 months?\n5. **Perverse-incentive stress test** — could the team game this in a way that hurts customers?\n6. **Pick one. Put it on the wall.** Explicitly name supporting metrics as supporting, not NSMs.\n7. **Re-evaluate quarterly** — early stage → engagement; growth → retention; scale → revenue-adjacent.\n\n### Output: the NSM Audit\n\n```\nNSM Audit: <product>\nCustomer value: \"<customer experiences ___, measured in ___>\"\nCandidates: (1) <metric> (2) <metric> (3) <metric>\n3-Criteria check: | Candidate | Value? | Strategy? | Leads rev? | Verdict |\nTime-shifted correlation lead: <weeks/months>\nPerverse-incentive risk: <what could go wrong> | Mitigation: <guardrail>\nChosen NSM: <NSM> — measured as <operational def>, reported <weekly/monthly>\nSupporting (NOT NSM): <metric> — <why supporting>\nRe-evaluation date: <next quarter>\n```\n\n*→ Method in Action: [Facebook's \"Seven Friends in Ten Days\" (2007–2010)](examples/facebook-seven-friends-in-ten-days-2007-2010.md)*\n\n## NSM Selection Packs\n\nTypical NSMs by domain: **content platforms** → minutes streamed per active user; **B2B SaaS** → active value moments per account (deals closed, tickets resolved); **marketplaces** → successful transactions; **freemium consumer apps** → core action completions. Adding a pack for your domain (typical NSMs, value-to-revenue mechanism, common mis-picks) is the easiest way to contribute.\n\n## Applying It Well\n\n- NSM is a leading indicator, not the goal. Revenue is the goal; NSM predicts it early enough to act.\n- NSM measures customer value, not company convenience. Easy-to-measure ≠ right NSM.\n- One metric only. Multiple \"north stars\" = strategy conflict, not a metric problem.\n- Stress-test for perverse incentives. The team will optimize whatever the NSM measures.\n- NSM evolves. Pre-commit to quarterly review.\n\n*→ Primary sources: [references/sources.md](references/sources.md)*\n\n## Common Rationalizations\n\n**[D] = designed upfront | [O] = observed in real use. [O] entries are more valuable.**\n\n| Fake move | Reality |\n|---|---|\n| [D] **Revenue as the NSM** | Revenue is the goal, not the leading indicator — lagging dashboard, not a steering wheel. |\n| [D] **Multiple \"north stars\"** | Multiple north stars defeat cross-team alignment; if you genuinely need multiple, you have a strategy conflict. |\n| [D] **Vanity metric promoted to NSM** | Page views, signups, app downloads move with marketing spend; they don't predict revenue. |\n| [D] **NSM fails perverse-incentive test** | If ruthless optimization of this metric hurts customers, the NSM is mis-chosen. Stress-test first. |\n| [D] **Treating the NSM as eternal** | Stage 1 → engagement; growth → retention; scale → revenue-adjacent. Re-evaluate quarterly. |\n| [D] **No operational definition** | \"Engagement\" is not an NSM. \"Users completing ≥3 core actions per week\" is. Specify the event. |\n| [D] **NSM as marketing/PR claim** | \"Making the world better\" is positioning, not a metric. NSM must be a number that goes up or down. |\n| *→ Add [O] entries here after each real use — paste the actual failure pattern* | *What went wrong and why* |\n\n## Red Flags\n\n- The NSM is revenue itself (lagging indicator)\n- The team has 3+ \"north star\" candidates and no decision among them\n- The NSM is a vanity metric (signups, page views, app downloads)\n- No perverse-incentive stress test was done\n- No time-shifted correlation with revenue has been examined\n- The NSM has no operational definition (e.g., \"engagement\" without specifying what counts)\n- The same NSM has been in place for 2+ years across very different business stages with no review\n\n## Verification\n\n- [ ] The customer value is articulated in customer units (not product mechanism)\n- [ ] 3–5 candidate metrics were generated\n- [ ] Each candidate passes all three criteria (customer value + strategy + leading indicator)\n- [ ] Time-shifted correlation with revenue is examined\n- [ ] Perverse-incentive stress test is done with named mitigations\n- [ ] Exactly one NSM is chosen with operational definition\n- [ ] Supporting metrics are explicitly named *as* supporting, not as NSMs\n- [ ] Quarterly re-evaluation is scheduled\n\n---\n\n*Part of **deciqAI Knowledge Skills** — 164 open-source thinking skills that make rigor executable for AI agents. The same skills power every deciqAI agent, which runs them autonomously to operate your company. **See it run → https://www.deciqai.com/c/north-star-metric** · ⭐ Star the repo → https://github.com/deciqAI/knowledge-skills · Contributions welcome.*\n\nFile v1.0.3:_meta.json\n\n{\n  \"ownerId\": \"kn754b8sk22s8c6gjxt02bftbn88q7ye\",\n  \"slug\": \"north-star-metric\",\n  \"version\": \"1.0.3\",\n  \"publishedAt\": 1783509143965\n}\n\nFile v1.0.3:references/sources.md\n\n# Sources — north-star-metric\n\n> *Primary sources for the [north-star-metric](../SKILL.md) skill.*\n\n- **Ellis, Sean.** \"The North Star Metric for Sustainable Growth.\" *GrowthHackers*, 2017. https://growthhackers.com/articles/north-star-metric **Primary source for the term**.\n- **Amplitude.** *The North Star Playbook* (2nd ed., Amplitude Inc., 2022). https://amplitude.com/north-star **Operational framework with the 3-criteria test**.\n- **Croll, Alistair & Yoskovitz, Benjamin.** *Lean Analytics: Use Data to Build a Better Startup Faster*. O'Reilly, 2013. **The \"One Metric That Matters\" formulation**.\n- **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**.\n- **Chen, Andrew.** Growth writings: https://andrewchen.com/ — useful secondary source for NSM patterns across companies.\n- 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.\n\nFile v1.0.3:examples/facebook-seven-friends-in-ten-days-2007-2010.md\n\n# Method in Action: Facebook's \"Seven Friends in Ten Days\" (2007–2010)\n\n> *Example for the [north-star-metric](../SKILL.md) skill.*\n\nA worked example. Not Silicon Valley legend — discussed publicly by Chamath Palihapitiya, head of Facebook's growth team 2007–2011, in lectures and interviews.\n\nWhen 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.\n\nThe 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.\n\nPalihapitiya, speaking publicly years later:\n\n> \"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.\"\n> — 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\n\nWalk the NSM Audit on Facebook 2007:\n\n- **Customer value (Step 1):** Users connected to their existing social network and using it as their primary social communication tool.\n- **Candidate metrics (Step 2):** Signups, DAU/MAU ratio, time spent, photos posted, messages sent, friends-added per user, friends-added-in-first-N-days.\n- **3-criteria check (Step 3):**\n  - Signups: customer value? weak (signup ≠ value). **Reject as NSM.**\n  - Time spent: customer value? mixed (could be addiction-y, not genuine value). **Supporting, not NSM.**\n  - 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 ✓.**\n- **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.\n- **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 on what counted.\n- **Pick (Step 6):** \"7 friends in 10 days\" became the growth team's official cross-team NSM for the 2007–2010 period. Every product change, signup flow optimization, friend-recommendation algorithm, and onboarding tweak was evaluated against this single number.\n- **Re-evaluation (Step 7):** As Facebook scaled past ~500M users and saturated the addressable graph, the NSM evolved — toward DAU/MAU ratio, then engagement quality, then mobile-DAU specifically, then ad-impressions-per-DAU. The NSM is not eternal.