Assumption Audit
Surface the things a plan quietly takes for granted, write them down where everyone can see them, and rank them by blast radius — then go test the dangerous ones before reality does.
When to use
- Before committing significant time, money, or reputation to a plan.
- An estimate or forecast feels confident but you can't say why.
- A plan depends on things outside your control (a partner, a market, a tool, a user behavior).
- A post-mortem found a failure that "no one saw coming" — it was an unexamined assumption.
- The user wants a prioritized de-risking list, not a vague "it might not work."
When NOT to use
- Pure execution of an already-validated plan where assumptions were checked recently.
- When the assumptions are cheap to be wrong about and trivially reversible.
- Analysis-paralysis situations where the user needs to act, not enumerate more risks.
The method
- Restate the plan/belief/estimate plainly. Get the claim in one or two sentences so the assumptions have something to hang off.
- Hunt assumptions in every category. Go category by category so you don't miss whole classes:
- Factual — "X is true" (data, market size, a number you didn't measure).
- Behavioral — "people/users/the team will do Y."
- Causal — "if we do A, B will follow."
- Resource — "we'll have the time/money/skill/access."
- Stability — "the world won't change underneath us" (prices, rules, dependencies).
- Definitional — "we all mean the same thing by this word/goal."
- Make the implicit explicit. Rewrite each as a flat declarative sentence: "We are assuming that ___." If you can't say it cleanly, you don't understand it yet.
- Find the load-bearing ones. Mark which assumptions the whole plan depends on — remove the assumption and the plan collapses. These are structural; the rest are cosmetic.
- Score each on two axes: Impact if wrong (cosmetic → fatal) and Confidence it's true (proven → pure guess). The danger zone is high impact + low confidence.
- Rank by risk and sort the action. Order by impact-if-wrong × uncertainty. For the top ones, define the cheapest possible test that would prove or kill the assumption (an experiment, a phone call, a small spend, a lookup).
- Decide per assumption: test it now, build a fallback for it, or knowingly accept the risk. The goal is that no fatal assumption is left both untested and unacknowledged.
What good looks like
- A clean list of explicit "We are assuming that ___" statements.
- The 2-3 load-bearing, low-confidence assumptions clearly flagged as the real risk.
- Each top assumption paired with a cheap, concrete test — not "we should validate this" but "call three customers and ask X."
- Confidence levels stated honestly, including admitting "this is a guess."
- The user knows exactly what to verify first thing tomorrow.
Anti-patterns
- Listing only the comfortable assumptions you already know are true — the dangerous ones are the ones you don't want to look at.
- Flat lists with no ranking — 20 unranked assumptions is noise.
- Stopping at naming without proposing a test or fallback.
- Fake confidence: scoring a wild guess as "high confidence" because admitting uncertainty is uncomfortable.
- Auditing trivia while skipping the one belief the whole plan rests on.
Example
Plan: "We'll hit Q4 revenue by converting 5% of free users to paid after launching the new tier." Audit surfaces: (Behavioral) we assume free users want a paid tier at all — confidence: low, impact: fatal → load-bearing. (Factual) we assume the 5% conversion rate — based on a blog post, not our data — confidence: low, impact: high. (Resource) we assume the tier ships by October — confidence: medium. (Stability) we assume no competitor launches a free equivalent — confidence: low, impact: high. Ranked, the danger zone is "free users want to pay" + "5% rate." Cheapest tests: put a fake "Upgrade" button in the app and measure click-through this week (tests willingness for ~$0), and pull conversion benchmarks from a comparable product instead of trusting the blog. Everything else can wait or get a fallback.
