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-competitorsgit 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-competitors)<a href="https://agentmods.dev/skills/zubair-trabzada/ai-restaurant-claude/restaurant-competitors"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-restaurant-claude/restaurant-competitors/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-competitors"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-restaurant-claude/restaurant-competitors.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 | $0.00027 | $0.02896 |
| Opus 5 | $0.00014 | $0.01448 |
| Sonnet 5 | $0.00005 | $0.00579 |
| Haiku 4.5 | $0.00003 | $0.00290 |
Grade A, and why
restaurant-competitors scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 303 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Local Competitor Analysis
You identify a restaurant's top 5 direct local competitors and produce a head-to-head comparison across menu, pricing, reviews, social presence, local SEO position, and overall positioning — surfacing the gaps the subject restaurant can exploit.
DISCLAIMER: AI-generated competitive analysis. Use as strategic input, not a copy-paste blueprint.
When to use
/restaurant competitors <name>— full competitive set analysis- "who are my competitors"
- "how do I beat [competitor name]"
What Makes a "Direct Competitor"
A direct competitor scores high on ALL of:
- Geographic proximity — within 3 miles (or 10 minutes drive)
- Cuisine overlap — same or very similar (Italian vs Italian, not Italian vs French)
- Price tier — within one tier ($ to $$ is OK, $ to $$$ is not)
- Concept type — casual to casual, fine to fine
- Daypart overlap — both serve dinner, both serve brunch, etc.
Indirect competitors (still worth tracking):
- Same cuisine, different city (aspirational competitor)
- Different cuisine, same neighborhood (substitution risk)
- Delivery-only / ghost kitchens (delivery channel risk)
Execution Pipeline
Step 1: Identify Competitive Set
WebSearch("best [cuisine] in [city]")
WebSearch("best [cuisine] in [neighborhood]")
WebSearch("[cuisine] restaurants near [subject address]")
Filter for:
- Within 3 miles
- Same cuisine & price tier
- At least 50 reviews (excludes brand-new / shadow restaurants)
Step 2: Profile Each Competitor
For each of the top 5, capture:
| Field | Detail |
|---|---|
| Name | ... |
| Distance | ... |
| Years open | ... |
| Concept type | ... |
| Price tier | ... |
| Google rating | X.X (Y reviews) |
| Yelp rating | X.X (Y reviews) |
| Most-praised dish | ... |
| Most-common complaint | ... |
| Signature differentiator | ... |
| Website | ... |
| Instagram (handle, followers, last post date) | ... |
| TikTok (handle, followers) | ... |
| Delivery platforms | ... |
| Reservation platforms | ... |
| Catering offered? | Y/N |
| Private events offered? | Y/N |
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 · 303 lines · 27 tokens per session scan A 04059f96cb7a
restaurant-competitors is a skill published in the GitHub repository zubair-trabzada/ai-restaurant-claude (26 stars, last pushed 3mo ago), licensed MIT. It adds 27 tokens to every session and 2,896 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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