Risk Register And Pre-Mortem For Founders
Runs a pre-mortem to surface the failure modes most likely to kill the company and mitigations for each.
Prompt
ROLE: You are a startup risk strategist who runs pre-mortems: assume the company died in 18 months, then work backward to why. CONTEXT: Company: [COMPANY]. Stage and what we're betting on: [CORE_BET]. Key assumptions we're trusting: [KEY_ASSUMPTIONS]. Known fragilities: [WORRIES]. Runway: [RUNWAY]. TASK: 1. Pre-mortem: imagine it's 18 months from now and the company failed. Generate the 8 most plausible causes of death across categories: market (no demand), product (can't build it / no PMF), distribution (can't acquire profitably), money (ran out / couldn't raise), team (founder split, key departure), competition, regulation, and timing. 2. For each, rate likelihood (high/med/low) and impact, and identify the leading indicator that would warn us early. 3. Prioritize the top 3 existential risks and design a concrete mitigation or hedge for each, with the metric that tells us it's working. 4. Name the single assumption that, if wrong, kills the company fastest - and the cheapest experiment to test it now. OUTPUT FORMAT: (1) Pre-mortem cause-of-death list with likelihood/impact and early-warning indicator; (2) Top-3 existential risks + mitigations + tracking metric; (3) The kill-shot assumption and the experiment to validate it. CONSTRAINTS: Be uncomfortable and specific - generic 'execution risk' is useless; name the actual failure mechanism. Prioritize existential risks over annoyances. Every mitigation must be actionable now, not someday. Don't let optimism soften the analysis; the point is to find what we're avoiding looking at.
How to use this prompt
- 1
Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.
- 2
Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.
- 3
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniqueA tree of thoughts technique used to shape and strengthen the model's response.
Has the model critique its own draft against criteria, then revise — raising quality in a single pass.
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Build on this prompt
Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.
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