\n\nFacebook grew from ~50M users in 2007 to 1 billion in 2012. The growth team consistently credited the \"7 in 10\" NSM as the single most important focusing tool — *not because it was the most important business number* (revenue was), *but because it was the leading indicator that predicted revenue early enough to course-correct*.\n\n**Sources:** 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**. Croll, Alistair & Yoskovitz, Benjamin. *Lean Analytics: Use Data to Build a Better Startup Faster*. O'Reilly, 2013. **The \"OMTM\" framing**. Palihapitiya, Chamath. \"How We Put Facebook On The Path To 1 Billion Users.\" Stanford GSB lecture, ca. 2013. Public excerpts: https://www.businessinsider.com/the-secret-to-facebooks-explosive-growth-2014-12 **Primary source for the Facebook NSM case**. Andrew Chen's writings on growth metrics: https://andrewchen.com/\n\nFile v1.0.3:skill-card.md\n\n## Description: <br>\nGuides product teams through choosing a single North Star Metric that measures customer value, predicts revenue, and aligns cross-team optimization. <br>\n\nThis skill is ready for commercial/non-commercial use. <br>\n\n## Publisher: <br>\n[deciqai](https://clawhub.ai/user/deciqai) <br>\n\n### License/Terms of Use: <br>\nMIT-0 <br>\n\n\n## Use Case: <br>\nProduct leaders, founders, and strategy teams use this skill to run an NSM audit, prioritize one leading metric, test candidate metrics for customer value and revenue signal, and identify supporting metrics and guardrails. <br>\n\n### Deployment Geography for Use: <br>\nGlobal <br>\n\n## Known Risks and Mitigations: <br>\nRisk: Broad activation wording could route unrelated strategy or analytics requests to this skill. <br>\nMitigation: Narrow activation phrases or require explicit North Star Metric, NSM, OMTM, or one-metric prioritization intent when deploying alongside many strategy skills. <br>\nRisk: Metric recommendations could be misleading if accepted without business review or source data validation. <br>\nMitigation: Review the NSM audit with product and analytics owners, validate time-shifted revenue correlation, and keep supporting guardrail metrics before adoption. <br>\n\n\n## Reference(s): <br>\n- [ClawHub skill page](https://clawhub.ai/deciqai/skills/north-star-metric) <br>\n- [deciqai publisher profile](https://clawhub.ai/user/deciqai) <br>\n- [Sources - north-star-metric](references/sources.md) <br>\n- [Facebook seven friends in ten days example](examples/facebook-seven-friends-in-ten-days-2007-2010.md) <br>\n- [The North Star Metric for Sustainable Growth](https://growthhackers.com/articles/north-star-metric) <br>\n- [The North Star Playbook](https://amplitude.com/north-star) <br>\n- [Facebook growth case public excerpt](https://www.businessinsider.com/the-secret-to-facebooks-explosive-growth-2014-12) <br>\n- [deciqAI North Star Metric short link](https://www.deciqai.com/c/north-star-metric) <br>\n- [deciqAI knowledge-skills repository](https://github.com/deciqAI/knowledge-skills) <br>\n\n\n## Skill Output: <br>\n**Output Type(s):** [Guidance, Analysis, Markdown] <br>\n**Output Format:** [Markdown with structured audit sections and step-by-step coaching prompts] <br>\n**Output Parameters:** [1D] <br>\n**Other Properties Related to Output:** [Produces an NSM Audit with customer value, candidate metrics, criteria checks, revenue-leading signal, perverse-incentive risk, chosen NSM, supporting metrics, and review cadence.] <br>\n\n## Skill Version(s): <br>\n1.0.3 (source: server release metadata) <br>\n\n## Ethical Considerations: <br>\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment. <br>\n\nArchive v1.0.2: 5 files, 8692 bytes\n\nFiles: examples/facebook-seven-friends-in-ten-days-2007-2010.md (4675b), references/sources.md (1331b), skill-card.md (2680b), SKILL.md (7983b), _meta.json (136b)\n\nFile v1.0.2:SKILL.md\n\n---\nname: north-star-metric\ndescription: \"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?'\n  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.\"\n---\n\n# North Star Metric\n\n## Overview\n\nThe **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.\n\n**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.\n\n## When to Use\n\n**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.\"\n\n**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).\n\n## Coaching Novices (Adaptive Front Door)\n\n- **Engine mode:** user has a concrete case → run The Process directly.\n- **Coach mode:** user is unfamiliar or has no concrete case → guide step by step.\n\nIn Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.\n\n1. 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.\n2. Check fit against When to Use / When NOT to use. No customers → redirect to lean-startup. Single mature team → wrong scope.\n3. Elicit the product's core value in customer units (time saved, problem solved) — not the product feature.\n> **[WAIT — do not advance until user responds]**\n4. Walk: value → metric candidate → 3-criteria check → test against company strategy → pick. Pause at each.\n> **[WAIT — do not advance until user responds]**\n5. 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.\n> **[WAIT — do not advance until user responds]**\n\n## The Process\n\nRun the **NSM Audit**:\n\n1. **Articulate customer value in customer units** — the customer's outcome, not your product's mechanism.\n2. **Generate 3–5 NSM candidates** — each proxies that value as a measurable metric.\n3. **Apply the 3 criteria** (Amplitude §2): (a) Customer value? (b) Strategy fit? (c) Leads revenue? All three required; two-of-three = supporting metric only.\n4. **Time-shifted correlation** — does the candidate *lead* revenue over 6–12 months?\n5. **Perverse-incentive stress test** — could the team game this in a way that hurts customers?\n6. **Pick one. Put it on the wall.** Explicitly name supporting metrics as supporting, not NSMs.\n7. **Re-evaluate quarterly** — early stage → engagement; growth → retention; scale → revenue-adjacent.\n\n### Output: the NSM Audit\n\n```\nNSM Audit: <product>\nCustomer value: \"<customer experiences ___, measured in ___>\"\nCandidates: (1) <metric> (2) <metric> (3) <metric>\n3-Criteria check: | Candidate | Value? | Strategy? | Leads rev? | Verdict |\nTime-shifted correlation lead: <weeks/months>\nPerverse-incentive risk: <what could go wrong> | Mitigation: <guardrail>\nChosen NSM: <NSM> — measured as <operational def>, reported <weekly/monthly>\nSupporting (NOT NSM): <metric> — <why supporting>\nRe-evaluation date: <next quarter>\n```\n\n*→ Method in Action: [Facebook's \"Seven Friends in Ten Days\" (2007–2010)](examples/facebook-seven-friends-in-ten-days-2007-2010.md)*\n\n## NSM Selection Packs\n\nTypical NSMs by domain: **content platforms** → minutes streamed per active user; **B2B SaaS** → active value moments per account (deals closed, tickets resolved); **marketplaces** → successful transactions; **freemium consumer apps** → core action completions. Adding a pack for your domain (typical NSMs, value-to-revenue mechanism, common mis-picks) is the easiest way to contribute.\n\n## Applying It Well\n\n- NSM is a leading indicator, not the goal. Revenue is the goal; NSM predicts it early enough to act.\n- NSM measures customer value, not company convenience. Easy-to-measure ≠ right NSM.\n- One metric only. Multiple \"north stars\" = strategy conflict, not a metric problem.