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 RiggdAI/uniqent --skill dietary-checkgit clone --depth 1 https://github.com/RiggdAI/uniqentWrote 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/riggdai/uniqent/dietary-check)<a href="https://agentmods.dev/skills/riggdai/uniqent/dietary-check"><img src="https://agentmods.dev/badge/skills/riggdai/uniqent/dietary-check/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/riggdai/uniqent/dietary-check"><img src="https://agentmods.dev/badge/skills/riggdai/uniqent/dietary-check.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 | $0.00023 | $0.00270 |
| Opus 5.5 | $0.00009 | $0.00108 |
| Sonnet 5.5 | $0.00005 | $0.00054 |
| Haiku 4.5 | $0.00002 | $0.00027 |
Grade A, and why
dietary-check 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 1mo 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.
What it actually says
Dietary Check
When asked to check a menu for dietary compliance:
- Scan every dish for the Big Four allergens: nuts (all tree nuts + peanuts), gluten (wheat, barley, rye, oats), dairy, and shellfish. Flag any unlabelled allergen as a blocker.
- Check vegan coverage. A vegan dish must contain no meat, fish, dairy, eggs, or honey. Flag every service period that lacks at least one vegan option.
- Check halal coverage. Flag any service period that lacks at least one clearly halal option (no pork, no alcohol in cooking, halal-certified protein).
- Surface hidden risks. Look for cross-contamination notes (shared fryers, prep surfaces) and flag them separately from declared allergens.
- Report findings as a table: dish | allergens present | vegan? | halal? | issues. Use ✓ / ✗ for boolean columns so the operator can scan at a glance.
- Propose fixes. For each flagged issue suggest the minimal change that resolves it — a substitution, an added label, or a preparation note.
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.
- 1mo ago First seen · 22 lines · 23 tokens per session scan A 3c09600d0135
dietary-check is a skill published in the GitHub repository RiggdAI/uniqent (15 stars, last pushed 10d ago), licensed Apache-2.0. It adds 23 tokens to every session and 270 once invoked, about $0.0001 per session on Opus 5.5. 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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