Select the right Proof of Life (PoL) probe based on hypothesis, risk, and resources. Use this to match the validation method to the real learning goal, not tooling comfort.
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Select the right Proof of Life (PoL) probe based on hypothesis, risk, and resources. Use this to match the validation method to the real learning goal, not tooling comfort.
name: pol-probe-advisor
argument-hint: "[hypothesis or risk]"
description: Select the right Proof of Life (PoL) probe based on hypothesis, risk, and resources. Use this to match the validation method to the real learning goal, not tooling comfort.
intent: >-
Guide product managers through selecting the right Proof of Life (PoL) probe type (of 5 flavors) based on their hypothesis, risk, and available resources. Use this when you need to eliminate a specific risk or test a narrow hypothesis, but aren't sure which validation method to use. This interactive skill ensures you match the cheapest prototype to the harshest truth—not the prototype you're most comfortable building.
type: interactive
best_for:
"Choosing the cheapest useful validation method for a risky idea"
"Matching a hypothesis to the right Proof of Life probe"
"Avoiding overbuilding before learning the harsh truth"
scenarios:
"Which Proof of Life probe should I use to test demand for this idea?"
"Help me pick the right validation method for an onboarding hypothesis"
"I have a risky AI concept. What PoL probe should I run first?"
theme: validation-experiments
estimated_time: "15-20 min"
Purpose
Guide product managers through selecting the right Proof of Life (PoL) probe type (of 5 flavors) based on their hypothesis, risk, and available resources. Use this when you need to eliminate a specific risk or test a narrow hypothesis, but aren't sure which validation method to use. This interactive skill ensures you match the cheapest prototype to the harshest truth—not the prototype you're most comfortable building.
This is not a tool for deciding you should validate (you should). It's a decision framework for choosing to validate most effectively.
if
how
Input
Works best with: The hypothesis you want to validate or the risk you want to eliminate.
Also useful: Your resources (time, budget, engineering access), audience access, and what failure would cost.
Anything supplied with the invocation itself — text after the skill name, a pasted context dump, or an appended ARGUMENTS: line — counts as answers already given. Use it and skip whatever it covers; don't re-ask.
Arriving empty-handed? That works too. The advisor opens by asking what you're trying to learn, then matches you to one of the 5 probe flavors.
Example invocation:Which probe fits? Hypothesis: mid-market HR teams will trust AI-drafted job descriptions enough to publish them; 2 weeks, no eng support.
Key Concepts
The Core Problem: Method-Hypothesis Mismatch
Common failure mode: PMs choose validation methods based on tooling comfort ("I know Figma, so I'll design a prototype") rather than learning goal. Result: validate the wrong thing, miss the actual risk.
Solution: Work backwards from the hypothesis. Ask: "What specific risk am I eliminating? What's the cheapest path to harsh truth?"
The 5 PoL Probe Flavors (Quick Reference)
Type
Core Question
Best For
Timeline
Feasibility Check
"Can we build this?"
Technical unknowns, API dependencies, data integrity
1-2 days
Task-Focused Test
"Can users complete this job without friction?"
Critical UI moments, field labels, decision points
User access (10 users from waitlist, 5 beta customers, etc.)
Budget (for UsabilityHub, Optimal Workshop, etc.)
Step 1: Identify the Core Question
Agent synthesizes user input and asks:
Based on your hypothesis and risk, which of these core questions are you really trying to answer?
Offer 5 options (aligned to probe types):
"Can we build this?" — You're uncertain about technical feasibility, API integration, data availability, or third-party dependencies
"Can users complete this job without friction?" — You're validating critical UI moments, field labels, navigation, or decision points
"Does this workflow earn stakeholder buy-in?" — You need to explain a complex flow, align leadership, or "tell vs. test" the story
"Can we model this without production risk?" — You need to explore edge cases, simulate user behavior, or test prompt logic safely
"Will this solution survive real user contact?" — You need users to interact with a semi-functional workflow to catch UX/workflow issues
User response: [Select one number, or describe if none fit]
Step 2: Recommend PoL Probe Type
Based on user selection, agent recommends the matching probe type:
Option 1 Selected: "Can we build this?"
→ Recommended Probe: Feasibility Check
What it is:
A 1-2 day spike-and-delete test to surface technical risk. Not meant to impress anyone—meant to reveal blockers fast.
Methods:
GenAI prompt chains (test if AI can handle your use case)
API sniff tests (verify third-party integrations work)
Data integrity sweeps (check if your data supports the feature)
Third-party tool evaluation (test if Zapier/Stripe/Twilio does what you think)
Timeline: 1-2 days
Tools:
ChatGPT/Claude (prompt testing)
Postman/Insomnia (API testing)
Jupyter notebooks (data exploration)
Proof-of-concept scripts (throwaway code)
Success Criteria Example:
Pass: API returns expected data format in <200ms
Fail: API times out, or data structure incompatible with our schema
Learn: Identify specific technical blocker
Disposal Plan: Delete all spike code after documenting findings.