\n- Stress-test for perverse incentives. The team will optimize whatever the NSM measures.\n- NSM evolves. Pre-commit to quarterly review.\n\n*→ Primary sources: [references/sources.md](references/sources.md)*\n\n## Common Rationalizations\n\n**[D] = designed upfront | [O] = observed in real use. [O] entries are more valuable.**\n\n| Fake move | Reality |\n|---|---|\n| [D] **Revenue as the NSM** | Revenue is the goal, not the leading indicator — lagging dashboard, not a steering wheel. |\n| [D] **Multiple \"north stars\"** | Multiple north stars defeat cross-team alignment; if you genuinely need multiple, you have a strategy conflict. |\n| [D] **Vanity metric promoted to NSM** | Page views, signups, app downloads move with marketing spend; they don't predict revenue. |\n| [D] **NSM fails perverse-incentive test** | If ruthless optimization of this metric hurts customers, the NSM is mis-chosen. Stress-test first. |\n| [D] **Treating the NSM as eternal** | Stage 1 → engagement; growth → retention; scale → revenue-adjacent. Re-evaluate quarterly. |\n| [D] **No operational definition** | \"Engagement\" is not an NSM. \"Users completing ≥3 core actions per week\" is. Specify the event. |\n| [D] **NSM as marketing/PR claim** | \"Making the world better\" is positioning, not a metric. NSM must be a number that goes up or down. |\n| *→ Add [O] entries here after each real use — paste the actual failure pattern* | *What went wrong and why* |\n\n## Red Flags\n\n- The NSM is revenue itself (lagging indicator)\n- The team has 3+ \"north star\" candidates and no decision among them\n- The NSM is a vanity metric (signups, page views, app downloads)\n- No perverse-incentive stress test was done\n- No time-shifted correlation with revenue has been examined\n- The NSM has no operational definition (e.g., \"engagement\" without specifying what counts)\n- The same NSM has been in place for 2+ years across very different business stages with no review\n\n## Verification\n\n- [ ] The customer value is articulated in customer units (not product mechanism)\n- [ ] 3–5 candidate metrics were generated\n- [ ] Each candidate passes all three criteria (customer value + strategy + leading indicator)\n- [ ] Time-shifted correlation with revenue is examined\n- [ ] Perverse-incentive stress test is done with named mitigations\n- [ ] Exactly one NSM is chosen with operational definition\n- [ ] Supporting metrics are explicitly named *as* supporting, not as NSMs\n- [ ] Quarterly re-evaluation is scheduled\n\n---\n\n*Part of **deciqAI Knowledge Skills** — 163 open-source thinking skills that make rigor executable for AI agents. The same skills power every deciqAI agent, which runs them autonomously to operate your company. **See it run → https://www.deciqai.com/skills/north-star-metric?utm_source=clawhub&utm_medium=marketplace&utm_campaign=knowledge-skills&utm_content=north-star-metric** · ⭐ Star the repo → https://github.com/deciqAI/knowledge-skills · Contributions welcome.*\n\nFile v1.0.2:_meta.json\n\n{\n  \"ownerId\": \"kn754b8sk22s8c6gjxt02bftbn88q7ye\",\n  \"slug\": \"north-star-metric\",\n  \"version\": \"1.0.2\",\n  \"publishedAt\": 1783472228538\n}\n\nFile v1.0.2:references/sources.md\n\n# Sources — north-star-metric\n\n> *Primary sources for the [north-star-metric](../SKILL.md) skill.*\n\n- **Ellis, Sean.** \"The North Star Metric for Sustainable Growth.\" *GrowthHackers*, 2017. https://growthhackers.com/articles/north-star-metric **Primary source for the term**.\n- **Amplitude.** *The North Star Playbook* (2nd ed., Amplitude Inc., 2022). https://amplitude.com/north-star **Operational framework with the 3-criteria test**.\n- **Croll, Alistair & Yoskovitz, Benjamin.** *Lean Analytics: Use Data to Build a Better Startup Faster*. O'Reilly, 2013. **The \"One Metric That Matters\" formulation**.\n- **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**.\n- **Chen, Andrew.** Growth writings: https://andrewchen.com/ — useful secondary source for NSM patterns across companies.\n- 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.\n\nFile v1.0.2:examples/facebook-seven-friends-in-ten-days-2007-2010.md\n\n# Method in Action: Facebook's \"Seven Friends in Ten Days\" (2007–2010)\n\n> *Example for the [north-star-metric](../SKILL.md) skill.*\n\nA worked example. Not Silicon Valley legend — discussed publicly by Chamath Palihapitiya, head of Facebook's growth team 2007–2011, in lectures and interviews.\n\nWhen 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.\n\nThe 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.\n\nPalihapitiya, speaking publicly years later:\n\n> \"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.\"\n> — 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\n\nWalk the NSM Audit on Facebook 2007:\n\n- **Customer value (Step 1):** Users connected to their existing social network and using it as their primary social communication tool.\n- **Candidate metrics (Step 2):** Signups, DAU/MAU ratio, time spent, photos posted, messages sent, friends-added per user, friends-added-in-first-N-days.\n- **3-criteria check (Step 3):**\n  - Signups: customer value? weak (signup ≠ value). **Reject as NSM.**\n  - Time spent: customer value? mixed (could be addiction-y, not genuine value). **Supporting, not NSM.**\n  - 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 ✓.**\n- **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.\n- **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 on what counted.\n- **Pick (Step 6):** \"7 friends in 10 days\" became the growth team's official cross-team NSM for the 2007–2010 period. Every product change, signup flow optimization, friend-recommendation algorithm, and onboarding tweak was evaluated against this single number.\n- **Re-evaluation (Step 7):** As Facebook scaled past ~500M users and saturated the addressable graph, the NSM evolved — toward DAU/MAU ratio, then engagement quality, then mobile-DAU specifically, then ad-impressions-per-DAU. The NSM is not eternal.\n\nFacebook grew from ~50M users in 2007 to 1 billion in 2012. The growth team consistently credited the \"7 in 10\" NSM as the single most important focusing tool — *not because it was the most important business number* (revenue was), *but because it was the leading indicator that predicted revenue early enough to course-correct*.\n\n**Sources:** 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**. Croll, Alistair & Yoskovitz, Benjamin. *Lean Analytics: Use Data to Build a Better Startup Faster*. O'Reilly, 2013. **The \"OMTM\" framing**. Palihapitiya, Chamath. \"How We Put Facebook On The Path To 1 Billion Users.\" Stanford GSB lecture, ca. 2013. Public excerpts: https://www.businessinsider.com/the-secret-to-facebooks-explosive-growth-2014-12 **Primary source for the Facebook NSM case**. Andrew Chen's writings on growth metrics: https://andrewchen.com/\n\nFile v1.0.2:skill-card.md\n\n## Description: <br>\nGuides agents through a North Star Metric audit to help teams choose one leading indicator of customer value that predicts revenue over time. <br>\n\nThis skill is ready for commercial/non-commercial use. <br>\n\n## Publisher: <br>\n[deciqai](https://clawhub.ai/user/deciqai) <br>\n\n### License/Terms of Use: <br>\nMIT-0 <br>\n\n\n## Use Case: <br>\nProduct teams, founders, operators, and agents use this skill when they need to align around a single cross-team metric, generate and test candidate North Star Metrics, and separate the chosen NSM from supporting metrics. <br>\n\n### Deployment Geography for Use: <br>\nGlobal <br>\n\n## Known Risks and Mitigations: <br>\nRisk: Broad activation phrases such as \"what should we optimize?