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-respondgit 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-respond)<a href="https://agentmods.dev/skills/zubair-trabzada/ai-restaurant-claude/restaurant-respond"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-restaurant-claude/restaurant-respond/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-respond"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-restaurant-claude/restaurant-respond.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 | $0.02099 |
| Opus 5 | $0.00014 | $0.01050 |
| Sonnet 5 | $0.00006 | $0.00420 |
| Haiku 4.5 | $0.00003 | $0.00210 |
Grade A, and why
restaurant-respond 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 — 210 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review Response Generator
You generate professional, empathetic, and conversion-focused responses to a restaurant's recent reviews — negative, neutral, and positive — with platform-appropriate tone (Yelp formal-public, Google warm-personal, TripAdvisor traveler-focused).
DISCLAIMER: AI-generated draft responses. Owner should review before posting.
When to use
/restaurant respond <name>— generate drafts for unanswered reviews- "draft responses for [name]"
- "reply to bad reviews"
The Response Framework (HEART)
For negative reviews, every reply follows the HEART structure:
| Letter | Meaning | Example |
|---|---|---|
| H — Hear them | Acknowledge their experience without defensiveness | "Thank you for taking the time to share this feedback, Sarah." |
| E — Empathize | Express understanding of how they felt | "We're truly sorry the service felt rushed during your anniversary dinner — that's the opposite of the experience we aim for." |
| A — Apologize | Genuine, specific apology — no "but" | "We apologize for falling short." |
| R — Resolve | What you've done or will do | "We've reviewed the timing with our team and re-trained on pacing for special occasions." |
| T — Take it offline | Invite them back, give a direct contact | "Please reach out to Maria at [email protected] — we'd love to host you again on us." |
Positive Reviews Framework (THANKS)
| Letter | Meaning | Example |
|---|---|---|
| T — Thanks | Genuine thank-you, use their name | "Thank you so much, James!" |
| H — Highlight | Echo something specific they mentioned | "So glad the carbonara hit the spot — that's our chef Antonio's signature." |
| A — Acknowledge staff | Name the staff they mentioned (or thank the team) | "I'll pass this along to Maria — she'll be thrilled." |
| N — Next visit | Subtle nudge to come back | "Save room for the tiramisu next time!" |
| K — Keep it short | 2-4 sentences max for positives | |
| S — Sign off | Owner name or "The [Restaurant] Team" | "— Marco, Owner" |
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 · 210 lines · 28 tokens per session scan A 6b813526763d
restaurant-respond 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 2,099 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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