1---2name: assumption-audit3description: Excavates the hidden assumptions baked into a plan, belief, or estimate, lists them explicitly, then ranks them by how much damage results if each turns out to be wrong — so the riskiest get tested first. Use this skill when the user asks "what am I assuming", "audit my assumptions", "what am I taking for granted", has a shaky estimate or forecast, is building a plan that depends on unknowns, wants to de-risk before committing resources, or when a plan feels confident but rests on untested beliefs. Best when a plan looks solid on the surface but its foundations have never been checked.4---56# Assumption Audit78Surface the things a plan quietly takes for granted, write them down where everyone can see them, and rank them by blast radius — then go test the dangerous ones before reality does.910## When to use11- Before committing significant time, money, or reputation to a plan.12- An estimate or forecast feels confident but you can't say why.13- A plan depends on things outside your control (a partner, a market, a tool, a user behavior).14- A post-mortem found a failure that "no one saw coming" — it was an unexamined assumption.15- The user wants a prioritized de-risking list, not a vague "it might not work."1617## When NOT to use18- Pure execution of an already-validated plan where assumptions were checked recently.19- When the assumptions are cheap to be wrong about and trivially reversible.20- Analysis-paralysis situations where the user needs to act, not enumerate more risks.2122## The method231. **Restate the plan/belief/estimate plainly.** Get the claim in one or two sentences so the assumptions have something to hang off.242. **Hunt assumptions in every category.** Go category by category so you don't miss whole classes:25 - **Factual** — "X is true" (data, market size, a number you didn't measure).26 - **Behavioral** — "people/users/the team will do Y."27 - **Causal** — "if we do A, B will follow."28 - **Resource** — "we'll have the time/money/skill/access."29 - **Stability** — "the world won't change underneath us" (prices, rules, dependencies).30 - **Definitional** — "we all mean the same thing by this word/goal."313. **Make the implicit explicit.** Rewrite each as a flat declarative sentence: "We are assuming that ___." If you can't say it cleanly, you don't understand it yet.324. **Find the load-bearing ones.** Mark which assumptions the whole plan *depends* on — remove the assumption and the plan collapses. These are structural; the rest are cosmetic.335. **Score each on two axes:** *Impact if wrong* (cosmetic → fatal) and *Confidence it's true* (proven → pure guess). The danger zone is **high impact + low confidence**.346. **Rank by risk and sort the action.** Order by impact-if-wrong × uncertainty. For the top ones, define the cheapest possible test that would prove or kill the assumption (an experiment, a phone call, a small spend, a lookup).357. **Decide per assumption:** test it now, build a fallback for it, or knowingly accept the risk. The goal is that no fatal assumption is left both untested and unacknowledged.3637## What good looks like38- A clean list of explicit "We are assuming that ___" statements.39- The 2-3 *load-bearing, low-confidence* assumptions clearly flagged as the real risk.40- Each top assumption paired with a cheap, concrete test — not "we should validate this" but "call three customers and ask X."41- Confidence levels stated honestly, including admitting "this is a guess."42- The user knows exactly what to verify first thing tomorrow.4344## Anti-patterns45- **Listing only the comfortable assumptions** you already know are true — the dangerous ones are the ones you don't want to look at.46- **Flat lists with no ranking** — 20 unranked assumptions is noise.47- **Stopping at naming** without proposing a test or fallback.48- **Fake confidence:** scoring a wild guess as "high confidence" because admitting uncertainty is uncomfortable.49- **Auditing trivia** while skipping the one belief the whole plan rests on.5051## Example52Plan: "We'll hit Q4 revenue by converting 5% of free users to paid after launching the new tier." Audit surfaces: (Behavioral) *we assume free users want a paid tier at all* — confidence: low, impact: fatal → load-bearing. (Factual) *we assume the 5% conversion rate* — based on a blog post, not our data — confidence: low, impact: high. (Resource) *we assume the tier ships by October* — confidence: medium. (Stability) *we assume no competitor launches a free equivalent* — confidence: low, impact: high. Ranked, the danger zone is "free users want to pay" + "5% rate." Cheapest tests: put a fake "Upgrade" button in the app and measure click-through this week (tests willingness for ~$0), and pull conversion benchmarks from a comparable product instead of trusting the blog. Everything else can wait or get a fallback.
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