Prospect Panel Simulator

Before you send the email, run the deck, or publish the pricing page — run it past the people who'd receive it. This skill simulates a panel of your actual prospects and reacts the way the market will: skeptical, busy, half-reading, comparing you to three other options.

Where customer-panel-of-experts debates a business decision with existing customers, this skill stress-tests a sales or marketing artifact against people who don't know you yet and don't owe you a reply.

What it pressure-tests

• Cold emails and full sequences (does it get a reply, or a delete?)
• Pitch decks and one-pagers (where do they check out?)
• Landing pages and pricing pages (what makes them bounce?)
• Demo scripts and discovery-call openers
• Proposals and SOWs (what gets pushed back on?)

Step 0 — Assemble the prospect panel

1. Preferred: load personas from icp-deep-scanner output (personas/, icp-profile.md) and seat the buying committee — economic buyer, champion, blocker, and end user — since a cold artifact hits all of them differently.
2. If no library exists and tools are connected, run icp-deep-scanner (read-only) to ground the panel in real won/lost-deal data and real objection language.
3. Bootstrap from the user's description only as a last resort, labeled PROVISIONAL.

Critically, model cold-state prospects: they have low context, low trust, and an alternative they already use. A simulated prospect who reads charitably is useless.

Security

Read-only connections. No sending, no writing to any tool. No real prospect names/emails in output — these are archetypes. Secrets stay in env vars.

Step 1 — Take in the artifact

Read exactly what will go out (paste, file, or URL via WebFetch). Note the channel and the moment: a cold email at 7am from an unknown sender is judged differently than a pricing page reached after a demo. Confirm: who is this for, what's the one action it's asking for, and what does the prospect see right before this?

Step 2 — Simulate reactions

Each panel member reacts in character through the real sequence of a busy buyer:

1. First 3 seconds — subject line / headline / first slide only. Open or delete? Keep scrolling or bounce? Be brutal; most things die here.
2. Skim — what they actually absorb on a fast read, what they skip, where the eye snags.
3. Objection — the specific reason this particular persona hesitates, stated in their words ("we already use X", "no budget this quarter", "who are these guys", "feels generic").
4. Trust check — does anything read as spam, AI-generated, over-promised, or off (the champion and the economic buyer flag different things).
5. Verdict + next action — reply / book / forward to {persona} / ignore / unsubscribe — and the honest probability.

Let personas disagree: a value prop that excites the end user can spook the economic buyer on price.

Step 3 — Report

markdown
# Prospect Panel — {Artifact}
Generated: {timestamp} · Panel: {personas} · Channel: {cold email / LP / deck} · Grounding: {data / PROVISIONAL}

## Predicted outcome: {STRONG / MIXED / WEAK} — est. reply/convert signal
One-line read on whether to send as-is.

## Reaction by persona
| Persona | Opens? | Gets it? | Top objection | Action |

## Where it loses people (ranked, with the exact line)
1. "{quoted line}" — {persona} → {reaction} → {fix}

## AI-tell / trust flags
- Phrases or patterns that read as generic, automated, or over-promised.

## Rewrite the weak points
- Before → After on the 2–3 highest-leverage lines.

## A/B worth running
- The one variable most worth testing live.

Step 4 — Iterate

Offer to apply the rewrites and re-run the panel on v2, or hand the winning angle to cold-email-sequence-generator / landing-page-copywriter to scale it.

Guardrails recap

Model cold, skeptical, time-poor prospects — not friendly readers · ground in real won/lost data when available, flag PROVISIONAL otherwise · read-only, no sending · quote the exact lines that fail · no real prospect PII.