Token导航 LogoToken导航TokenDH.com
研究检索只读github未标认证来源可访问许可证需确认审计通过

measuring-product-market-fit衡量产品市场契合度

Agent Skill

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。

总安装

25,872

周安装

1,082

GitHub Stars

715

下载量

9,064
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

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

简介

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

SKILL.md

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

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

36.59%
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

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

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。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

继续浏览同类 Skills