bundled-skills/idea-autopsy/SKILL.md
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.
npx skillsauth add FrancoStino/opencode-skills-antigravity idea-autopsyInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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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.
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 |
|------|----------------------|--------------|----------|
Demand evidence, not optimism. One hard NO = dead.
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.
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).
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.
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.
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.
REJECTION.md (hence risk: critical). It never edits other files; ask permission before creating the file on first run.data-ai
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