agentleFS
JakeschincariolJakeschincariol/founder-skill/skills/founder-consumer/SKILL.md

founder-consumer

Skill131 starsChanged 37 days agoMITView on GitHub

The consumer panel. Spins up a swarm of buyer sub-agents (default 100, --quick for 20) trained on the user's target customer, each with a different income, age, buying behaviour and objection, shows every one the same pitch, and runs the business through 100 buyer scenarios to see who buys, who doesn't, and why. Tallies the answers into buy rates by segment, the reasons people pass in their own words, what would flip a no, and price answers for founder-pricing. Use when the user says "would people buy this", "test it on customers", "run the consumer panel", "simulate buyers", "who is my customer", or "validate demand".

The file

We split this skill into the rules it gives an agent

6 rules. About 0 are common, 1 is distinctive, and 5 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈1

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 5

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    All bookkeeping goes through panel.py in this skill's folder:

    line 25
  2. Very specific:

    Write founder/customer.json (shape: customer.example.json in this folder):

    line 36
  3. Very specific:

    Write founder/pitch.md: what a buyer would actually see.

    line 54
  4. Distinctive:

    Wait for the whole wave before the next one.

    line 74
  5. Very specific:

    If a buyer printed its JSON in its reply instead of writing the file (it happens), file its own reply with PANEL save <id> and the reply on …

    line 83
  6. Very specific:

    Before a 100-buyer run, tell the user it is 100 sub-agent calls and suggest --quick (20 buyers) for a first look.

    line 88

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

founder-consumer

The craziest part of the pack. Instead of guessing whether people will buy, you ask a hundred of them, each one a sub-agent playing a specific buyer drawn from your target customer. You are the moderator. You never answer for a buyer.

What the user typed after /founder-consumer: $ARGUMENTS

The tool

All bookkeeping goes through panel.py in this skill's folder:

python3 "${CLAUDE_SKILL_DIR}/panel.py" <command>

Below, PANEL means exactly that. If the path is not expanded, use the base directory Claude Code printed for this skill. State lives in founder/panel/.

Step 1: the target customer

Write founder/customer.json (shape: customer.example.json in this folder):

  • business, and target: who the customer is, in one specific line.
  • segments: 2 to 4 groups with a share and an income range [low, high].
  • behaviours: 4 to 8 buying behaviours with a share and a how line (daily regular, deal hunter, reads every review, loyal to a competitor...).
  • objections: the 4 to 8 doubts this kind of buyer really has.
  • habits (optional): everyday context lines that make a buyer concrete.

Ground it in something real. Use what the user knows about their customers, and if /founder-competitors ran, the complaints in founder/competitors.md. If the user has real customer data (interviews, reviews, survey answers), prefer it to your guesses and say which parts are guesses. Show the user the profile in a few lines and let them correct it before you deal: a wrong customer makes the whole panel wrong.

Step 2: the pitch

Write founder/pitch.md: what a buyer would actually see. The product, the price, where and how to get it, any opening offer. Three to six plain lines, no superlatives, nothing the business cannot deliver. Every buyer sees exactly this.

Step 3: deal and run

PANEL init --customer founder/customer.json --pitch founder/pitch.md --n 100
PANEL prompts

init deals the persona cards (same seed, same cards; the seed is recorded). prompts writes one brief per buyer and lists them in waves of 10. For each wave, send ONE message with one Agent tool call per buyer in that wave (the Agent tool is called Task in older Claude Code versions):

  • subagent_type: general-purpose
  • description: panel <id>
  • prompt: Read <brief path> and follow it exactly. It is your whole brief.

Wait for the whole wave before the next one. Claude Code runs about 10 tool calls at once by default, so 100 buyers is 10 waves. After the last wave:

PANEL check      # lists missing or broken answers
PANEL prompts    # rewrites briefs for those only; run them once more
PANEL tally

If a buyer printed its JSON in its reply instead of writing the file (it happens), file its own reply with PANEL save <id> and the reply on stdin; save refuses anything that is not a valid answer. A buyer that fails twice is left out and counted in the results. Never write an answer for a buyer yourself.

Before a 100-buyer run, tell the user it is 100 sub-agent calls and suggest --quick (20 buyers) for a first look. Sub-agents write one file each into founder/panel/answers/; in the default permission mode that is one approval per file, so suggest accept-edits mode (Shift+Tab) for the run.

Step 4: read it to the user

PANEL tally writes founder/panel/results.md and results.json. Give the user:

  1. The headline: "40 buy · 60 pass" and the buy rate.
  2. Who buys and who doesn't: the segment and behaviour rows that differ most.
  3. Why they pass: the top three reasons with one quote each, as written.
  4. What would flip a no: the most repeated answers.
  5. One sentence on what to change before spending money, and which skill does it (pricing, offer, marketing).

Honest limits, say them once

These are simulated buyers. They are good at surfacing objections, segments and wording you had not thought of, and bad at predicting real conversion: language models lean agreeable, so treat the buy rate as an upper bound. Confirm the big calls with real people (a waitlist, a pre-sale, a landing page with a price) before signing anything. Do not present panel quotes as customer testimonials.

The brief every buyer gets

panel.py fills the card into a fixed template (see BRIEF in the script): who the buyer is (name, age, segment, income, buying behaviour, a habit, the doubt that comes to mind), the pitch word for word, an instruction not to be agreeable and not to invent facts, and the exact JSON to write: buys, purchases in the first month, a reason code, the reason in their own voice, their main objection, what would change their mind, and four price answers (too cheap, a bargain, getting expensive, too expensive). Never edit a brief for one buyer.

Output

founder/customer.json, founder/pitch.md, founder/panel/ (cards, briefs, answers, results). Next: /founder-pricing reads the price answers, /founder-offer and /founder-marketing read the objections.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.