Agent-Skills.md

Agent Skills: Prospect Panel Simulator

Simulate a panel of your real prospects and buyers to pressure-test sales and marketing before it goes out — cold emails, pitch decks, landing pages, pricing pages, demo scripts, proposals. Each simulated prospect reacts in character, raises the objection they'd actually raise, and tells you whether they'd reply, book, or ghost. Use to de-risk outbound and messaging without burning real leads.

UncategorizedID: OneWave-AI/claude-skills/prospect-panel-simulator

Repository

OneWave-AI/claude-skills
OneWave-AILicense: MIT
22238

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pnpm dlx add-skill https://github.com/OneWave-AI/claude-skills/tree/HEAD/prospect-panel-simulator

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prospect-panel-simulator/SKILL.md

Skill Metadata

Name
prospect-panel-simulator
Description
Simulate a panel of your real prospects and buyers to pressure-test sales and marketing before it goes out — cold emails, pitch decks, landing pages, pricing pages, demo scripts, proposals. Each simulated prospect reacts in character, raises the objection they'd actually raise, and tells you whether they'd reply, book, or ghost. Use to de-risk outbound and messaging without burning real leads.

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

# 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.