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pol-probe

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Purpose

目的

Define and document a Proof of Life (PoL) probe—a lightweight, disposable validation artifact designed to surface harsh truths before expensive development. Use this when you need to eliminate a specific risk or test a narrow hypothesis without building production-quality software. PoL probes are reconnaissance missions, not MVPs—they're meant to be deleted, not scaled.
This framework prevents prototype theater (expensive demos that impress stakeholders but teach nothing) and forces you to match validation method to actual learning goal.
定义并记录生命验证（PoL）探针——一种轻量、可废弃的验证工具，旨在投入高昂开发成本前揭示残酷真相。当你需要消除特定风险或测试某个具体假设，无需构建生产级软件时，可使用该工具。PoL探针是侦察任务，而非MVP（最小可行产品）——它们的归宿是被删除，而非被规模化。
这个框架可避免“原型表演”（即那些能打动利益相关者但毫无实际学习价值的昂贵演示），并促使你根据实际学习目标匹配验证方法。

Key Concepts

核心概念

What is a PoL Probe?

什么是PoL探针？

A Proof of Life (PoL) probe is a deliberate, disposable validation experiment designed to answer one specific question as cheaply and quickly as possible. It's not a product, not an MVP, not a pilot—it's a targeted truth-seeking mission.
Origin: Coined by Dean Peters (Productside), building on Marty Cagan's 2014 work on prototype flavors and Jeff Patton's principle: "The most expensive way to test your idea is to build production-quality software."
生命验证（PoL）探针是一种刻意设计、可废弃的验证实验，旨在以尽可能低成本、快速的方式回答一个具体问题。它不是产品，不是MVP，不是试点项目——它是一项针对性的求真任务。
起源： 由Dean Peters（Productside）提出，基于Marty Cagan在2014年提出的原型类型框架，以及Jeff Patton的原则：“测试想法最昂贵的方式就是构建生产级软件。”

The 5 Essential Characteristics

5项核心特征

Every PoL probe must satisfy these criteria:
CharacteristicWhat It MeansWhy It Matters
LightweightMinimal resource investment (hours/days, not weeks)If it's expensive, you'll avoid killing it when the data says to
DisposableExplicitly planned for deletion, not scalingPrevents sunk-cost fallacy and scope creep
Narrow ScopeTests one specific hypothesis or riskBroad experiments yield ambiguous results
Brutally HonestSurfaces harsh truths, not vanity metricsPolite data is useless data
Tiny & FocusedReconnaissance missions, never MVPsSmall surface area = faster learning cycles
Anti-Pattern: If your "prototype" feels too polished to delete, it's not a PoL probe—it's prototype theater.
每个PoL探针都必须满足以下标准：
特征含义重要性
轻量性资源投入极少（数小时/数天，而非数周）如果成本高昂，当数据显示应该终止时，你会舍不得放弃
可废弃性明确规划为用完即删，而非规模化避免沉没成本谬误和范围蔓延
窄范围仅测试一个具体假设或风险宽泛的实验会产生模糊的结果
残酷诚实揭示残酷真相，而非虚荣指标无关痛痒的数据毫无价值
微小聚焦侦察任务，绝非MVP更小的范围 = 更快的学习周期
反模式： 如果你的“原型”精致到让你舍不得删除，那它就不是PoL探针——而是原型表演。

PoL Probe vs. MVP

PoL探针 vs. MVP

DimensionPoL ProbeMVP
PurposeDe-risk decisions through narrow hypothesis testingJustify ideas or defend roadmap direction
ScopeSingle question, single riskSmallest shippable product increment
LifespanHours to days, then deletedWeeks to months, then iterated
AudienceInternal team + narrow user sampleReal customers in production
FidelityJust enough illusion to catch signalsProduction-quality (or close)
OutcomeLearn what doesn't workLearn what does work (and ship it)
Key Distinction: PoL probes are pre-MVP reconnaissance. You run probes to decide if you should build an MVP, not to launch something.
维度PoL探针MVP
目的通过窄范围假设测试降低决策风险验证想法合理性或支撑路线图方向
范围单一问题、单一风险最小可交付产品增量
生命周期数小时至数天，随后被删除数周至数月，随后迭代优化
受众内部团队 + 小范围用户样本生产环境中的真实客户
保真度仅需足够的“假象”来捕捉信号生产级（或接近生产级）质量
结果了解哪些方法无效了解哪些方法有效（并交付使用）
核心区别： PoL探针是MVP前置侦察工具。你运行探针是为了决定是否应该构建MVP，而非为了发布产品。

