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.
npx skills add OneWave-AI/claude-skills --skill customer-panel-of-expertsgit clone --depth 1 https://github.com/OneWave-AI/claude-skillsWrote 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.
[](https://agentmods.dev/skills/onewave-ai/claude-skills/customer-panel-of-experts)<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.
<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>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once 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 |
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.
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:
- 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?
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.
- 12d ago First seen · 90 lines · 86 tokens per session scan A 9975cc58883c
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.
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