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 zubair-trabzada/ai-restaurant-claude --skill restaurant-emailgit clone --depth 1 https://github.com/zubair-trabzada/ai-restaurant-claudeWrote 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/zubair-trabzada/ai-restaurant-claude/restaurant-email)<a href="https://agentmods.dev/skills/zubair-trabzada/ai-restaurant-claude/restaurant-email"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-restaurant-claude/restaurant-email/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/zubair-trabzada/ai-restaurant-claude/restaurant-email"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-restaurant-claude/restaurant-email.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 | $0.00028 | $0.03386 |
| Opus 5 | $0.00014 | $0.01693 |
| Sonnet 5 | $0.00006 | $0.00677 |
| Haiku 4.5 | $0.00003 | $0.00339 |
Grade A, and why
restaurant-email 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 — 504 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Email & SMS Sequence Library
You generate complete email + SMS sequences for a restaurant's full lifecycle marketing — welcome series, birthday flow, win-back campaigns, abandoned online order, event invitations, and loyalty program nudges.
DISCLAIMER: AI-generated copy. Verify TCPA / CAN-SPAM compliance for SMS and email. Get express opt-in.
When to use
/restaurant email <name>— full sequence library- "loyalty emails for [name]"
- "birthday email flow"
Why Email + SMS Matters
| Channel | Open Rate | Click Rate | Revenue per send |
|---|---|---|---|
| Email (restaurant industry) | 20-25% | 2-3% | $0.15-$0.40 |
| SMS (restaurant industry) | 95-98% | 15-30% | $0.50-$2.00 |
| Push notification | 30-50% | 5-15% | $0.20-$0.60 |
A 5,000-person email list with one weekly send generates $750-$2,000/month for the restaurant. An SMS list of the same size = $2,500-$10,000/month.
The bottleneck: getting opt-ins. Every receipt, every reservation confirmation, every birthday card is an opt-in opportunity.
The 7 Core Sequences
| Sequence | Trigger | # of touches | Goal |
|---|---|---|---|
| 1. Welcome Series | New email signup | 3 emails over 2 weeks | First visit / order |
| 2. Birthday Flow | Birthday in next 30 days | 3 touches (14d, 7d, day-of) | Birthday booking |
| 3. Win-Back | No order/visit in 60 days | 3 emails over 21 days | Re-engagement |
| 4. Abandoned Order | Started online order, didn't complete | 1 email + 1 SMS within 24hrs | Recover the order |
| 5. Event Invite | Live music, wine dinner, holiday | 3 emails leading up | RSVPs |
| 6. Post-Visit Thanks | Within 24hrs of visit | 1 email (low-pressure) | Review request + repeat visit |
| 7. Loyalty Program | Loyalty member status changes | Per milestone | Tier upgrade / redemption |
Subject Line Frameworks
| Framework | Open Rate |
|---|---|
| Curiosity ("We added something to the menu...") | 28-35% |
| Personalization (uses name) | 26-32% |
| Offer ("Free dessert tonight only") | 25-30% |
| Urgency / FOMO ("3 tables left this Saturday") | 30-40% |
| Question ("What's your go-to order?") | 22-28% |
| Local ("Best brunch in [neighborhood] — proof inside") | 25-30% |
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 · 504 lines · 28 tokens per session scan A 909e340a0b67
restaurant-email is a skill published in the GitHub repository zubair-trabzada/ai-restaurant-claude (26 stars, last pushed 3mo ago), licensed MIT. It adds 28 tokens to every session and 3,386 once invoked, about $0.0001 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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