Next Step: Would you like me to generate a pol-probe artifact documenting this feasibility check?
Option 2 Selected: "Can users complete this job without friction?"
→ Recommended Probe: Task-Focused Test
What it is:
Validate critical moments—field labels, decision points, navigation, drop-off zones—using specialized testing tools. Focus on observable task completion, not opinions.
Loom-recorded task walkthroughs (ask users to "think aloud")
Timeline: 2-5 days
Tools:
Optimal Workshop ($200/month)
UsabilityHub ($100-300/month)
Maze (free tier available)
Loom (free for basic)
Success Criteria Example:
Pass: 80%+ users complete task in <2 minutes
Fail: <60% completion, or 3+ users get stuck on same step
Learn: Identify exact friction point (specific field, button, etc.)
Disposal Plan: Archive session recordings, document learnings, delete test prototype.
Next Step: Would you like me to generate a pol-probe artifact documenting this task-focused test?
Option 3 Selected: "Does this workflow earn stakeholder buy-in?"
→ Recommended Probe: Narrative Prototype
What it is:
Tell the story, don't test the interface. Use video walkthroughs or slideware storyboards to explain workflows and measure interest. This is "tell vs. test"—you're validating the narrative, not the UI.
Methods:
Loom walkthroughs (screen recording with voiceover)
Next Step: Would you like me to generate a pol-probe artifact documenting this narrative prototype?
Option 4 Selected: "Can we model this without production risk?"
→ Recommended Probe: Synthetic Data Simulation
What it is:
Use simulated users, synthetic data, or prompt logic testing to explore edge cases and unknown-unknowns without touching production. Think "wind tunnel testing, cheaper than postmortem."
Methods:
Synthea (synthetic patient data generation)
DataStax LangFlow (test prompt logic without real users)
Monte Carlo simulations (model probabilistic outcomes)
Synthetic user behavior scripts (simulate click patterns, load testing)
Timeline: 2-4 days
Tools:
Synthea (open-source, healthcare)
DataStax LangFlow (prompt chain testing)
Python + Faker library (generate synthetic data)
Locust/k6 (load testing with synthetic users)
Success Criteria Example:
Pass: System handles 10,000 synthetic users with <1% error rate
Fail: Edge cases cause crashes or incorrect outputs
Next Step: Would you like me to generate a pol-probe artifact documenting this synthetic data simulation?
Option 5 Selected: "Will this solution survive real user contact?"
→ Recommended Probe: Vibe-Coded PoL Probe
What it is:
A Frankensoft stack (ChatGPT Canvas + Replit + Airtable) that creates just enough illusion for users to interact with a semi-functional workflow. Not production-grade—just enough to catch UX/workflow signals in 48 hours.
⚠️ Warning: This is the riskiest probe type. It looks real enough to confuse momentum with maturity. Use only when you need real user contact and other methods won't suffice.
Methods:
ChatGPT Canvas (quick UI generation)
Replit (host throwaway code)
Airtable (fake database)
Carrd/Webflow (landing page + workflow mockup)
Timeline: 2-3 days
Stack Example:
ChatGPT Canvas: Generate form UI
Replit: Host simple Flask/Node app
Airtable: Capture form submissions
Loom: Record user sessions for post-mortem analysis
Fail: Users get stuck, ask "Is this broken?", or abandon mid-flow
Learn: Identify exact step where users lose confidence
Disposal Plan: Delete all code after user sessions, archive Loom recordings, document learnings.
Next Step: Would you like me to generate a pol-probe artifact documenting this vibe-coded probe?
Step 3: Apply Component Skill
Agent offers:
I recommend using [selected probe type] for your hypothesis. Would you like me to:
Generate a complete pol-probe artifact (using the pol-probe.md template) with your hypothesis, success criteria, timeline, and disposal plan?
Walk through the execution steps for this probe type (tools, methods, user recruitment)?
Refine the hypothesis before proceeding (if it seems too broad or ambiguous)?
User selects option, and agent executes accordingly.
Step 4: Refine or Iterate (Optional)
If user is uncertain or hypothesis is too broad:
Agent asks:
Your hypothesis seems broad or tests multiple things at once. PoL probes work best when they're narrow and falsifiable. Let me ask a few clarifying questions:
Clarifying Questions:
What's the smallest thing you could test first? (Isolate one variable)
What would "failure" look like? (If you can't describe failure, you can't measure success)
Is this testing user behavior, technical feasibility, or stakeholder alignment? (Pick one focus)
Agent then re-recommends probe type based on refined hypothesis.
