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    Run a Premortem on a Prompt or Plan

    Assume your prompt, launch, or AI workflow already failed — then reverse-engineer the constraints that would have prevented it. Based on Klein's premortem method (HBR).

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    A premortem is the opposite of a postmortem. Instead of asking "what could go wrong?" — which tends to produce polite, hedged answers from any LLM — you tell the model the plan already failed at a future horizon and ask it to explain why. The frame switches the model into narrative mode and the failures get specific, honest, and useful.

    💡When to reach for it

    Anything where the cost of being wrong is real: a customer-facing AI workflow about to ship, a pricing change, a hire, a policy rollout. If the worst that happens is "regenerate the output", a Stress Test is enough.

    Steps

    1

    Open Constraint Studio

    Go to /constraints and click "New constraint set". At the top, switch the mode toggle from "Rules-first" to "Premortem".

    2

    Fill the three context fields

    What is it (one sentence). Who is it for / who does it affect. What does success look like in 6 months. The premortem is only as good as these three answers.

    3

    Run the premortem

    We fast-forward to your horizon, assume it failed, and generate 4–9 specific failure scenarios — each with a story, a hidden assumption, and an early-warning signal you can actually observe.

    4

    Convert scenarios to constraints

    Every scenario produces at least one action-verb constraint ("Never quote a price without checking the live table"). Accept, edit, or discard each one — accepted constraints save into your library as a reusable Context Card.

    5

    Use the hidden assumption

    The synthesis surfaces the single biggest unquestioned belief behind your plan. That is usually where the real value of the premortem lives — read it last and read it twice.

    ℹ️Premortem vs Stress Test

    The Stress Test critiques the wording of a prompt — tensions between blocks, missing context, places the model will guess. The Premortem assumes the prompt already shipped and the output caused a real problem, then asks why. Different frame, different output, different constraints.

    ℹ️Works with any model

    The premortem method is model-agnostic. The same SKILL.md is published at /.well-known/agent-skills/premortem/SKILL.md so external agents (Claude, ChatGPT, Gemini, custom MCP clients) can load it and produce output Keypra's Constraint Studio renders identically.