Customer panel of experts
172 production-ready Claude Code skills for sales, marketing, design, engineering, and AI agent architecture. Built and maintained by OneWave AI.
npx -y skills add OneWave-AI/claude-skills --skill customer-panel-of-expertsAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
What its author says it does
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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.
SKILL.md
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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:
- Use an existing persona library. Look for
personas/andicp-profile.md(output oficp-deep-scanner). Load every persona file andpersonas/index.md. - Generate one now. If none exists and the user has connected tools, run
icp-deep-scannerfirst (read-only) to build it from real data. - 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:
- Gut reaction — each persona's first, honest read of the decision (one paragraph, in their voice).
- 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.
- 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).
- 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.
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.
Gives 0 of the 12 instructions most product growth skills give in ~1.2k tokens
Counted across 728 of the 1,010 authors here whose files we hold, read 2026-08-07
- Read product marketing context before asking questionsin 24 of 728, across 18 files
- Define the ideal customer profilein 21 of 728, across 3 files
- Document a rollback plan before deploymentin 21 of 728, across 12 files
- Analyze the codebase to understand the productin 19 of 728, across 1 file
- Ask clarifying questions about the value propositionin 19 of 728, across 1 file
- Search for companies matching the criteriain 19 of 728, across 1 file
- Look for signals of immediate needin 19 of 728, across 1 file
- Assign a fit score from one to tenin 19 of 728, across 1 file
- Identify the target decision-maker rolein 19 of 728, across 1 file
- Suggest a personalized contact strategyin 19 of 728, across 1 file
- Provide conversation starters for outreachin 19 of 728, across 1 file
- Format results in a scannable markdown templatein 19 of 728, across 1 file
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.