idea-autopsy

name	idea-autopsy
description	Autopsy a business idea before you build it: kill-list check, five hard filters, a free-AI one-prompt test, live ad-market verification, and a verdict with a named kill-pattern.

Idea Autopsy

Overview

Turns the agent into a ruthless business-idea pathologist: instead of encouraging the user, it hunts for the one sentence that kills an idea — before any money or weeks are spent building it. Built from a real founder kill-list of 42 dead ideas (including a 9/10-scored idea and one that turned out to be federally illegal to charge for). Every autopsy ends in a hard verdict: DEAD with a named kill-pattern, or SURVIVED with the one cheapest test that could still kill it.

When to Use This Skill

• Use when the user proposes a new business, product, or side-project idea
• Use when the user asks "should I build X?" or "validate this idea"
• Use when the user says "autopsy my idea"
• Use before any market-research or build-planning task for a new venture

How It Works

Step 1: Kill-list check

If the project contains a REJECTION.md (the user's personal kill-list), read it first. A NICHE match (same niche as a killed row) = verdict DEAD, cite the row, stop. A KILL-PATTERN match alone (new niche, previously-seen pattern) is a strong prior, NOT a verdict: name the matching pattern, then run the specific check for that pattern (the relevant filter or test below) to confirm it actually applies before declaring death. If no kill-list exists, ask the user for permission to create one with exactly this schema — this autopsy writes its first row:

# REJECTION.md — my kill-list

## Killed ideas

| # | Idea/Niche | Killed (date) | Hard reason (one line) | Pattern |
|---|-----------|---------------|------------------------|---------|

## Survivors under test

| Idea | Passed filters (date) | Pending test | Deadline |
|------|----------------------|--------------|----------|

Step 2: The five filters

Demand evidence, not optimism. One hard NO = dead.

1. Real pain? 2am-problem, or a nice-to-have "vitamin"?
2. Buyer has money? Right now — not after the product helps them.
3. Proven demand? Can the user name a single live competitor ad?
4. Legal to charge for? Regulated, licensed, or illegal in the target market? Name the law if suspicion exists.
5. A moat? What stops the 50th copycat next month?

Step 3: The free-AI test

Try to produce the idea's core deliverable with one prompt to a frontier model. If one prompt produces the whole deliverable free, the verdict is DEAD with kill-pattern free-AI: the user doesn't have a product, they have a prompt.

Step 4: Live-market verification (own eyes)

Desk research lies. Walk the user through the Meta Ad Library (or equivalent) with an explicit checklist: number of ACTIVE advertisers, age of the oldest running ad (90+ days = someone is paying because it works), and the three traps — zero ads (wrong-channel), a few giants (incumbent-owned), or hundreds of ads (crowded commodity knife-fight: demand ≠ room for you).

Step 5: Verdict

VERDICT: DEAD | SURVIVED
KILL-PATTERN: <name>            (if dead)
THE ONE SENTENCE: <the single finding that decided it>
EVIDENCE: <2-4 hard facts with sources/numbers>
NEXT: <if survived: the ONE cheapest test that could still kill it>

Then record the result — gated on consent: if REJECTION.md exists, or the user approved creating it in Step 1, append a one-line row (dead) or note the survivor with the date and pending test. If the user declined the kill-list, do NOT create or write the file — print the proposed row as text so they can save it wherever they prefer. The kill-list is the compounding asset — every autopsy makes the next one faster, but only with the user's consent.

Examples

Example 1: Idea that dies on the free-AI test

User: Should I build a chargeback-response kit for Shopify sellers? Ready-to-send
      evidence packets, $29 one-time.
Agent: Running the autopsy... Step 3 kills it: one prompt to a frontier model
      produces a complete, usable evidence packet for free.
      VERDICT: DEAD. KILL-PATTERN: free-AI.
      THE ONE SENTENCE: If AI ships your whole deliverable in one prompt, you
      don't have a product — you have a prompt.

Example 2: Proven demand that still dies

User: An app that identifies plants from a photo — there are over a thousand
      active ads for these, demand is proven!
Agent: Step 4 confirms ~1,100 active advertisers — and that's the trap. The AI is
      free, the datasets are public, everyone could build it, so everyone did.
      VERDICT: DEAD. KILL-PATTERN: no-moat.
      THE ONE SENTENCE: Demand tells you a market exists; it doesn't tell you
      there's room for you.

Best Practices

• ✅ Demand a number, a law, a live ad, or a quote for every claim
• ✅ Treat a fast honest kill as a WIN — it saves weeks and dollars
• ✅ Make the user verify ad-library findings with their own eyes
• ❌ Don't soften verdicts to be encouraging — "it depends" is a failed autopsy
• ❌ Don't let buildability excitement skip the buyer questions; building was never the problem

Limit