Examples
Example 1: Choosing Task-Focused Test
User Context:
Hypothesis: "Users can complete our checkout flow in under 3 minutes"
Risk: High cart abandonment on mobile
Timeline: 3 days
Resources: UsabilityHub account, 20 users from waitlist
Agent Reasoning:
Core question: "Can users complete this job without friction?"
Risk is task completion (observable behavior), not technical feasibility
Timeline fits Task-Focused Test (2-5 days)
Recommendation: Task-Focused Test using UsabilityHub
Method: Maze prototype testing with mobile users
Success criteria: 80%+ complete checkout in <3 minutes
Disposal: Delete prototype after analyzing heatmaps
Example 2: Choosing Feasibility Check
User Context:
Hypothesis: "We can auto-tag support tickets using GPT-4 with <5% error rate"
Risk: Unknown if AI can handle industry-specific jargon
Timeline: 1 day
Resources: 100 past support tickets, ChatGPT API access
Agent Reasoning:
Core question: "Can we build this?"
Risk is technical feasibility (AI capability), not user behavior
Timeline is short (1 day = spike territory)
Recommendation: Feasibility Check using prompt engineering spike
Method: Run 100 tickets through GPT-4 prompt chain, measure error rate
Success criteria: <5% misclassified tickets
Disposal: Delete spike code after documenting findings
Outcome: Error rate was 18%. Decided NOT to build feature. Saved 6 weeks of development.
Example 3: Choosing Narrative Prototype (NOT Vibe-Coded)
User Context:
Hypothesis: "Enterprise buyers will understand our multi-tenant security model"
Risk: Complex technical concept needs explanation
Timeline: 2 days
Resources: Loom, 5 enterprise prospects
Agent Reasoning:
Core question: "Does this workflow earn stakeholder buy-in?"
Risk is comprehension/alignment, not task completion
Building a functional prototype would over-invest (2 days = just explain it)
Recommendation: Narrative Prototype using Loom walkthrough
Method: 5-minute Loom explaining security model with diagrams
Success criteria: 4/5 prospects say "I understand this" without follow-up questions
Disposal: Archive video after 5 sessions
Why NOT Vibe-Coded Probe: You don't need users to interact with security settings—you need them to understand the concept. Narrative is cheaper and faster.
Example 4: Avoiding Prototype Theater
User Context:
Hypothesis: "Executives will approve budget if we show a polished demo"
Risk: Internal politics
Timeline: 3 weeks
Resources: Full design team
Agent Response:
⚠️ This is prototype theater, not a PoL probe.
You're testing internal politics, not user behavior or technical feasibility. PoL probes eliminate product risk, not organizational risk.
Recommendation: Skip the prototype entirely. Instead:
Use a Narrative Prototype (1-day Loom walkthrough) to explain the concept
Test with 5 target users (not executives) to validate the hypothesis
Present user feedback to executives, not a polished demo
If executives need a demo, build it after you've validated the hypothesis with real users.
Common Pitfalls
1. Choosing Based on Tooling Comfort
Failure Mode: "I know Figma, so I'll design a UI prototype" (even if design isn't the risk).
Consequence: Validate the wrong thing; miss the actual risk.
Fix: Answer the core question first, then pick the method. If you need a Feasibility Check but only know design tools, pair with an engineer for 1 day.
2. Defaulting to Code
Failure Mode: "Let's just build it and see what happens."
Consequence: 2 weeks of development before learning you tested the wrong hypothesis.
Fix: Ask: "What's the cheapest prototype that tells the harshest truth?" Usually it's NOT code.
3. Confusing Vibe-Coded Probes with MVPs
Failure Mode: Vibe-Coded probe "looks real," so team treats it like production code.
Consequence: Scope creep, technical debt, resistance to disposal.
Fix: Set disposal date before building. Vibe-Coded probes are Frankensoft by design—celebrate the jank, delete after learning.
4. Testing Multiple Things at Once
Failure Mode: "Let's test the workflow, the pricing, and the UI in one probe."
Consequence: Ambiguous results—you won't know which variable caused failure.
Fix: One probe, one hypothesis. If you have 3 hypotheses, run 3 probes.
5. Skipping Success Criteria
Failure Mode: "We'll know it when we see it."
Consequence: No harsh truth—just opinions and vanity metrics.
Fix: Write success criteria before building. Define "pass," "fail," and "learn" thresholds.
References
Related Skills
pol-probe (Component) — Template for documenting PoL probes
problem-statement (Component) — Frame problem before choosing validation method
Download skills/pol-probe-advisor/SKILL.md from the publisher, or copy the path and open the repository. PlugMCP lists the skill and does not host the file.
Where does pol-probe-advisor live?
The publisher keeps it at https://github.com/deanpeters/Product-Manager-Skills.
Which agents can use pol-probe-advisor?
Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.