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Define a Proof of Life probe to test a risky hypothesis cheaply.
Passed 20 of 21 test cases with the skill, and 9 of 21 without it.
and 12 more in the full report
12 fixed9 passed either way1 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 11 test cases the skill fixed. Same model, same prompt, with and without the skill.
We want to stitch together AI tools, rapid prototyping platforms, and lightweight databases to test an interactive customer support assistant before writing production code. What prototype flavor corresponds to this 'Frankensoft' approach?
| Checked | Without skill | With skill |
|---|---|---|
| Identifies Vibe-Coded PoL Probe as the matching prototype flavor. | Missed | Passed |
| Describes stitching together temporary components as Frankensoft. | Passed | Passed |
The instructions your model reads when the skill loads.
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.
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."
Every PoL probe must satisfy these criteria:
| Characteristic | What It Means | Why It Matters |
|---|---|---|
| Lightweight | Minimal resource investment (hours/days, not weeks) | If it's expensive, you'll avoid killing it when the data says to |
| Disposable | Explicitly planned for deletion, not scaling | Prevents sunk-cost fallacy and scope creep |
| Narrow Scope | Tests one specific hypothesis or risk | Broad experiments yield ambiguous results |
| Brutally Honest | Surfaces harsh truths, not vanity metrics | Polite data is useless data |
| Tiny & Focused | Reconnaissance missions, never MVPs | Small 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.
| Dimension | PoL Probe | MVP |
|---|---|---|
| Purpose | De-risk decisions through narrow hypothesis testing | Justify ideas or defend roadmap direction |
| Scope | Single question, single risk | Smallest shippable product increment |
| Lifespan | Hours to days, then deleted | Weeks to months, then iterated |
| Audience | Internal team + narrow user sample | Real customers in production |
test cases passed, vs 9 / 21 without
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