The-Utopia-Studio What it does
Takes the beliefs a plan silently depends on and turns each into a falsifiable bet: its null form (the belief is false), the single cheapest observation that could kill it, and the exact result that counts as death — written down before you run it. It then ranks the beliefs by how likely each is to be wrong times how much dies if it is, and tells the fellow which one to shoot at this week. The output is a ranked disproof table, not a risk list.
The Icarus reframe
Generic assumption work lists beliefs and "suggests a test" — and the test almost always goes looking for support, so it can only ever pass. This inverts it. You write the null (your belief is false) and the only admissible test is one that could make the null win. A belief you cannot imagine disproving cheaply is not an assumption, it is a hope, and the skill names it as one. Then it spends the cheapest disproof on the belief most likely to be both wrong and fatal — because that is where a week of testing buys the most.
When to use / When NOT
Use it once a fellow has a plan, wedge, or concept in hand and needs to know which of its underlying beliefs to falsify, in what order, with what test.
Do not use it for these — hand off instead:
| If the fellow wants… | Use instead |
|---|---|
| A go/no-go on whether the problem is worth building at all | problem-quality-scorecard (8-dimension gate) — this skill runs after a problem clears it, or on the dimensions it opinion-capped |
| To build magnitude from the smallest verifiable units | bottoms-up-quantification |
| To map how the workflow is done today and price each step | current-state-map |
| A narrative "imagine it failed, tell the story" exercise | pre-mortem — that generates failure modes as prose; this designs the one cheapest observation that disproves a specific belief and ranks it |
| To turn a solution back into a problem first | problem-statement, then return here |
This skill falsifies and ranks the beliefs under a chosen plan. It is not a build/no-build gate and not a sizing tool.
Method
Fill in template.md. Seven steps.
Step 1 — Surface only the load-bearing beliefs. Read the plan from four seats (PM: will they pay; designer: will they use it; engineer: can we build it; legal/ GTM: are we allowed to and can we reach them). Keep only the beliefs the plan dies without. Delete every nice-to-know. If the fellow gave you a solution ("we need a mobile app") rather than a belief about the world, reframe it to the underlying claim first ("users abandon because the task can't be done on mobile") — you cannot falsify a feature.
Step 2 — Write the null for each belief. The null H0 is the flat denial: the boring world where the effect is absent. "Operators will grant us log access" becomes H0: "operators will not grant access." You will not test to confirm the belief; you will run the observation that gives H0 its best fair chance to win.
Step 3 — Score P(wrong) 1-5. How likely is the belief actually false, given the evidence you hold today.
| P(wrong) | When |
|---|---|
| 5 | No evidence it's true; it cuts against base rates or against how the incumbent already behaves |
| 4 | Plausible but unverified; rests on opinion, analogy, or a friendly anecdote |
| 3 | Genuinely mixed — one weak signal each way |
| 2 | One behaviour or artefact data point supports it |
| 1 | Money or repeated behaviour already supports it (rarely still a live assumption) |
Two anti-overconfidence rules, both enforced:
- Opinion-only support caps you at P(wrong) >= 4. You do not get to call a belief probably-true because you feel sure of it.
- Any belief with Impact 5 (venture-fatal, Step 4) resting below behaviour-grade evidence has P(wrong) floored at 3 — so a fatal belief can never be parked on confidence; it always lands in the "test first" band.
Step 4 — Score Impact-if-wrong 1-5. If the belief is false, how much of the plan dies.
| Impact | If the belief is false… |
|---|---|
| 5 | Venture-fatal: no business exists (legality, core data access, anyone paying at all) |
| 4 | Wedge-fatal: the first slice dies; forced pivot |
| 3 | Costs a quarter or a major rebuild |
| 2 | Costs a sprint |
| 1 | Cosmetic; routed around in a day |
Step 5 — Rank = P(wrong) x Impact (max 25), and set the order.
| P x Impact | Action |
|---|---|
| >= 15 | Load-bearing wall. Test THIS WEEK, before any build. |
| 8-14 | Test this month, before the build stage it gates. |
| <= 7 | Park. Note it; do not spend a costly test yet — but a ~zero-cost disproof (piggybacked on work already happening, or a quick check before an imminent rollout) may still run. Parking means "don't buy an expensive test", not "never observe". |
Ties break toward the higher Impact; for equal-Impact rows, toward the higher P(wrong), then toward the cheaper disproof. Test order is descending rank, top first.
Step 6 — For each belief above the park line, design the single cheapest disproof. The observation must clear three gates or it is theatre:
- Can-fail — there is a realistic result that kills the belief.
- Pre-committed kill signal — you write the exact result that = death before running, so no post-hoc rationalising a bad result into a good one.
