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 tough-tongue/demo-prep-skills --skill demo-prepgit clone --depth 1 https://github.com/tough-tongue/demo-prep-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/tough-tongue/demo-prep-skills/demo-prep)<a href="https://agentmods.dev/skills/tough-tongue/demo-prep-skills/demo-prep"><img src="https://agentmods.dev/badge/skills/tough-tongue/demo-prep-skills/demo-prep/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/tough-tongue/demo-prep-skills/demo-prep"><img src="https://agentmods.dev/badge/skills/tough-tongue/demo-prep-skills/demo-prep.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00063 | $0.02074 |
| Opus 5 | $0.00032 | $0.01037 |
| Sonnet 5 | $0.00013 | $0.00415 |
| Haiku 4.5 | $0.00006 | $0.00207 |
Grade A, and why
demo-prep 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 — 232 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Demo Preparation
Generate a complete preparation package for an upcoming sales meeting. This is the flagship skill — it orchestrates research, generates talking points, predicts objections, and produces ready-to-use emails, all customized to your product and the specific prospect.
When to Use
- User has a demo, discovery call, or follow-up meeting coming up
- User says "prep me for a demo with [name] at [company]"
- User provides meeting details (company, contact, date)
- User needs a structured game plan before a sales conversation
Required Inputs
Ask the user for:
Company: [Name or website URL]
Contact: [Name and title]
Meeting Type: [Discovery call / Product demo / Follow-up / Pilot discussion]
Any Context: [How did this meeting come about? Inbound? Cold outreach? Referral?]
Optional but helpful:
- Specific topics they want to cover
- Known pain points or requirements
- Whether others are joining the call
Process
1. Load Your Context
Read company-profile.md from the workspace root. This contains the user's company info, product description, ICP, differentiators, objections, and background.
If it doesn't exist, tell the user to run the setup-company skill first.
2. Research the Prospect
Use the research-prospect skill process to gather:
- Company intel (what they do, size, sales model, recent news)
- Contact intel (role, background, tenure, recent activity)
- Personalization hooks (shared connections, schools, companies)
- ICP fit assessment
Visit the company's website and look up the contact on LinkedIn using browser tools. Do not fabricate information.
3. Generate the Prep Package
Produce all 8 outputs below, customized to the prospect and the user's product.
Output Structure
Save the output as prep/[company]-[contact]-[date].md.
OUTPUT 1: Meeting Brief
A one-page summary the user can glance at 5 minutes before the call.
# Demo Prep: [Company] — [Contact Name]
**Date:** [Meeting date]
**Type:** [Discovery / Demo / Follow-up]
**Duration:** [Estimated]
## Quick Summary
- **Company:** [What they do, in one line]
- **Contact:** [Name, Title — tenure, one notable background fact]
- **Why they're talking to you:** [Inbound? Referral? Cold?]
- **Strongest hook:** [Your best personalization angle]
- **ICP fit:** [Strong / Medium / Weak]
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 232 lines · 63 tokens per session scan A 4948d18d9095
demo-prep is a skill published in the GitHub repository tough-tongue/demo-prep-skills (5 stars, last pushed 6mo ago), licensed MIT. It adds 63 tokens to every session and 2,074 once invoked, about $0.0003 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 2026-08-31.
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