\" may cause the skill to activate in adjacent planning conversations. <br>\nMitigation: Confirm the user needs cross-team metric selection and redirect when the case is pre-customer, single-team KPI execution, or unresolved strategy rather than metric selection. <br>\nRisk: A poorly chosen metric can create perverse incentives or encourage teams to game the measurement instead of improving customer value. <br>\nMitigation: Use the skill's built-in perverse-incentive stress test, define guardrail metrics, and require one operational definition before treating a candidate as the North Star Metric. <br>\n\n\n## Reference(s): <br>\n- [Sources - north-star-metric](references/sources.md) <br>\n- [Facebook's Seven Friends in Ten Days example](examples/facebook-seven-friends-in-ten-days-2007-2010.md) <br>\n- [Sean Ellis, The North Star Metric for Sustainable Growth](https://growthhackers.com/articles/north-star-metric) <br>\n- [Amplitude, The North Star Playbook](https://amplitude.com/north-star) <br>\n- [Business Insider excerpt on Facebook growth](https://www.businessinsider.com/the-secret-to-facebooks-explosive-growth-2014-12) <br>\n- [Andrew Chen growth writings](https://andrewchen.com/) <br>\n\n\n## Skill Output: <br>\n**Output Type(s):** [text, markdown, guidance] <br>\n**Output Format:** [Markdown text with an NSM Audit template and decision table] <br>\n**Output Parameters:** [1D] <br>\n**Other Properties Related to Output:** [May include step-by-step coaching questions, candidate metric analysis, perverse-incentive risks, mitigations, and a chosen NSM with supporting metrics.] <br>\n\n## Skill Version(s): <br>\n1.0.2 (source: server release metadata) <br>\n\n## Ethical Considerations: <br>\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment. <br>\n\nArchive v1.0.1: 5 files, 8628 bytes\n\nFiles: examples/facebook-seven-friends-in-ten-days-2007-2010.md (4675b), references/sources.md (1331b), skill-card.md (2823b), SKILL.md (7855b), _meta.json (136b)\n\nFile v1.0.1:SKILL.md\n\n---\nname: north-star-metric\ndescription: \"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?'\n  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.\"\n---\n\n# North Star Metric\n\n## Overview\n\nThe **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.\n\n**Compose:** [aarrr-pirate-metrics](../aarrr-pirate-metrics/SKILL.md) instruments the full funnel; NSM elevates one funnel metric to cross-team primacy. [first-principles](../first-principles/SKILL.md) clarifies what value the product actually delivers. [pmf-crossing-the-chasm](../pmf-crossing-the-chasm/SKILL.md) — the NSM is typically an Activation- or Retention-stage metric.\n\n## When to Use\n\n**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.\"\n\n**Do NOT use when:** product has no customers (pre-PMF → use [lean-startup](../lean-startup/SKILL.md)); single-team execution in a mature business; genuinely conflicting strategic objectives (the strategy needs work, not a metric).\n\n## Coaching Novices (Adaptive Front Door)\n\n- **Engine mode:** user has a concrete case → run The Process directly.\n- **Coach mode:** user is unfamiliar or has no concrete case → guide step by step.\n\nIn Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.\n\n1. 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.\n2. Check fit against When to Use / When NOT to use. No customers → redirect to lean-startup. Single mature team → wrong scope.\n3. Elicit the product's core value in customer units (time saved, problem solved) — not the product feature.\n> **[WAIT — do not advance until user responds]**\n4. Walk: value → metric candidate → 3-criteria check → test against company strategy → pick. Pause at each.\n> **[WAIT — do not advance until user responds]**\n5. 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.\n> **[WAIT — do not advance until user responds]**\n\n## The Process\n\nRun the **NSM Audit**:\n\n1. **Articulate customer value in customer units** — the customer's outcome, not your product's mechanism.\n2. **Generate 3–5 NSM candidates** — each proxies that value as a measurable metric.\n3. **Apply the 3 criteria** (Amplitude §2): (a) Customer value? (b) Strategy fit? (c) Leads revenue? All three required; two-of-three = supporting metric only.\n4. **Time-shifted correlation** — does the candidate *lead* revenue over 6–12 months?\n5. **Perverse-incentive stress test** — could the team game this in a way that hurts customers?\n6. **Pick one. Put it on the wall.** Explicitly name supporting metrics as supporting, not NSMs.\n7. **Re-evaluate quarterly** — early stage → engagement; growth → retention; scale → revenue-adjacent.\n\n### Output: the NSM Audit\n\n```\nNSM Audit: <product>\nCustomer value: \"<customer experiences ___, measured in ___>\"\nCandidates: (1) <metric> (2) <metric> (3) <metric>\n3-Criteria check: | Candidate | Value? | Strategy? | Leads rev? | Verdict |\nTime-shifted correlation lead: <weeks/months>\nPerverse-incentive risk: <what could go wrong> | Mitigation: <guardrail>\nChosen NSM: <NSM> — measured as <operational def>, reported <weekly/monthly>\nSupporting (NOT NSM): <metric> — <why supporting>\nRe-evaluation date: <next quarter>\n```\n\n*→ Method in Action: [Facebook's \"Seven Friends in Ten Days\" (2007–2010)](examples/facebook-seven-friends-in-ten-days-2007-2010.md)*\n\n## NSM Selection Packs\n\nTypical NSMs by domain: **content platforms** → minutes streamed per active user; **B2B SaaS** → active value moments per account (deals closed, tickets resolved); **marketplaces** → successful transactions; **freemium consumer apps** → core action completions. Adding a pack for your domain (typical NSMs, value-to-revenue mechanism, common mis-picks) is the easiest way to contribute.\n\n## Applying It Well\n\n- NSM is a leading indicator, not the goal. Revenue is the goal; NSM predicts it early enough to act.\n- NSM measures customer value, not company convenience. Easy-to-measure ≠ right NSM.\n- One metric only. Multiple \"north stars\" = strategy conflict, not a metric problem.\n- Stress-test for perverse incentives. The team will optimize whatever the NSM measures.\n- NSM evolves. Pre-commit to quarterly review.\n\n*→ Primary sources: [references/sources.md](references/sources.md)*\n\n## Common Rationalizations\n\n**[D] = designed upfront | [O] = observed in real use. [O] entries are more valuable.