Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add killvxk/pm-skills-zh --skill north-star-metricgit clone --depth 1 https://github.com/killvxk/pm-skills-zhWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/killvxk/pm-skills-zh/north-star-metric)<a href="https://agentmods.dev/skills/killvxk/pm-skills-zh/north-star-metric"><img src="https://agentmods.dev/badge/skills/killvxk/pm-skills-zh/north-star-metric/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/killvxk/pm-skills-zh/north-star-metric"><img src="https://agentmods.dev/badge/skills/killvxk/pm-skills-zh/north-star-metric.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00095 | $0.01046 |
| Opus 5 | $0.00048 | $0.00523 |
| Sonnet 5 | $0.00019 | $0.00209 |
| Haiku 4.5 | $0.00010 | $0.00105 |
Grade A, and why
north-star-metric scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
北极星指标
确定北极星指标(North Star Metric,NSM)及 3-5 个构成指标星座的输入指标。识别产品所处的商业模式类型,并对照有效北极星指标的标准进行验证。适用于定义核心指标、搭建指标框架或决定衡量方向的场景。
领域背景
NSM 不是:多个指标的集合、收入或 LTV 类指标(必须以客户为中心)、OKR(那是目标设定方法),也不是战略本身(但选择正确的 NSM 本身是一个战略决策)。
NSM 是:单一的、以客户为中心的 KPI,能够反映客户从产品中获得的价值,并作为长期业务成功的领先指标。你可以用关键结果(OKR 中的 KR)来表达 NSM 的预期变化量。
免费资源:The North Star Framework 101 (PDF)
适用场景
- 定义公司核心指标框架
- 搭建指标追踪体系
- 决定衡量什么、优化什么
- 评估北极星候选指标
- 触发关键词:北极星指标、north star、核心指标、衡量什么、指标框架、OMTM(One Metric That Matters)
三种商业模式类型
在确定北极星指标之前,先将业务归入以下三种模式之一:
- 注意力型(Attention Game):客户在你的产品上花了多少时间?(典型案例:Facebook、Spotify、YouTube、TikTok)
- 交易型(Transaction Game):客户与平台之间发生了多少次交易?(典型案例:Amazon、Uber、Airbnb、PayPal)
- 生产力型(Productivity Game):用户能多高效地完成工作或达成目标?(典型案例:Canva、Dropbox、Loom、Notion)
提示词
你是一位专注于北极星指标和增长度量框架的指标策略专家。
根据以下业务背景:$ARGUMENTS
第一步:识别商业模式类型 判断该公司属于哪种模式:注意力型、交易型还是生产力型。
第二步:确定北极星指标 提出一个满足以下全部七条有效北极星指标标准的单一指标:
- 易于理解:定义清晰,组织内每个人都能看懂
- 以客户为中心:反映向客户交付的价值,而非单纯追踪收入或活跃行为
- 可持续价值:体现习惯养成和客户长期参与
- 愿景契合:代表公司愿景与使命方向上的实质性进展
- 可量化:可度量,有明确的数字追踪方式
- 可干预:团队可以通过产品、营销和运营手段直接影响它
- 领先指标:能够预测未来业务成功和收入增长
第三步:确定输入指标 定义 3-5 个最直接影响和驱动北极星指标的输入指标(也称领先指标)。每个输入指标应满足:
- 在短期内更容易撬动
- 直接促成北极星指标的达成
- 帮助明确优化投入应聚焦的方向
获得最佳结果的建议
- 提供业务模式和收入模型的详细信息
- 分享公司愿景、使命或长期目标
- 附上目前在追踪的指标
- 说明核心客户细分群体和使用场景
- 描述为客户创造的主要价值
延伸阅读
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 75 lines · 95 tokens per session scan A f4a8c7665a27
north-star-metric is a skill published in the GitHub repository killvxk/pm-skills-zh (159 stars, last pushed 5mo ago), licensed MIT. It adds 95 tokens to every session and 1,046 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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