customer-panel-of-experts

customer-panel-of-experts is a skill for Claude Code, Codex from OneWave-AI/claude-skills. It costs 86 tokens per session (1,258 once invoked), scanned A, original, MIT.

A customer-feedback panel built from buyer profiles gathered from connected tools or an existing persona library. It has those profiles debate a business decision and returns objections, a recommendation, and ideas to test.

In plain words
What is it for?
Use it to assess launches, price increases, product changes, feature removals, and alternative positioning before making a high-stakes decision.
Why use it?
It replaces guesswork about customer reactions with a structured comparison of different buyer viewpoints. This helps expose who may support, resist, or misunderstand a proposed change.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: model in frontmatter; mentions subagents.

Good fit Use it to assess launches, price increases, product changes, feature removals, and alternative positioning before making a high-stakes decision.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/onewave-ai/claude-skills/customer-panel-of-experts
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add OneWave-AI/claude-skills --skill customer-panel-of-experts
Clone the repo
git clone --depth 1 https://github.com/OneWave-AI/claude-skills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for customer-panel-of-experts

README.md
[![agentmods](https://agentmods.dev/badge/skills/onewave-ai/claude-skills/customer-panel-of-experts/github.svg)](https://agentmods.dev/skills/onewave-ai/claude-skills/customer-panel-of-experts)
Your own site
<a href="https://agentmods.dev/skills/onewave-ai/claude-skills/customer-panel-of-experts"><img src="https://agentmods.dev/badge/skills/onewave-ai/claude-skills/customer-panel-of-experts/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for customer-panel-of-experts

Your own site · 80×15
<a href="https://agentmods.dev/skills/onewave-ai/claude-skills/customer-panel-of-experts"><img src="https://agentmods.dev/badge/skills/onewave-ai/claude-skills/customer-panel-of-experts.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 86 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,258 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00086 $0.01258
Opus 5 $0.00043 $0.00629
Sonnet 5 $0.00017 $0.00252
Haiku 4.5 $0.00009 $0.00126

Measured 12d ago against content hash 9975cc58883c, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

customer-panel-of-experts scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

customer-panel-of-experts/SKILL.md · 90 lines

How it starts

The opening of the file, as written. The whole thing — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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?

Read the full file on GitHub · 90 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 12d ago First seen · 90 lines · 86 tokens per session scan A 9975cc58883c

Subscribe to this mod's changes

customer-panel-of-experts is a skill published in the GitHub repository OneWave-AI/claude-skills (291 stars, last pushed 1mo ago), licensed MIT. It adds 86 tokens to every session and 1,258 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other skills, from other repositories

analytics-strategy

Design measurement frameworks including event taxonomy, KPI hierarchy, dashboard architecture, attribution models, and analytics implementation strategy. Use this skill whenever the user wants to plan analytics, design dashboards, build event taxonomies, define KPIs, set up tracking, or audit existing measurement.…

rampstackco/claude-skills · 126 tokens

content-strategy

Develop a content strategy covering editorial positioning, content pillars, formats, calendar, governance, and topical authority planning. Use this skill whenever the user wants to plan a content program, define content pillars, build an editorial calendar, structure topic clusters, set up content governance, or align…

rampstackco/claude-skills · 120 tokens

incident-response

Manage active production incidents through detection, triage, mitigation, communication, and resolution with structured roles and decision-making. Use this skill whenever the user has an active incident, a production issue, a service outage, a security incident, or needs to plan incident response procedures. Triggers…

rampstackco/claude-skills · 117 tokens

stakeholder-communication

Communicate effectively with stakeholders across functions and seniority levels. Use this skill when writing status updates, preparing executive reviews, sharing technical decisions with non-technical audiences, managing up, communicating bad news, or designing the communication cadence for a project. Triggers on…

rampstackco/claude-skills · 103 tokens

review-work

Post-implementation gate review: run manual QA on the real surface yourself, then launch ONE gate reviewer (never a panel) to audit goal, constraints, code quality, security, missed context, and QA evidence. Use before a PR handoff or when the user explicitly asks to review completed work.

code-yeongyu/oh-my-openagent · 63 tokens

agb-begriff-vorformuliert-305

Für AGB Begriff Vorformuliert 305: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Prüfprodukt mit Risiko und nächstem Schritt. Fachgebiet: AGB-Recht-Prüfer. Route: agb-begriff-vorformuliert-305.

Klotzkette/claude-fuer-deutsches-recht · 68 tokens