customer-panel-of-experts — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited customer-panel-of-experts (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
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.
The panel is only as good as its members. In order of preference:
personas/ and icp-profile.md (output of icp-deep-scanner). Load every persona file and personas/index.md.icp-deep-scanner first (read-only) to build it from real data.Restate the decision crisply and lock the variables before debating:
If the user's ask is vague ("is this a good idea?"), tighten it into a decision with options before proceeding.
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.
Each persona argues in character, from their real goals, pains, and language — not as a generic critic. Structure:
Keep personas honest: include the ones who will hate it. A panel that all agrees is a panel you rigged.
# 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.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.
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.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.