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 prospect-panel-simulatorgit 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/prospect-panel-simulator)<a href="https://agentmods.dev/skills/onewave-ai/claude-skills/prospect-panel-simulator"><img src="https://agentmods.dev/badge/skills/onewave-ai/claude-skills/prospect-panel-simulator/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/prospect-panel-simulator"><img src="https://agentmods.dev/badge/skills/onewave-ai/claude-skills/prospect-panel-simulator.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 | $0.00082 | $0.01090 |
| Opus 5 | $0.00041 | $0.00545 |
| Sonnet 5 | $0.00016 | $0.00218 |
| Haiku 4.5 | $0.00008 | $0.00109 |
Grade A, and why
prospect-panel-simulator 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 9d 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 — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prospect Panel Simulator
Before you send the email, run the deck, or publish the pricing page — run it past the people who'd receive it. This skill simulates a panel of your actual prospects and reacts the way the market will: skeptical, busy, half-reading, comparing you to three other options.
Where customer-panel-of-experts debates a business decision with existing customers, this skill stress-tests a sales or marketing artifact against people who don't know you yet and don't owe you a reply.
What it pressure-tests
- Cold emails and full sequences (does it get a reply, or a delete?)
- Pitch decks and one-pagers (where do they check out?)
- Landing pages and pricing pages (what makes them bounce?)
- Demo scripts and discovery-call openers
- Proposals and SOWs (what gets pushed back on?)
Step 0 — Assemble the prospect panel
- Preferred: load personas from
icp-deep-scanneroutput (personas/,icp-profile.md) and seat the buying committee — economic buyer, champion, blocker, and end user — since a cold artifact hits all of them differently. - If no library exists and tools are connected, run
icp-deep-scanner(read-only) to ground the panel in real won/lost-deal data and real objection language. - Bootstrap from the user's description only as a last resort, labeled PROVISIONAL.
Critically, model cold-state prospects: they have low context, low trust, and an alternative they already use. A simulated prospect who reads charitably is useless.
Security
Read-only connections. No sending, no writing to any tool. No real prospect names/emails in output — these are archetypes. Secrets stay in env vars.
Step 1 — Take in the artifact
Read exactly what will go out (paste, file, or URL via WebFetch). Note the channel and the moment: a cold email at 7am from an unknown sender is judged differently than a pricing page reached after a demo. Confirm: who is this for, what's the one action it's asking for, and what does the prospect see right before this?
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.
- 9d ago First seen · 78 lines · 82 tokens per session scan A c107b30d09ae
prospect-panel-simulator is a skill published in the GitHub repository OneWave-AI/claude-skills (291 stars, last pushed 1mo ago), licensed MIT. It adds 82 tokens to every session and 1,090 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 2026-09-03.
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