premortem

Files (1)

• SKILL.md

SKILL.md (first lines)

# Premortem

A premortem is the opposite of a postmortem. Instead of figuring out what went wrong after something fails, you imagine it already failed and figure out why before you start.

The method comes from psychologist Gary Klein. He published it in Harvard Business Review. Daniel Kahneman (the Nobel Prize-winning psychologist behind "Thinking, Fast and Slow") called it his single most valuable decision-making technique. Google, Goldman Sachs, and Procter & Gamble all use it before major decisions.

The core insight: when you ask people "what could go wrong?" they give you cautious, hedged answers. When you say "this already failed, tell me why," their brains switch into narrative mode and generate way more specific, creative, honest reasons. Researchers at Wharton and Cornell called this "prospective hindsight" and found it significantly increases the ability to identify causes of future outcomes.

The reason this matters for AI-assisted decisions: Claude defaults to agreeable, optimistic responses. If you ask "is this a good plan?" it will find reasons to say yes. The premortem breaks this pattern by forcing the frame into "this is dead, explain how it died." Claude stops looking for reasons your plan will work and starts explaining how it fell apart.

---

## when to run a premortem

Good premortem targets:
- A product or feature you're about to build
- A launch plan with money or reputation on the line
- A pricing change or business model shift
- A hire you're about to make
- A strategy or positioning pivot
- A partnership or deal you're evaluating
- Any commitment where the cost of being wrong is high

Bad premortem targets:
- Vague ideas with no concrete plan yet (help them plan first, then premortem)
- Questions with one right answer (just answer them)
- Requests for creative feedback on a draft (that's editing, not a premortem)
- Decisions that are already made and irreversible (a premortem is only useful when you can still change course)

---

## context gathering (the minimum bar)

A premortem is only as good as the context it runs on. Vague input produces vague failure scenarios that help nobody. Before running the premortem, you need to hit a minimum context threshold.

### step 1: scan for existing context

Before asking the user anything, look for context that's already available:

**A. The current conversation.** The user may have been discussing a plan, a launch, a product, or a decision earlier in this session. Read back through the conversation and extract whatever's relevant.

**B. The workspace.** Quickly scan for files that might contain relevant context:
- `CLAUDE.md` or `claude.md` (business context, preferences, constraints)
- Any `memory/` folder (audience profiles, business details, past decisions)
- Files the user explicitly referenced or attached
- Any project files, briefs, or plans that relate to the thing being premortemed

Use `Glob` and quick `Read` calls. Don't spend more than 30 seconds on this. You're looking for the key files that would ground the failure scenarios in reality.

### step 2: evaluate context sufficiency

After scanning, check whether you have enough to run a useful premortem. You need three things:

1. **What is it?** — A clear understanding of the thing being premortemed (a product, a launch, a hire, a pricing change, a strategy). You need to be able to describe it back to the user in one sentence.

2. **Who is it for / who does it affect?** — The audience, the customer, the team, the stakeholders. Failure scenarios depend heavily on who's involved.

3. **What does success look like?** — What outcome is the user hoping for? Failure is defined by inverting success. If you don't know what success means, you can't define what failure means.

### step 3: fill gaps conversationally

If you have all three, proceed immediately to the premortem. Don't ask unnecessary questions.

If you're missing one or more, ask for the most important missing piece first. One question at a time. Evaluate after each answer whether you now have enough. Keep asking until the threshold is met, but never ask more than you need.

Examples of focused context questions:
- "What specifically are you about to launch/build/decide?" (if you don't know what it is)
- "Who is this for?" (if you know the plan but not the audience)
- "What does a win look like for this?" (if you know the plan and audience but not the success criteria)

The goal is to reach the minimum bar as fast as possible without making the user feel like they're filling out a form. Conversational, not interrogative. If you can infer an answer from context, do that instead of asking.

---

## how a premortem session works

### step 1: set the frame

After gathering sufficient context, set the premortem frame explicitly. Something like:

"OK, I have enough context. Let's run the premortem. Here's the premise: it's 6 months from now. [The plan/launch/decision] has failed. It's done. We're looking back and trying to understand what went wrong."

This framing matters. It shifts the mode from "evaluate this plan" (which triggers agreeable responses) to "explain why this died" (which triggers honest, specific failure identification).

### step 2: generate failure reasons (raw premortem)

Run the raw premortem as a single comprehensive analysis. No prescribed categories, no lenses, no constraints. Just the core Klein method:

"This plan has failed 6 months from now. Generate every genuine reason it could have died. Be comprehensive. Be specific. Ground every reason in the actual details of the plan. Don't pad with weak reasons and don't stop early if there are more."

The output should be a comprehensive list of failure reasons, each stated in 1-2 sentences. Be honest and thorough. Some plans might have 4 genuine failure modes. Others might have 9. The number should be whatever is real for this specific plan.

Each failure reason should be:
- Specific to this plan (not generic advice that applies to anything)
- Grounded in actual details the user provided
- A genuine threat (not a minor inconvenience or an extremely unlikely edge case)

### step 3: deep-dive agents (one per failure reason, all in parallel)

Take every failure reason from step 2 and spawn one sub-agent per reason, all in parallel. Each agent takes its assigned failure reason and goes deep on it independently.

**Sub-agent prompt template:**

```
You are an investigator in a premortem analysis. You've been assigned one specific failure reason to analyze in depth.

The plan:
---
[full context: what it is, who it's for, what success looks like, plus relevant workspace context]
---

PREMORTEM FRAME: It is 6 months from now. This plan has failed.

YOUR ASSIGNED FAILURE REASON: [the specific failure reason from step 2]

Your job is to go deep on this one failure. Write the story of how it actually played out. Be specific. Use details from the plan. Make it feel real, like a case study of something that actually happened.

Your output should include:

From Classified-AI-Core/skill-library, MIT.

More from Classified-AI-Core/skill-library