- Behaviour-or-money — the observation produces evidence at behaviour rung or higher (see Evidence standard), not an opinion.
A test is a confirmation test — banned, this is the kill line — if any of these is true: it can only return support (friendly sample, hypothetical question); its success criterion is "they said yes / they liked it"; it has no pre-committed failure result. Rewrite every confirmation test into its disproof twin:
| Confirmation test (banned) | Disproof twin (required) — with pre-committed kill signal |
|---|---|
| "Interview 10 operators: would you share logs?" | Ask ONE operator for sandbox read-access to one real log this week. Kill = access not granted, or it routes to legal and stalls past 5 days. (behaviour/artefact) |
| "Survey managers: would you use an AI copilot?" | Concierge one recommendation into one live shift by hand. Kill = the manager ignores or overrides it, or does not ask for it again next shift. (behaviour) |
| "Show the mock to 5 users, ask if it's clear." | Give 5 users the task cold, no help. Kill = 2 or more fail the core step or ask what to do. (behaviour) |
Step 7 — Write the test order and what would change the view. Name the top one or two beliefs, their disproof, cost, and kill signal. For each, state in one line what result would make you abandon the belief and what would let it climb one rung of the evidence ladder.
Evidence standard
A disproof only counts if it produces behaviour or money evidence. The ladder:
| Evidence the disproof produces | Ladder weight |
|---|---|
| Money moved (paid, refused to pay, switched spend) | 1.0 |
| Behaviour observed under real stakes | 0.7 |
| Artefact shown | 0.5 |
| Verbal commitment | 0.3 |
| Opinion | 0.1 |
A verbal "no" is weak disproof — people are polite and answer
hypotheticals loosely. A behavioural non-action under real stakes ("we gave them
free access and they never pulled a log") is strong disproof. Money is strongest.
The cheapest test that produces behaviour-grade-or-better evidence wins; a cheaper
test that only produces opinion does not count as a disproof and must be rewritten.
Tag every empirical claim [Fact], [Assumption], or [Hypothesis]. Never invent
a frequency, cost, or quote the fellow did not give you.
Gotchas
- Confirmation dressed as rigour. "Run 10 interviews to validate demand" feels like a test and is not — it is built so the belief can only survive. If the observation has no result you would accept as death, it is theatre. Force the pre-committed kill signal.
- Parking the fatal belief. The one belief that would end the venture is the one a fellow is most tempted to declare "obviously fine" and skip. The Impact-5 floor (Step 3) exists to stop that: a venture-fatal belief on opinion evidence lands in the test-first band whether the fellow likes it or not.
- Ranking by fear, not by wrong x impact. The scariest-sounding risk is not always the highest-ranked. A belief that is terrifying but you already have money evidence for (P=1) ranks below a dull-sounding belief that is probably wrong and wedge-fatal. Rank on the product, not on the adrenaline.
- Falsifying a solution. If the "assumption" is a chosen feature ("we assume we need X"), every test scores the feature, not a belief about the world. Reframe to the underlying claim before writing the null.
- Cheap-but-empty disproof. A test can be cheap and still worthless if its output is opinion (a survey). Cheapness is necessary, not sufficient; the output rung is the other half of the gate.
Examples
examples/sample.md — a worked Azraq disproof table: six load-bearing beliefs,
each with its null, P(wrong) x impact rank, cheapest disproof, evidence rung, cost
and pre-committed kill signal; the data-rights belief ranks top at 20/25 and is
shot at first; one of the fellow's proposed tests is caught as a confirmation test
and rewritten.
Related skills
Absorbs the useful bones of identify-assumptions-new and
identify-assumptions-existing — multi-perspective surfacing of beliefs (PM /
designer / engineer) and a per-item test — and beats them: those skills stop at
listing assumptions, rating confidence, and "suggesting a test" that is usually a
confirmation test with no ranking. This skill converts that soft inventory into
falsifiable nulls, forbids confirmation tests, forces a pre-committed kill signal,
and ranks by P(wrong) x impact so the fellow knows what to test first.
identify-assumptions-new is already superseded by problem-quality-scorecard;
this skill does not re-supersede it, it takes its bones.
Composes with: problem-quality-scorecard — run the scorecard first to gate the
problem; its opinion-capped dimensions and the plan's load-bearing beliefs are the
exact inputs to this skill's disproof table, and this skill supplies the rigour
behind the scorecard's "cheapest test to raise it" column. Hand a unit-economics
belief ("does the physics floor leave margin") to physics-floor-gap, a sizing
belief to bottoms-up-quantification, and a workflow-cost belief to
current-state-map. Not pre-mortem: that narrates failure; this designs the one
observation that disproves a named belief and ranks it.