The 5 Prototype Flavors

5种原型类型

Match the probe type to your hypothesis, not your tooling comfort.
TypeCore QuestionTimelineTools/MethodsWhen to Use
1. Feasibility Checks"Can we build this?"1-2 daysGenAI prompt chains, API tests, data integrity sweeps, spike-and-delete codeTechnical risk is unknown; third-party dependencies unclear
2. Task-Focused Tests"Can users complete this job without friction?"2-5 daysOptimal Workshop, UsabilityHub, task flowsCritical moments (field labels, decision points, drop-off zones) need validation
3. Narrative Prototypes"Does this workflow earn stakeholder buy-in?"1-3 daysLoom walkthroughs, Sora/Synthesia videos, slideware storyboardsYou need to "tell vs. test"—share the story, measure interest
4. Synthetic Data Simulations"Can we model this without production risk?"2-4 daysSynthea (user simulation), DataStax LangFlow (prompt logic testing)Edge case exploration; unknown-unknown surfacing
5. Vibe-Coded PoL Probes"Will this solution survive real user contact?"2-3 daysChatGPT Canvas + Replit + Airtable = "Frankensoft"You need user feedback on workflow/UX, but not production-grade code
Golden Rule: "Use the cheapest prototype that tells the harshest truth. If it doesn't sting, it's probably just theater."
根据你的假设选择探针类型，而非根据你熟悉的工具。
类型核心问题时间周期工具/方法使用场景
1. 可行性检查“我们能构建这个吗？”1-2天GenAI提示链、API测试、数据完整性扫描、 spike-and-delete代码技术风险未知；第三方依赖不明确
2. 任务聚焦测试“用户能否无摩擦地完成这项任务？”2-5天Optimal Workshop、UsabilityHub、任务流关键环节（字段标签、决策点、流失区域）需要验证
3. 叙事原型“这个工作流能获得利益相关者的认可吗？”1-3天Loom演示、Sora/Synthesia视频、幻灯片故事板你需要“讲述而非测试”——分享故事，衡量兴趣
4. 合成数据模拟“我们能否在无生产风险的情况下建模？”2-4天Synthea（用户模拟）、DataStax LangFlow（提示逻辑测试）探索边缘案例；揭示未知的未知
5. 氛围编码PoL探针“这个解决方案能在真实用户接触中存活吗？”2-3天ChatGPT Canvas + Replit + Airtable = "Frankensoft"你需要获取用户对工作流/UX的反馈，但无需生产级代码
黄金法则： “使用能揭示最残酷真相的最便宜原型。如果结果不会让你刺痛，那它可能只是一场表演。”

When to Use a PoL Probe

何时使用PoL探针

✅ Use a PoL probe when:
  • You have a specific, falsifiable hypothesis to test
  • A particular risk blocks your next decision (technical feasibility, user task completion, stakeholder support)
  • You need harsh truth fast (within days, not weeks)
  • Building production software would be premature or wasteful
  • You can articulate what "failure" looks like before you start
❌ Don't use a PoL probe when:
  • You're trying to impress executives (that's prototype theater)
  • You already know the answer and just want validation (that's confirmation bias)
  • You can't articulate a clear hypothesis or disposal plan
  • The learning goal is too broad ("Will customers like this?")
  • You're using it to avoid making a hard decision
✅ 适合使用PoL探针的场景：
  • 你有一个具体、可证伪的假设需要测试
  • 某个特定风险阻碍了你的下一个决策（技术可行性、用户任务完成度、利益相关者支持）
  • 你需要快速获得残酷真相（数天内，而非数周）
  • 构建生产级软件为时尚早或纯属浪费
  • 你能在开始前明确“失败”的定义
❌ 不适合使用PoL探针的场景：
  • 你试图打动高管（那是原型表演）
  • 你已经知道答案，只是想要确认（那是确认偏误）
  • 你无法明确假设或废弃计划
  • 学习目标过于宽泛（比如“客户会喜欢这个吗？”）
  • 你用它来逃避艰难的决策