**\n\n| Fake move | Reality |\n|---|---|\n| [D] **Revenue as the NSM** | Revenue is the goal, not the leading indicator — lagging dashboard, not a steering wheel. |\n| [D] **Multiple \"north stars\"** | Multiple north stars defeat cross-team alignment; if you genuinely need multiple, you have a strategy conflict. |\n| [D] **Vanity metric promoted to NSM** | Page views, signups, app downloads move with marketing spend; they don't predict revenue. |\n| [D] **NSM fails perverse-incentive test** | If ruthless optimization of this metric hurts customers, the NSM is mis-chosen. Stress-test first. |\n| [D] **Treating the NSM as eternal** | Stage 1 → engagement; growth → retention; scale → revenue-adjacent. Re-evaluate quarterly. |\n| [D] **No operational definition** | \"Engagement\" is not an NSM. \"Users completing ≥3 core actions per week\" is. Specify the event. |\n| [D] **NSM as marketing/PR claim** | \"Making the world better\" is positioning, not a metric. NSM must be a number that goes up or down. |\n| *→ Add [O] entries here after each real use — paste the actual failure pattern* | *What went wrong and why* |\n\n## Red Flags\n\n- The NSM is revenue itself (lagging indicator)\n- The team has 3+ \"north star\" candidates and no decision among them\n- The NSM is a vanity metric (signups, page views, app downloads)\n- No perverse-incentive stress test was done\n- No time-shifted correlation with revenue has been examined\n- The NSM has no operational definition (e.g., \"engagement\" without specifying what counts)\n- The same NSM has been in place for 2+ years across very different business stages with no review\n\n## Verification\n\n- [ ] The customer value is articulated in customer units (not product mechanism)\n- [ ] 3–5 candidate metrics were generated\n- [ ] Each candidate passes all three criteria (customer value + strategy + leading indicator)\n- [ ] Time-shifted correlation with revenue is examined\n- [ ] Perverse-incentive stress test is done with named mitigations\n- [ ] Exactly one NSM is chosen with operational definition\n- [ ] Supporting metrics are explicitly named *as* supporting, not as NSMs\n- [ ] Quarterly re-evaluation is scheduled\n\n---\n\n*Part of **deciqAI Knowledge Skills** — open-source thinking skills that make rigor executable for AI agents. Built by deciqAI · https://deciqai.com · Contributions welcome — see the template at the repo root.*\n\nFile v1.0.1:_meta.json\n\n{\n  \"ownerId\": \"kn754b8sk22s8c6gjxt02bftbn88q7ye\",\n  \"slug\": \"north-star-metric\",\n  \"version\": \"1.0.1\",\n  \"publishedAt\": 1783463407286\n}\n\nFile v1.0.1:references/sources.md\n\n# Sources — north-star-metric\n\n> *Primary sources for the [north-star-metric](../SKILL.md) skill.*\n\n- **Ellis, Sean.** \"The North Star Metric for Sustainable Growth.\" *GrowthHackers*, 2017. https://growthhackers.com/articles/north-star-metric **Primary source for the term**.\n- **Amplitude.** *The North Star Playbook* (2nd ed., Amplitude Inc., 2022). https://amplitude.com/north-star **Operational framework with the 3-criteria test**.\n- **Croll, Alistair & Yoskovitz, Benjamin.** *Lean Analytics: Use Data to Build a Better Startup Faster*. O'Reilly, 2013. **The \"One Metric That Matters\" formulation**.\n- **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**.\n- **Chen, Andrew.** Growth writings: https://andrewchen.com/ — useful secondary source for NSM patterns across companies.\n- 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.\n\nFile v1.0.1:examples/facebook-seven-friends-in-ten-days-2007-2010.md\n\n# Method in Action: Facebook's \"Seven Friends in Ten Days\" (2007–2010)\n\n> *Example for the [north-star-metric](../SKILL.md) skill.*\n\nA worked example. Not Silicon Valley legend — discussed publicly by Chamath Palihapitiya, head of Facebook's growth team 2007–2011, in lectures and interviews.\n\nWhen 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.\n\nThe 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.\n\nPalihapitiya, speaking publicly years later:\n\n> \"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.\"\n> — 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\n\nWalk the NSM Audit on Facebook 2007:\n\n- **Customer value (Step 1):** Users connected to their existing social network and using it as their primary social communication tool.\n- **Candidate metrics (Step 2):** Signups, DAU/MAU ratio, time spent, photos posted, messages sent, friends-added per user, friends-added-in-first-N-days.\n- **3-criteria check (Step 3):**\n  - Signups: customer value? weak (signup ≠ value). **Reject as NSM.**\n  - Time spent: customer value? mixed (could be addiction-y, not genuine value). **Supporting, not NSM.**\n  - 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 ✓.**\n- **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.\n- **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 on what counted.\n- **Pick (Step 6):** \"7 friends in 10 days\" became the growth team's official cross-team NSM for the 2007–2010 period. Every product change, signup flow optimization, friend-recommendation algorithm, and onboarding tweak was evaluated against this single number.\n- **Re-evaluation (Step 7):** As Facebook scaled past ~500M users and saturated the addressable graph, the NSM evolved — toward DAU/MAU ratio, then engagement quality, then mobile-DAU specifically, then ad-impressions-per-DAU. The NSM is not eternal.\n\nFacebook grew from ~50M users in 2007 to 1 billion in 2012. The growth team consistently credited the \"7 in 10\" NSM as the single most important focusing tool — *not because it was the most important business number* (revenue was), *but because it was the leading indicator that predicted revenue early enough to course-correct*.\n\n**Sources:** 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**. Croll, Alistair & Yoskovitz, Benjamin. *Lean Analytics: Use Data to Build a Better Startup Faster*. O'Reilly, 2013. **The \"OMTM\" framing**. Palihapitiya, Chamath. \"How We Put Facebook On The Path To 1 Billion Users.\" Stanford GSB lecture, ca. 2013. Public excerpts: https://www.businessinsider.com/the-secret-to-facebooks-explosive-growth-2014-12 **Primary source for the Facebook NSM case**. Andrew Chen's writings on growth metrics: https://andrewchen.com/\n\nFile v1.0.1:skill-card.md\n\n## Description: <br>\nGuides agents through a North Star Metric audit to select one leading product metric that measures customer value, predicts revenue, and aligns teams around a single priority. <br>\n\nThis skill is ready for commercial/non-commercial use. <br>\n\n## Publisher: <br>\n[deciqai](https://clawhub.ai/user/deciqai) <br>\n\n### License/Terms of Use: <br>\nMIT-0 <br>\n\n\n## Use Case: <br>\nProduct teams, founders, growth operators, and agents use this skill when teams disagree about which metric to optimize or need a leading indicator of revenue. It produces a structured NSM Audit with candidate metrics, criteria checks, perverse-incentive review, a chosen North Star Metric, supporting metrics, and a re-evaluation date. <br>\n\n### Deployment Geography for Use: <br>\nGlobal <br>\n\n## Known Risks and Mitigations: <br>\nRisk: The skill may activate for broad product optimization wording even when a North Star Metric audit is not the right workflow. <br>\nMitigation: Review the trigger language against local workflows and use the skill's fit checks before applying it. <br>\nRisk: Metric recommendations can mislead teams if the product has no customers, the problem is single-team execution, or the strategy itself is conflicted. <br>\nMitigation: Apply the documented Do NOT use checks and require customer-value framing, time-shifted correlation, perverse-incentive testing, and quarterly re-evaluation. <br>\n\n\n## Reference(s): <br>\n- [ClawHub release page](https://clawhub.ai/deciqai/skills/north-star-metric) <br>\n- [Sources - north-star-metric](artifact/references/sources.md) <br>\n- [Facebook seven friends in ten days example](artifact/examples/facebook-seven-friends-in-ten-days-2007-2010.md) <br>\n- [The North Star Metric for Sustainable Growth](https://growthhackers.com/articles/north-star-metric) <br>\n- [The North Star Playbook](https://amplitude.com/north-star) <br>\n- [Facebook growth lecture excerpt](https://www.businessinsider.com/the-secret-to-facebooks-explosive-growth-2014-12) <br>\n- [Andrew Chen growth writings](https://andrewchen.com/) <br>\n\n\n## Skill Output: <br>\n**Output Type(s):** [guidance, markdown, text] <br>\n**Output Format:** [Markdown NSM Audit with structured metric candidates, criteria checks, risk mitigation, a chosen NSM, supporting metrics, and a re-evaluation date.] <br>\n**Output Parameters:** [1D] <br>\n**Other Properties Related to Output:** [May pause for user input in coaching mode before completing the audit.] <br>\n\n## Skill Version(s): <br>\n1.0.1 (source: ClawHub release evidence) <br>\n\n## Ethical Considerations: <br>\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment. <br>\n\nArchive v1.0.0: 5 files, 8563 bytes\n\nFiles: examples/facebook-seven-friends-in-ten-days-2007-2010.md (4675b), references/sources.md (1331b), skill-card.md (2586b), SKILL.md (7855b), _meta.json (136b)\n\nFile v1.0.0:SKILL.md\n\n---\nname: north-star-metric\ndescription: \"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?'