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.
What this skill does
# 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
```markdown
# 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.
Related in Sales & CRM
process-mapper
IncludedUse when a BizOps lead, COO, or process-improvement owner needs to document an end-to-end business process (procurement, employee onboarding, incident handoff, customer-onboarding, claims adjudication) in BPMN-style notation, measure cycle times by stage, surface where work spends most of its time waiting vs. being worked, and quantify the gap between processing time and total elapsed time. Pairs Lean / Six Sigma / Theory-of-Constraints canon with deterministic stdlib-only Python tools to produce a process map, a ranked bottleneck list (with severity + root-cause hypothesis), and a cycle-time analysis (P50, P90, value-add ratio, Little's-Law throughput). Distinct from sales-pipeline, system-reliability (SLO), and strategic-OKR work — this is tactical process documentation for internal operations.
payment-integration
IncludedIntegrate payments with SePay (VietQR), Polar, Stripe, Paddle (MoR subscriptions), Creem.io (licensing). Checkout, webhooks, subscriptions, QR codes, multi-provider orders.
customer-success-manager
IncludedMonitors customer health, predicts churn risk, and identifies expansion opportunities using weighted scoring models for SaaS customer success
sales-engineer
IncludedAnalyzes RFP/RFI responses for coverage gaps, builds competitive feature comparison matrices, and plans proof-of-concept (POC) engagements for pre-sales engineering. Use when responding to RFPs, bids, or proposal requests; comparing product features against competitors; planning or scoring a customer POC or sales demo; preparing a technical proposal; or performing win/loss competitor analysis. Handles tasks described as 'RFP response', 'bid response', 'proposal response', 'competitor comparison', 'feature matrix', 'POC planning', 'sales demo prep', or 'pre-sales engineering'.
customer-success-manager
IncludedMonitors customer health, predicts churn risk, and identifies expansion opportunities using weighted scoring models for SaaS customer success
sales-engineer
IncludedAnalyzes RFP/RFI responses for coverage gaps, builds competitive feature comparison matrices, and plans proof-of-concept (POC) engagements for pre-sales engineering. Use when responding to RFPs, bids, or proposal requests; comparing product features against competitors; planning or scoring a customer POC or sales demo; preparing a technical proposal; or performing win/loss competitor analysis. Handles tasks described as 'RFP response', 'bid response', 'proposal response', 'competitor comparison', 'feature matrix', 'POC planning', 'sales demo prep', or 'pre-sales engineering'.