Application

应用

Use 
template.md
for the full fill-in structure.
使用
template.md
获取完整的填空式模板。

PoL Probe Template

PoL探针模板

Use this structure to document your probe:
markdown
undefined
使用以下结构记录你的探针：
markdown
undefined

PoL Probe: [Descriptive Name]

PoL Probe: [描述性名称]

Hypothesis

Hypothesis

[One-sentence statement of what you believe to be true] Example: "If we reduce the onboarding form to 3 fields, completion rate will exceed 80%."
[一句话陈述你认为正确的假设] Example: "If we reduce the onboarding form to 3 fields, completion rate will exceed 80%."

Risk Being Eliminated

Risk Being Eliminated

[What specific risk or unknown are you addressing?] Example: "We don't know if users will abandon signup due to form length."
[你要解决的具体风险或未知问题是什么？] Example: "We don't know if users will abandon signup due to form length."

Prototype Type

Prototype Type

[Select one of the 5 flavors]
  • Feasibility Check
  • Task-Focused Test
  • Narrative Prototype
  • Synthetic Data Simulation
  • Vibe-Coded PoL Probe
[选择5种类型之一]
  • Feasibility Check
  • Task-Focused Test
  • Narrative Prototype
  • Synthetic Data Simulation
  • Vibe-Coded PoL Probe

Target Users / Audience

Target Users / Audience

[Who will interact with this probe?] Example: "10 users from our early access waitlist, non-technical SMB owners."
[谁会参与这个探针的测试？] Example: "10 users from our early access waitlist, non-technical SMB owners."

Success Criteria (Harsh Truth)

Success Criteria (Harsh Truth)

[What truth are you seeking? What would prove you wrong?]
  • Pass: 8+ users complete signup in under 2 minutes
  • Fail: <6 users complete, or average time exceeds 5 minutes
  • Learn: Identify specific drop-off fields
[你要寻求什么真相？什么结果会证明你错了？]
  • Pass: 8+ users complete signup in under 2 minutes
  • Fail: <6 users complete, or average time exceeds 5 minutes
  • Learn: Identify specific drop-off fields

Tools / Stack

Tools / Stack

[What will you use to build this?] Example: "ChatGPT Canvas for form UI, Airtable for data capture, Loom for post-session interviews."
[你将用什么工具构建这个探针？] Example: "ChatGPT Canvas for form UI, Airtable for data capture, Loom for post-session interviews."

Timeline

Timeline

  • Build: 2 days
  • Test: 1 day (10 user sessions)
  • Analyze: 1 day
  • Disposal: Day 5 (delete all code, keep learnings doc)
  • Build: 2 days
  • Test: 1 day (10 user sessions)
  • Analyze: 1 day
  • Disposal: Day 5 (delete all code, keep learnings doc)

Disposal Plan

Disposal Plan

[When and how will you delete this?] Example: "After user sessions complete, archive recordings, delete Frankensoft code, document learnings in Notion."
[你将在何时、如何删除这个探针？] Example: "After user sessions complete, archive recordings, delete Frankensoft code, document learnings in Notion."

Owner

Owner

[Who is accountable for running and disposing of this probe?]
[谁负责运行并废弃这个探针？]

Status

Status

  • Hypothesis defined
  • Probe built
  • Users recruited
  • Testing complete
  • Learnings documented
  • Probe disposed

---
  • Hypothesis defined
  • Probe built
  • Users recruited
  • Testing complete
  • Learnings documented
  • Probe disposed