\n  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.\"\n---\n\n# North Star Metric\n\n## Overview\n\nThe **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.\n\n**Compose:** [aarrr-pirate-metrics](../aarrr-pirate-metrics/SKILL.md) instruments the full funnel; NSM elevates one funnel metric to cross-team primacy. [first-principles](../first-principles/SKILL.md) clarifies what value the product actually delivers. [pmf-crossing-the-chasm](../pmf-crossing-the-chasm/SKILL.md) — the NSM is typically an Activation- or Retention-stage metric.\n\n## When to Use\n\n**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.\"\n\n**Do NOT use when:** product has no customers (pre-PMF → use [lean-startup](../lean-startup/SKILL.md)); single-team execution in a mature business; genuinely conflicting strategic objectives (the strategy needs work, not a metric).\n\n## Coaching Novices (Adaptive Front Door)\n\n- **Engine mode:** user has a concrete case → run The Process directly.\n- **Coach mode:** user is unfamiliar or has no concrete case → guide step by step.\n\nIn Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.\n\n1. 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.\n2. Check fit against When to Use / When NOT to use. No customers → redirect to lean-startup. Single mature team → wrong scope.\n3. Elicit the product's core value in customer units (time saved, problem solved) — not the product feature.\n> **[WAIT — do not advance until user responds]**\n4. Walk: value → metric candidate → 3-criteria check → test against company strategy → pick. Pause at each.\n> **[WAIT — do not advance until user responds]**\n5. 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.\n> **[WAIT — do not advance until user responds]**\n\n## The Process\n\nRun the **NSM Audit**:\n\n1. **Articulate customer value in customer units** — the customer's outcome, not your product's mechanism.\n2. **Generate 3–5 NSM candidates** — each proxies that value as a measurable metric.\n3. **Apply the 3 criteria** (Amplitude §2): (a) Customer value? (b) Strategy fit? (c) Leads revenue? All three required; two-of-three = supporting metric only.\n4. **Time-shifted correlation** — does the candidate *lead* revenue over 6–12 months?\n5. **Perverse-incentive stress test** — could the team game this in a way that hurts customers?\n6. **Pick one. Put it on the wall.** Explicitly name supporting metrics as supporting, not NSMs.\n7. **Re-evaluate quarterly** — early stage → engagement; growth → retention; scale → revenue-adjacent.\n\n### Output: the NSM Audit\n\n```\nNSM Audit: <product>\nCustomer value: \"<customer experiences ___, measured in ___>\"\nCandidates: (1) <metric> (2) <metric> (3) <metric>\n3-Criteria check: | Candidate | Value? | Strategy? | Leads rev? | Verdict |\nTime-shifted correlation lead: <weeks/months>\nPerverse-incentive risk: <what could go wrong> | Mitigation: <guardrail>\nChosen NSM: <NSM> — measured as <operational def>, reported <weekly/monthly>\nSupporting (NOT NSM): <metric> — <why supporting>\nRe-evaluation date: <next quarter>\n```\n\n*→ Method in Action: [Facebook's \"Seven Friends in Ten Days\" (2007–2010)](examples/facebook-seven-friends-in-ten-days-2007-2010.md)*\n\n## NSM Selection Packs\n\nTypical NSMs by domain: **content platforms** → minutes streamed per active user; **B2B SaaS** → active value moments per account (deals closed, tickets resolved); **marketplaces** → successful transactions; **freemium consumer apps** → core action completions. Adding a pack for your domain (typical NSMs, value-to-revenue mechanism, common mis-picks) is the easiest way to contribute.\n\n## Applying It Well\n\n- NSM is a leading indicator, not the goal. Revenue is the goal; NSM predicts it early enough to act.\n- NSM measures customer value, not company convenience. Easy-to-measure ≠ right NSM.\n- One metric only. Multiple \"north stars\" = strategy conflict, not a metric problem.\n- Stress-test for perverse incentives. The team will optimize whatever the NSM measures.\n- NSM evolves. Pre-commit to quarterly review.\n\n*→ Primary sources: [references/sources.md](references/sources.md)*\n\n## Common Rationalizations\n\n**[D] = designed upfront | [O] = observed in real use. [O] entries are more valuable.**\n\n| Fake move | Reality |\n|---|---|\n| [D] **Revenue as the NSM** | Revenue is the goal, not the leading indicator — lagging dashboard, not a steering wheel. |\n| [D] **Multiple \"north stars\"** | Multiple north stars defeat cross-team alignment; if you genuinely need multiple, you have a strategy conflict. |\n| [D] **Vanity metric promoted to NSM** | Page views, signups, app downloads move with marketing spend; they don't predict revenue. |\n| [D] **NSM fails perverse-incentive test** | If ruthless optimization of this metric hurts customers, the NSM is mis-chosen. Stress-test first. |\n| [D] **Treating the NSM as eternal** | Stage 1 → engagement; growth → retention; scale → revenue-adjacent. Re-evaluate quarterly. |\n| [D] **No operational definition** | \"Engagement\" is not an NSM. \"Users completing ≥3 core actions per week\" is. Specify the event. |\n| [D] **NSM as marketing/PR claim** | \"Making the world better\" is positioning, not a metric. NSM must be a number that goes up or down. |\n| *→ Add [O] entries here after each real use — paste the actual failure pattern* | *What went wrong and why* |\n\n## Red Flags\n\n- The NSM is revenue itself (lagging indicator)\n- The team has 3+ \"north star\" candidates and no decision among them\n- The NSM is a vanity metric (signups, page views, app downloads)\n- No perverse-incentive stress test was done\n- No time-shifted correlation with revenue has been examined\n- The NSM has no operational definition (e.g., \"engagement\" without specifying what counts)\n- The same NSM has been in place for 2+ years across very different business stages with no review\n\n## Verification\n\n- [ ] The customer value is articulated in customer units (not product mechanism)\n- [ ] 3–5 candidate metrics were generated\n- [ ] Each candidate passes all three criteria (customer value + strategy + leading indicator)\n- [ ] Time-shifted correlation with revenue is examined\n- [ ] Perverse-incentive stress test is done with named mitigations\n- [ ] Exactly one NSM is chosen with operational definition\n- [ ] Supporting metrics are explicitly named *as* supporting, not as NSMs\n- [ ] Quarterly re-evaluation is scheduled\n\n---\n\n*Part of **deciqAI Knowledge Skills** — open-source thinking skills that make rigor executable for AI agents. Built by deciqAI · https://deciqai.com · Contributions welcome — see the template at the repo root.