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pol-probe

deanpeters/product-manager-skills

Define lightweight, disposable validation experiments to test risky hypotheses before expensive development.

What is pol-probe?

A Proof of Life (PoL) probe is a deliberate, disposable validation artifact designed to answer one specific question as cheaply and quickly as possible—not a product or MVP, but a targeted truth-seeking mission. Use this when you need to eliminate a specific risk or test a narrow hypothesis without building production-quality software, surfacing harsh truths before engineering investment.

  • Document a lightweight, disposable validation experiment matched to your specific hypothesis
  • Match prototype type (feasibility check, task test, narrative, simulation, or vibe-coded) to your learning goal
  • Define success criteria that surface harsh truths, not vanity metrics
  • Plan explicit disposal to prevent sunk-cost fallacy and scope creep
  • Reduce risk before spending engineering time on unvalidated ideas

How to install pol-probe

npx skills add https://github.com/deanpeters/product-manager-skills --skill pol-probe
Claude Code
Cursor
Windsurf
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How to use pol-probe

  1. 1.Define your specific, falsifiable hypothesis in one sentence
  2. 2.Identify the risk or unknown blocking your next decision
  3. 3.Select the appropriate prototype type from the 5 flavors (feasibility, task-focused, narrative, simulation, vibe-coded)
  4. 4.Set harsh success criteria that would prove you wrong
  5. 5.Build the probe in 1-3 days using the cheapest tools that surface truth
  6. 6.Recruit target users and run sessions (1-5 days)
  7. 7.Document learnings and explicitly delete the probe artifact
  8. 8.Archive findings in your decision log, not your codebase

Use cases

Good for
  • Test a pricing hypothesis with a narrative prototype before building billing infrastructure
  • Validate a critical workflow with 10 users using a task-focused test in 3 days
  • Check technical feasibility of a third-party integration with a spike-and-delete code probe
  • Explore edge cases with synthetic data simulation without production risk
  • Determine stakeholder buy-in on a new feature workflow with a Loom walkthrough
Who it's for
  • Product managers deciding whether to fund feature development
  • Engineering leads assessing technical risk before committing resources
  • Teams needing rapid validation before MVP investment
  • Stakeholders seeking honest feedback instead of prototype theater

pol-probe FAQ

What's the difference between a PoL probe and an MVP?

A PoL probe is pre-MVP reconnaissance designed to answer one specific question and be deleted. An MVP is the smallest shippable product increment meant to be iterated. Run probes to decide if you should build an MVP, not to launch something.

How long should a PoL probe take?

1-3 days to build, plus 1-5 days to test with users. If it's taking longer, you've lost the 'lightweight' characteristic and may be building prototype theater instead.

What does 'disposable' mean?

Explicitly plan to delete the probe after learning. This prevents sunk-cost fallacy and scope creep. If it feels too polished to delete, it's not a PoL probe.

Which prototype type should I use?

Match the type to your hypothesis: feasibility checks for technical risk, task-focused tests for workflow friction, narrative prototypes for stakeholder buy-in, synthetic simulations for edge cases, and vibe-coded probes for UX validation.

What makes a PoL probe different from prototype theater?

PoL probes surface harsh truths and are explicitly planned for deletion. Prototype theater impresses stakeholders but teaches nothing. If your data doesn't sting, it's probably just theater.

Full instructions (SKILL.md)

Source of truth, from deanpeters/product-manager-skills.


name: pol-probe description: Define a Proof of Life probe to test a risky hypothesis cheaply. Use when you need harsh truth before building real product. intent: >- 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. type: component best_for:

  • "Documenting a lightweight validation artifact before build"
  • "Testing a narrow hypothesis without shipping production software"
  • "Reducing risk before spending engineering time" scenarios:
  • "Define a Proof of Life probe for a new workflow automation idea"
  • "Help me write a PoL probe for this pricing hypothesis"
  • "Create a low-cost validation probe before we build this feature"

Purpose

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.

Key Concepts

What is a PoL Probe?

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."


The 5 Essential Characteristics

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.


PoL Probe 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.


The 5 Prototype Flavors

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."


When to Use a PoL Probe

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

Application

Use template.md for the full fill-in structure.

PoL Probe Template

Use this structure to document your probe:

# PoL Probe: [Descriptive Name]

## 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%."

## 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."

## Prototype Type
[Select one of the 5 flavors]
- [ ] Feasibility Check
- [ ] Task-Focused Test
- [ ] Narrative Prototype
- [ ] Synthetic Data Simulation
- [x] Vibe-Coded PoL Probe

## Target Users / Audience
[Who will interact with this probe?]
Example: "10 users from our early access waitlist, non-technical SMB owners."

## 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

## Tools / Stack
[What will you use to build this?]
Example: "ChatGPT Canvas for form UI, Airtable for data capture, Loom for post-session interviews."

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

## Disposal Plan
[When and how will you delete this?]
Example: "After user sessions complete, archive recordings, delete Frankensoft code, document learnings in Notion."

## Owner
[Who is accountable for running and disposing of this probe?]

## Status
- [ ] Hypothesis defined
- [ ] Probe built
- [ ] Users recruited
- [ ] Testing complete
- [ ] Learnings documented
- [ ] Probe disposed

Quality Checklist

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.


Examples

See examples/sample.md for full PoL probe examples.

Mini example excerpt:

**Hypothesis:** Users can distinguish "archive" vs "delete"
**Probe Type:** Task-Focused Test
**Pass:** 80%+ correct interpretation

Common Pitfalls

  • 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

References

Related Skills

External Frameworks

  • Jeff PattonUser Story Mapping (lean validation principles)
  • Marty CaganInspired (2014 prototype flavors framework)
  • Dean PetersVibe First, Validate Fast, Verify Fit (Dean Peters' Substack, 2025)

Tools Mentioned

  • 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