---

Quality Checklist

质量检查表

Before launching your PoL probe, verify:
  • Lightweight: Can you build this in 1-3 days?
  • Disposable: Have you committed to a disposal date?
  • Narrow Scope: Does it test ONE hypothesis?
  • Brutally Honest: Will the data hurt if you're wrong?
  • Tiny & Focused: Is this smaller than an MVP?
  • Falsifiable: Can you describe what "failure" looks like?
  • Clear Owner: Is one person accountable for executing and disposing of this?
If any answer is "no," revise your probe or reconsider whether you need one.
在启动PoL探针前，请验证：
  • 轻量性： 你能在1-3天内构建完成吗？
  • 可废弃性： 你是否已承诺废弃日期？
  • 窄范围： 它是否仅测试一个假设？
  • 残酷诚实： 如果你错了，数据会让你难受吗？
  • 微小聚焦： 它是否比MVP更小？
  • 可证伪： 你能否描述“失败”的定义？
  • 明确负责人： 是否有专人负责执行并废弃这个探针？
如果任何问题的答案是“否”，请修改你的探针，或重新考虑是否需要它。

Examples

示例

See 
examples/sample.md
for full PoL probe examples.
Mini example excerpt:
markdown
**Hypothesis:** Users can distinguish "archive" vs "delete"
**Probe Type:** Task-Focused Test
**Pass:** 80%+ correct interpretation
查看
examples/sample.md
获取完整的PoL探针示例。
迷你示例节选：
markdown
**Hypothesis:** Users can distinguish "archive" vs "delete"
**Probe Type:** Task-Focused Test
**Pass:** 80%+ correct interpretation

Common Pitfalls

常见陷阱

  • Running a broad "will users like this?" experiment instead of testing one falsifiable hypothesis
  • Treating a PoL probe as a proto-MVP and refusing to dispose of it
  • Using vanity metrics that avoid uncomfortable truth
  • Skipping a pre-defined failure threshold before testing begins
  • Choosing tools first and hypothesis second
  • 运行宽泛的“用户会喜欢这个吗？”实验，而非测试一个可证伪的假设
  • 将PoL探针视为准MVP，拒绝废弃它
  • 使用虚荣指标，逃避令人不适的真相
  • 在测试前未预先定义失败阈值
  • 先选择工具，再确定假设

References

参考资料

Related Skills

相关技能

  • pol-probe-advisor (Interactive) — Decision framework for choosing which prototype type to use
  • discovery-process (Workflow) — Use PoL probes in validation phase
  • problem-statement (Component) — Define problem before creating PoL probe
  • epic-hypothesis (Component) — Frame hypothesis before testing with PoL probe
  • pol-probe-advisor (交互式) — 用于选择原型类型的决策框架
  • discovery-process (工作流) — 在验证阶段使用PoL探针
  • problem-statement (组件) — 在创建PoL探针前定义问题
  • epic-hypothesis (组件) — 在使用PoL探针测试前构建假设

External Frameworks

外部框架

  • Jeff Patton — User Story Mapping (lean validation principles)
  • Marty Cagan — Inspired (2014 prototype flavors framework)
  • Dean Peters — Vibe First, Validate Fast, Verify Fit (Dean Peters' Substack, 2025)
  • Jeff Patton — User Story Mapping（精益验证原则）
  • Marty Cagan — Inspired（2014年原型类型框架）
  • Dean Peters — Vibe First, Validate Fast, Verify Fit（Dean Peters的Substack专栏，2025）

Tools Mentioned

提及的工具

  • Feasibility: GenAI (ChatGPT, Claude), API testing tools
  • Task-Focused: Optimal Workshop, UsabilityHub
  • Narrative: Loom, Sora, Synthesia, Veo3 (text-to-video)
  • Synthetic Data: Synthea (patient simulation), DataStax LangFlow
  • Vibe-Coded: ChatGPT Canvas, Replit, Airtable, Carrd
  • 可行性检查： GenAI（ChatGPT、Claude）、API测试工具
  • 任务聚焦测试： Optimal Workshop、UsabilityHub
  • 叙事原型： Loom、Sora、Synthesia、Veo3（文本转视频）
  • 合成数据模拟： Synthea（患者模拟）、DataStax LangFlow
  • 氛围编码： ChatGPT Canvas、Replit、Airtable、Carrd