*\n\nFile v1.0.0:_meta.json\n\n{\n  \"ownerId\": \"kn754b8sk22s8c6gjxt02bftbn88q7ye\",\n  \"slug\": \"north-star-metric\",\n  \"version\": \"1.0.0\",\n  \"publishedAt\": 1782825513070\n}\n\nFile v1.0.0:references/sources.md\n\n# Sources — north-star-metric\n\n> *Primary sources for the [north-star-metric](../SKILL.md) skill.*\n\n- **Ellis, Sean.** \"The North Star Metric for Sustainable Growth.\" *GrowthHackers*, 2017. https://growthhackers.com/articles/north-star-metric **Primary source for the term**.\n- **Amplitude.** *The North Star Playbook* (2nd ed., Amplitude Inc., 2022). https://amplitude.com/north-star **Operational framework with the 3-criteria test**.\n- **Croll, Alistair & Yoskovitz, Benjamin.** *Lean Analytics: Use Data to Build a Better Startup Faster*. O'Reilly, 2013. **The \"One Metric That Matters\" formulation**.\n- **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**.\n- **Chen, Andrew.** Growth writings: https://andrewchen.com/ — useful secondary source for NSM patterns across companies.\n- 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.\n\nFile v1.0.0:examples/facebook-seven-friends-in-ten-days-2007-2010.md\n\n# Method in Action: Facebook's \"Seven Friends in Ten Days\" (2007–2010)\n\n> *Example for the [north-star-metric](../SKILL.md) skill.*\n\nA worked example. Not Silicon Valley legend — discussed publicly by Chamath Palihapitiya, head of Facebook's growth team 2007–2011, in lectures and interviews.\n\nWhen 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.\n\nThe 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.\n\nPalihapitiya, speaking publicly years later:\n\n> \"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.\"\n> — 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\n\nWalk the NSM Audit on Facebook 2007:\n\n- **Customer value (Step 1):** Users connected to their existing social network and using it as their primary social communication tool.\n- **Candidate metrics (Step 2):** Signups, DAU/MAU ratio, time spent, photos posted, messages sent, friends-added per user, friends-added-in-first-N-days.\n- **3-criteria check (Step 3):**\n  - Signups: customer value? weak (signup ≠ value). **Reject as NSM.**\n  - Time spent: customer value? mixed (could be addiction-y, not genuine value). **Supporting, not NSM.**\n  - 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 ✓.**\n- **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.\n- **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 on what counted.\n- **Pick (Step 6):** \"7 friends in 10 days\" became the growth team's official cross-team NSM for the 2007–2010 period. Every product change, signup flow optimization, friend-recommendation algorithm, and onboarding tweak was evaluated against this single number.\n- **Re-evaluation (Step 7):** As Facebook scaled past ~500M users and saturated the addressable graph, the NSM evolved — toward DAU/MAU ratio, then engagement quality, then mobile-DAU specifically, then ad-impressions-per-DAU. The NSM is not eternal.\n\nFacebook grew from ~50M users in 2007 to 1 billion in 2012. The growth team consistently credited the \"7 in 10\" NSM as the single most important focusing tool — *not because it was the most important business number* (revenue was), *but because it was the leading indicator that predicted revenue early enough to course-correct*.\n\n**Sources:** 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**. Croll, Alistair & Yoskovitz, Benjamin. *Lean Analytics: Use Data to Build a Better Startup Faster*. O'Reilly, 2013. **The \"OMTM\" framing**. Palihapitiya, Chamath. \"How We Put Facebook On The Path To 1 Billion Users.\" Stanford GSB lecture, ca. 2013. Public excerpts: https://www.businessinsider.com/the-secret-to-facebooks-explosive-growth-2014-12 **Primary source for the Facebook NSM case**. Andrew Chen's writings on growth metrics: https://andrewchen.com/\n\nFile v1.0.0:skill-card.md\n\n## Description: <br>\nHelps teams select a North Star Metric by auditing customer value, candidate metrics, strategy fit, revenue-leading behavior, and perverse-incentive risks. <br>\n\nThis skill is ready for commercial/non-commercial use. <br>\n\n## Publisher: <br>\n[deciqai](https://clawhub.ai/user/deciqai) <br>\n\n### License/Terms of Use: <br>\nMIT-0 <br>\n\n\n## Use Case: <br>\nProduct leaders, growth teams, founders, and strategy facilitators use this skill to align teams around one leading metric that reflects customer value and predicts revenue. It is most useful when a team has too many competing metrics or needs a structured NSM audit. <br>\n\n### Deployment Geography for Use: <br>\nGlobal <br>\n\n## Known Risks and Mitigations: <br>\nRisk: The skill may steer broad metrics discussions toward North Star Metric framing too early. <br>\nMitigation: Use the fit checks in the skill before running the NSM audit, and override or ignore the framing when the team only needs tactical KPIs or early discovery support. <br>\nRisk: An incorrectly chosen NSM can create perverse incentives that optimize a metric while harming customer value. <br>\nMitigation: Run the perverse-incentive stress test and define guardrail metrics before treating the chosen NSM as cross-team guidance. <br>\n\n\n## Reference(s): <br>\n- [Sources - north-star-metric](references/sources.md) <br>\n- [Method in Action: Facebook's \"Seven Friends in Ten Days\" (2007-2010)](examples/facebook-seven-friends-in-ten-days-2007-2010.md) <br>\n- [The North Star Metric for Sustainable Growth](https://growthhackers.com/articles/north-star-metric) <br>\n- [The North Star Playbook](https://amplitude.com/north-star) <br>\n- [Facebook growth case excerpt](https://www.businessinsider.com/the-secret-to-facebooks-explosive-growth-2014-12) <br>\n- [Andrew Chen growth writings](https://andrewchen.com/) <br>\n\n\n## Skill Output: <br>\n**Output Type(s):** [Analysis, Markdown, Guidance] <br>\n**Output Format:** [Markdown] <br>\n**Output Parameters:** [1D] <br>\n**Other Properties Related to Output:** [Produces a structured NSM Audit with candidate metrics, criteria checks, correlation lead, perverse-incentive risk, mitigation, chosen NSM, supporting metrics, and re-evaluation timing.] <br>\n\n## Skill Version(s): <br>\n1.0.0 (source: server release metadata) <br>\n\n## Ethical Considerations: <br>\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment. <br>","readmeExcerpt":"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","codeSnippets":[],"executableExamples":[{"language":"text","snippet":"NSM Audit: <product>\nCustomer value: \"<customer experiences ___, measured in ___>\"\nCandidates: (1) <metric> (2) <metric> (3) <metric>\n3-Criteria check: | Candidate | Value? | Strategy? | Leads rev? | Verdict |\nTime-shifted correlation lead: <weeks/months>\nPerverse-incentive risk: <what could go wrong> | Mitigation: <guardrail>\nChosen NSM: <NSM> — measured as <operational def>, reported <weekly/monthly>\nSupporting (NOT NSM): <metric> — <why supporting>\nRe-evaluation date: <next quarter>"},{"language":"text","snippet":"NSM Audit: <product>\nCustomer value: \"<customer experiences ___, measured in ___>\"\nCandidates: (1) <metric> (2) <metric> (3) <metric>\n3-Criteria check: | Candidate | Value? | Strategy? | Leads rev? | Verdict |\nTime-shifted correlation lead: <weeks/months>\nPerverse-incentive risk: <what could go wrong> | Mitigation: <guardrail>\nChosen NSM: <NSM> — measured as <operational