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Agent Skill · 研究检索

measuring-product-market-fit(衡量产品市场契合度)

measuring-product-market-fit 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

measuring-product-market-fit 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 5,447。

总安装 25,872周安装 1,082GitHub Stars 715下载 9,064核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:measuring-product-market-fit(衡量产品市场契合度)
来源仓库:https://github.com/refoundai/lenny-skills
仓库路径:skills/measuring-product-market-fit
安装命令:
npx skills add https://github.com/refoundai/lenny-skills --skill measuring-product-market-fit
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/refoundai/lenny-skills --skill measuring-product-market-fit

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

使用 46 个产品领导者的信号对产品市场契合度进行基于框架的评估。

将 Sean Ellis 的“非常失望”调查作为领先的 PMF 指标,目标是在获得长期保留数据之前达到 40% 的阈值 通过保留曲线、参考客户数量和客户拉动信号诊断阶段;区分虚荣指标和真正的 PMF 证据 通过四个级别(初级到极端)识别 PMF,并针对特定细分市场进行匹配;了解 PMF 需要产品保留和可扩展的分发 标记常见错误,包括将发布峰值误认为 PMF、过早扩展以及将市场规模与实际产品市场契合度混为一谈

来源仓库内路径:skills/measuring-product-market-fit

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/refoundai/lenny-skills --skill measuring-produ…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

measuring-product-market-fit 怎么安装?

在终端执行 npx skills add https://github.com/refoundai/lenny-skills --skill measuring-product-market-…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

measuring-product-market-fit 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

measuring-product-market-fit 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

measuring-product-market-fit 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

measuring-product-market-fit 用的人多吗?

公开数据是累计安装 25,872 次,近一周安装 1,082 次,下载 9,064 次,GitHub 715 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

measuring-product-market-fit 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
measuring-product-market-fit

衡量产品市场契合度 · 本页

25,872715只读未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Measuring Product-Market Fit

Help the user assess and achieve product-market fit using frameworks from 46 product leaders.

How to Help

When the user asks about product-market fit:

  1. Understand their stage - Ask how many customers they have, what their retention looks like, and what signals they're seeing (or not seeing)
  2. Diagnose the situation - Determine if they're confusing vanity metrics with PMF, if they have PMF in a specific segment, or if they're clearly pre-PMF
  3. Apply the right framework - Help them use the Sean Ellis survey, retention curves, or reference customer counts depending on their situation
  4. Guide next steps - Help them decide whether to scale or continue iterating based on the evidence

Core Principles

Use the Sean Ellis "disappointment" survey

Sean Ellis: "How would you feel if you could no longer use this product? Very disappointed, somewhat disappointed, or not disappointed. If 40% say 'very disappointed,' you're on the right track." This is a leading indicator of PMF before long-term retention data is available. Focus on the "very disappointed" segment as your core value indicator.

Retention is the ultimate metric

Uri Levine: "Product market fit has one metric. Retention. If you create value, they will come back. If they're not coming back, you're not creating value." Look for retention curves that flatten over time rather than decaying to zero. The "smile curve" - where engagement increases over time - is the strongest signal.

PMF is obvious when you have it

Matt MacInnis: "Product market fit is something where you absolutely know it when you see it. Therefore if you don't absolutely know it, you don't have it." If there's doubt, you likely don't have it. Look for the market pulling the product out of your hands.

PMF is not static - it can be lost

Casey Winters: "Protecting what you've built is increasingly important once you build scale. You might fall out of product market fit in a year or five years if you're not continually making your product better." Markets shift, competitors improve, and user expectations rise.

Reference customers validate PMF

Christian Idiodi: "The holy grail is really a reference customer - somebody who loves it enough to tell people about it. I want 6-8 references for B2B, 15-25 for B2C as an indication of PMF." Don't launch publicly until you have secured the target number of references from early users.

PMF exists in segments, not universally

Karri Saarinen: "The way we think about it is, 'Do we have the fit in specific segments?' and how strong that fit is." Find PMF in one segment first (e.g., early-stage startups) before expanding. Double down where you see natural pull.

PMF requires distribution, not just retention

Casey Winters: "If you have a product that retains well and you can't find more users for it, I don't think that's product market fit." True PMF requires both a retaining product AND a scalable, built-in distribution mechanism.

PMF is multi-stage, not binary

Todd Jackson: "There's essentially four levels: nascent, developing, strong, extreme." Level 1 (3-5 customers), Level 2 (5-25 customers), Level 3 (25-100 customers), Level 4 (100+ customers). Sequence focus: satisfaction at Level 1, demand at Level 2, efficiency at Level 3.

Look for customer "pull"

Raaz Herzberg: "We felt the questions change - 'How are you pricing this? When can we start a POV?' That's real intent." True pull is characterized by customers driving next steps, not just saying "this is interesting."

A lack of outrage during outages = no PMF

Jeff Weinstein: "During those 20 minutes our customers weren't furious. That was the signal we did not have product market fit." If your product goes down and nobody notices or complains, you haven't solved a mission-critical problem.

Questions to Help Users

  • "If users couldn't use your product anymore, what percentage would be 'very disappointed'?"
  • "What does your retention curve look like at day 7, 30, and 90?"
  • "Do you have customers willing to be references and tell others about you?"
  • "Is the market pulling the product from you, or are you pushing it on them?"
  • "Are customers driving next steps (asking about pricing, timelines) or just being politely interested?"
  • "What specific segment do you have the strongest fit in?"

Common Mistakes to Flag

  • Confusing launch spikes with PMF - Product Hunt success or press coverage doesn't mean you have PMF. Look for sustained organic growth
  • Ignoring retention data - If users aren't coming back, you don't have PMF regardless of how many you acquire
  • Scaling too early - Paid growth before PMF just burns cash and can damage your brand
  • Conflating TAM with PMF - A large market opportunity doesn't mean you've achieved fit within it
  • Listening to "somewhat disappointed" users - Focus on what makes "very disappointed" users love you, not what would make lukewarm users slightly happier

Deep Dive

For all 64 insights from 46 guests, see references/guest-insights.md

Related Skills

  • Designing Growth Loops
  • Retention & Engagement
  • Conducting User Interviews
  • Startup Pivoting

平台分布

Codex

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按下载量换算3,317

Claude

27.36%
按下载量换算2,480

Cursor

20.04%
按下载量换算1,816

Gemini CLI

10.27%
按下载量换算931

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权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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