Agent-Skills.md

Agent Skills: Customer Panel of Experts

Build a panel of your real buyer personas (from a deep scan of any tools you allow it to connect to) and have them debate any decision you bring — a marketing launch, a price increase, a new product, a positioning change, a feature cut. Returns a structured debate, the strongest objections, and a clear recommendation. Use when you want your actual customers in the room before you commit.

UncategorizedID: OneWave-AI/claude-skills/customer-panel-of-experts

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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/customer-panel-of-experts

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customer-panel-of-experts/SKILL.md

Skill Metadata

Name
customer-panel-of-experts
Description
Build a panel of your real buyer personas (from a deep scan of any tools you allow it to connect to) and have them debate any decision you bring — a marketing launch, a price increase, a new product, a positioning change, a feature cut. Returns a structured debate, the strongest objections, and a clear recommendation. Use when you want your actual customers in the room before you commit.

Customer Panel of Experts

Put your customers in the room before you spend money or burn trust. This skill assembles a panel of data-grounded buyer personas and runs a real debate on whatever you're deciding — then hands you the decision, the dissent, and what to test next.

It is the flagship of the panel family. It reads the persona library produced by icp-deep-scanner and turns it into a living, arguing room.

When to use it

  • "Should we raise prices 20%?" — and what each segment will actually do.
  • "Here's the launch campaign for {product}. Will it land?"
  • "We're killing {feature} and adding {feature}. Who revolts?"
  • "Pick between positioning A and positioning B."
  • Any high-stakes call where you'd normally guess what customers think.

Step 0 — Get the personas

The panel is only as good as its members. In order of preference:

  1. Use an existing persona library. Look for personas/ and icp-profile.md (output of icp-deep-scanner). Load every persona file and personas/index.md.
  2. Generate one now. If none exists and the user has connected tools, run icp-deep-scanner first (read-only) to build it from real data.
  3. Bootstrap from input. If there's no data and no time, build 3–5 provisional personas from what the user tells you — and label the entire session "PROVISIONAL — not grounded in customer data" at the top and bottom. Never let a guessed panel masquerade as a researched one.

Data & security rules

  • Connecting tools is read-only. Never write to, send from, or modify a connected source. Confirm before any exception.
  • Personas are archetypes. Do not surface real customer names/emails/account IDs in the debate. Quotes must be scrubbed.
  • Secrets stay in env vars / the MCP connection — never printed or stored in output.

Step 1 — Frame the decision

Restate the decision crisply and lock the variables before debating:

  • The decision: one sentence, with the specific option(s) on the table.
  • What changes for the customer: price, workflow, access, expectation.
  • Success metric: what "this went well" means in numbers.
  • Reversibility: can we walk it back, and at what cost?

If the user's ask is vague ("is this a good idea?"), tighten it into a decision with options before proceeding.

Step 2 — Seat the panel

Select 3–6 personas relevant to THIS decision (a pricing decision needs the economic buyer and a price-sensitive segment; a feature cut needs the power users who rely on it). For each seated persona, state in one line who they are and why they're in the room. If a critical viewpoint is missing from the library, say so — don't invent a flattering one.

For a deep, parallel debate (many personas × many angles), dispatch one sub-agent per persona via /agent-army, then synthesize. Otherwise run it inline.

Step 3 — Run the debate

Each persona argues in character, from their real goals, pains, and language — not as a generic critic. Structure:

  1. Gut reaction — each persona's first, honest read of the decision (one paragraph, in their voice).
  2. Cross-examination — personas challenge each other. The economic buyer and the end user often want opposite things; let that tension play out. Surface where one persona's win is another's loss.
  3. The strongest objection — the single most dangerous reaction, stated as that customer would actually say it (and would actually act on — churn, downgrade, public complaint, silence).
  4. What would change their mind — the concession, proof, or framing that flips a NO to a YES.

Keep personas honest: include the ones who will hate it. A panel that all agrees is a panel you rigged.

Step 4 — Synthesize the decision

# Customer Panel — {Decision}
Generated: {timestamp} · Panel: {persona list} · Grounding: {data-backed / PROVISIONAL}

## Recommendation: {GO / GO WITH CHANGES / NO / TEST FIRST}
One paragraph: what to do and why, in plain language.

## Vote by persona
| Persona | Verdict | Why | If it ships anyway, they will… |

## The objections that matter (ranked)
1. {Objection} — who raises it, how likely to act, blast radius, mitigation.

## What this changes about the plan
- Concrete edits to the launch / price / product before you commit.

## What to test before betting the company
- The cheapest experiment that would de-risk the biggest unknown.

## Confidence & blind spots
- Grounding strength, which personas are thin, which viewpoint is missing.

Step 5 — Offer the next move

Offer to: rerun the panel against a revised plan, hand the strongest objection to prospect-panel-simulator to test live messaging, route a pricing decision to pricing-change-strategist, or escalate a full launch to product-launch-war-room.

Guardrails recap

Grounded personas beat invented ones — and provisional panels say so loudly · read-only connections · no real PII in output · include the customers who'll hate it · every verdict ties to a persona's real motivation.