def>, reported <weekly/monthly>\nSupporting (NOT NSM): <metric> — <why supporting>\nRe-evaluation date: <next quarter>"},{"language":"text","snippet":"NSM Audit: <product>\nCustomer value: \"<customer experiences ___, measured in ___>\"\nCandidates: (1) <metric> (2) <metric> (3) <metric>\n3-Criteria check: | Candidate | Value? | Strategy? | Leads rev? | Verdict |\nTime-shifted correlation lead: <weeks/months>\nPerverse-incentive risk: <what could go wrong> | Mitigation: <guardrail>\nChosen NSM: <NSM> — measured as <operational def>, reported <weekly/monthly>\nSupporting (NOT NSM): <metric> — <why supporting>\nRe-evaluation date: <next quarter>"},{"language":"text","snippet":"NSM Audit: <product>\nCustomer value: \"<customer experiences ___, measured in ___>\"\nCandidates: (1) <metric> (2) <metric> (3) <metric>\n3-Criteria check: | Candidate | Value? | Strategy? | Leads rev? | Verdict |\nTime-shifted correlation lead: <weeks/months>\nPerverse-incentive risk: <what could go wrong> | Mitigation: <guardrail>\nChosen NSM: <NSM> — measured as <operational def>, reported <weekly/monthly>\nSupporting (NOT NSM): <metric> — <why supporting>\nRe-evaluation date: <next quarter>"},{"language":"text","snippet":"NSM Audit: <product>\nCustomer value: \"<customer experiences ___, measured in ___>\"\nCandidates: (1) <metric> (2) <metric> (3) <metric>\n3-Criteria check: | Candidate | Value? | Strategy? | Leads rev? | Verdict |\nTime-shifted correlation lead: <weeks/months>\nPerverse-incentive risk: <what could go wrong> | Mitigation: <guardrail>\nChosen NSM: <NSM> — measured as <operational def>, reported <weekly/monthly>\nSupporting (NOT NSM): <metric> — <why supporting>\nRe-evaluation date: <next quarter>"},{"language":"text","snippet":"NSM Audit: <product>\nCustomer value: \"<customer experiences ___, measured in ___>\"\nCandidates: (1) <metric> (2) <metric> (3) <metric>\n3-Criteria check: | Candidate | Value? | Strategy? | Leads rev? | Verdict |\nTime-shifted correlation lead: <weeks/months>\nPerverse-incentive risk: <what could go wrong> | Mitigation: <guardrail>\nChosen NSM: <NSM> — measured as <operational def>, reported <weekly/monthly>\nSupporting (NOT NSM): <metric> — <why supporting>\nRe-evaluation date: <next quarter>"}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"SKILL.md","content":"---\nname: north-star-metric\ndescription: \"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?'\n  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\"\n---\n\n# North Star Metric\n\n## Overview\n\nThe **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.\n\n**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.\n\n## When to Use\n\n**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.\n\n**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).\n\n## Coaching Novices (Adaptive Front Door)\n\n- **Engine mode:** user has a concrete case → run The Process directly.\n- **Coach mode:** user is unfamiliar or has no concrete case → guide step by step.\n\nIn Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.\n\n1. 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.\n2. Check fit against When to Use / When NOT to use. No customers → redirect to lean-startup. Single mature team → wrong scope.\n3. Elicit the product's core value in customer units (time saved, problem solved) — not the product feature.\n> **[WAIT — do not advance until user responds]**\n4. Walk: value → metric candidate → 3-criteria check → test against company strategy → pick. Pause at each.\n> **[WAIT — do not advance until user responds]**\n5. 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.\n> **[WAIT — do not advance until user responds]**"},{"path":"_meta.json","content":"{\n  \"ownerId\": \"kn754b8sk22s8c6gjxt02bftbn88q7ye\",\n  \"slug\": \"north-star-metric\",\n  \"version\": \"1.0.5\",\n  \"publishedAt\": 1784225339762\n}"},{"path":"references/sources.md","content":"# Sources — north-star-metric\n\n> *Primary sources for the [north-star-metric](../SKILL.md) skill.*\n\n- **Ellis, Sean.** \"The North Star Metric for Sustainable Growth.\" *GrowthHackers*, 2017. https://growthhackers.com/articles/north-star-metric **Primary source for the term**.\n- **Amplitude.** *The North Star Playbook* (2nd ed., Amplitude Inc., 2022). https://amplitude.com/north-star **Operational framework with the 3-criteria test**.\n- **Croll, Alistair & Yoskovitz, Benjamin.** *Lean Analytics: Use Data to Build a Better Startup Faster*. O'Reilly, 2013. **The \"One Metric That Matters\" formulation**.\n- **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**.\n- **Chen, Andrew.** Growth writings: https://andrewchen.com/ — useful secondary source for NSM patterns across companies.\n- **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.\n- **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.\n- 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."},{"path":"examples/ai-product-activated-value-vs-vanity-metrics-2023-2026.md","content":"# Method in Action: Choosing a North Star for an AI Product — Activated Value vs. Vanity Metrics (2023–2026)\n\n> *Example for the [north-star-metric](../SKILL.md) skill.*\n\nA 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.\n\n**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.\n\nRun the **NSM Audit** on a representative AI agent/copilot product:\n\n1. **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.\n\n2. **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.\n\n3. **Apply the 3 criteria (Step 3):**\n   - **Sign-ups:** customer value? weak — a signup is intent, not value delivered. Moves with marketing spend. **Reject as NSM** (classic vanity metric).\n   - **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.**\n   - **Demo plays / feature engagement:** customer value? weak — novelty-driven, decays after launch. **Reject as NSM.**\n   - **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 "},{"path":"examples/facebook-seven-friends-in-ten-days-2007-2010.md","content":"# Method in Action: Facebook's \"Seven Friends in Ten Days\" (2007–2010)\n\n> *Example for the [north-star-metric](../SKILL.md) skill.*\n\nA worked example. Not Silicon Valley legend — discussed publicly by Chamath Palihapitiya, head of Facebook's growth team 2007–2011, in lectures and interviews.\n\nWhen 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.\n\nThe 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.\n\nPalihapitiya, speaking publicly years later:\n\n> \"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.\"\n> — 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\n\nWalk the NSM Audit on Facebook 2007:\n\n- **Customer value (Step 1):** Users connected to their existing social network and using it as their primary social communication tool.\n- **Candidate metrics (Step 2):** Signups, DAU/MAU ratio, time spent, photos posted, messages sent, friends-added per user, friends-added-in-first-N-days.\n- **3-criteria check (Step 3):**\n  - Signups: customer value? weak (signup ≠ value). **Reject as NSM.**\n  - Time spent: customer value? mixed (could be addiction-y, not genuine value). **Supporting, not NSM.**\n  - 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 ✓.**\n- **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.\n- **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 "}],"languages":[],"docsSourceLabel":"CLAWHUB","editorialOverview":"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... 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