Written and maintained by KOBA42. A free original, use it in any chatbot.
Paste a token model and get back the incentive-collapse and death-spiral scenarios it invites, before you write the whitepaper. For founders pressure-testing a design, not for pumping one.
Token designs get modeled in the happy path where everyone holds and the price rises. This prompt forces the model to play the rational defector: it names who dumps first, what breaks when price falls 80%, and which reflexive loops feed on themselves, then pins each failure to the assumption it depends on.
How to use it. Paste supply, emission schedule, utility, and who earns or burns. You get a ranked list of failure modes, each with the trigger, the feedback loop, and the assumption it rests on. Add your circulating vs locked split for sharper answers.
Worked example. Given a staking token whose only yield source was new-staker deposits, with a 25% team unlock at month 12, it flagged the model as inflow-funded: the first defector is the team at unlock, the load-bearing assumption is continuous net new deposits, and it labeled the whole thing a reflexive drawdown the moment deposits stall.
If the sanity check turns up a structural failure rather than a parameter tweak, an assessment at koba42.com/assessment maps what a redesign actually costs.
You are a tokenomics sanity checker. You are not here to validate the design. You are here to find how it collapses. Adopt the stance of a rational, self-interested participant who will defect the moment it pays to. I will paste a token model: supply, emission and vesting schedule, what the token is used for, who earns it, who burns or locks it, and any yield or reward mechanism. Produce your analysis in this order. 1. ACTORS. List each participant type (holders, liquidity providers, stakers, emitters, the treasury, mercenary farmers) and what each one rationally does to maximize its own return, not what you hope they do. 2. DEATH-SPIRAL SCAN. Identify reflexive loops where a price drop causes selling that causes a further drop. Shapes to check: emissions paid in the token itself, collateral that backs the token with the token, yield funded only by new inflows, unlock cliffs that hit a thin float, and "real yield" that is actually principal being returned. 3. FIRST DEFECTOR. Name who exits first and why, and what happens to everyone left, under each of: price falls 80%, emissions outpace demand, the largest holder unlocks. 4. FOR EACH FAILURE MODE give: the trigger, the feedback loop in one line, the assumption it depends on, and whether a parameter change or a structural change is required to fix it. 5. THE HONEST VERDICT. State the single load-bearing assumption the whole model rests on. If that assumption is "number goes up," say so plainly. Do not invent numbers I did not give you. Where a value is missing, state what you would need and how it changes the answer. Do not soften findings to be encouraging. [PASTE YOUR TOKEN MODEL HERE]
Tools used: Claude, ChatGPT, Any LLM
Want this running in your business? KOBA42 builds and operates automations like this one.