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 PangenomeAI/academic-skills-food-nutrition --skill food-reviewgit clone --depth 1 https://github.com/PangenomeAI/academic-skills-food-nutritionWrote 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/pangenomeai/academic-skills-food-nutrition/food-review)<a href="https://agentmods.dev/skills/pangenomeai/academic-skills-food-nutrition/food-review"><img src="https://agentmods.dev/badge/skills/pangenomeai/academic-skills-food-nutrition/food-review/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/pangenomeai/academic-skills-food-nutrition/food-review"><img src="https://agentmods.dev/badge/skills/pangenomeai/academic-skills-food-nutrition/food-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00173 | $0.02207 |
| Opus 5 | $0.00086 | $0.01104 |
| Sonnet 5 | $0.00035 | $0.00441 |
| Haiku 4.5 | $0.00017 | $0.00221 |
Grade A, and why
food-review 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 8d 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 — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Food-Review — Multi-Reviewer Peer Review for Food & Nutrition Manuscripts
Give the author the review a good food-science journal would return, from a panel rather than a single voice. Original work; architecture informed by open community peer-review skills (see the repo README Acknowledgements).
Modes
- full (default) — the whole panel: three domain reviewers + devil's advocate + format check, synthesized by the coordinator into an editorial decision.
- quick — coordinator + one blended reviewer pass; a fast readiness verdict.
- methodology — deep dive by
reviewer_methodologyonly. - re-review — re-assess a revised manuscript against the prior reports and the author's response, verifying each point was addressed.
Panel (dispatch via the Agent tool; reviewers run in parallel)
review_coordinator(editor-in-chief) — sets the target journal + scope, dispatchesknowledge_builder, then the reviewers, synthesizes their reports, resolves disagreement, and issues the decision.knowledge_builder— runs first: reads the manuscript's cited sources (Pathway A) and the field's key literature (Pathway B) into a shared knowledge base so the panel judges the science from knowledge, not impression.reviewer_methodology— design, statistics, reproducibility, validation.reviewer_domain— novelty, significance, scope fit, domain correctness (food/nutrition science).reviewer_integrity— data & citation integrity, food-safety/ethics, reporting completeness.devils_advocate— adversarial challenge to the paper's central claim.format_checker— formatting & reference-style compliance vs the target journal.
Ground the panel first — the knowledge base
Reviewers must understand the topic and its background before they critique it.
knowledge_builder runs before the reviewers and builds one knowledge base from:
- A — the manuscript's own citations: retrieve and read the full cited articles, extract what each actually shows, and audit whether it supports the claim it is attached to.
- B — the field's key literature: extract the manuscript's keywords and
research disciplines, search the literature for the field's key work
(may use the
food-researchfull reviewbranch for discovery/screening — but knowledge extraction only, no literature-review article).
What ships with it
13 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.
- agents/devils_advocate.md 1.6 KB
- agents/format_checker.md 1.7 KB
- agents/knowledge_builder.md 8.7 KB
- agents/review_coordinator.md 4.0 KB
- agents/reviewer_domain.md 2.0 KB
- agents/reviewer_integrity.md 1.9 KB
- agents/reviewer_methodology.md 2.0 KB
- references/editorial-decisions.md 1.5 KB
- references/ethics-integrity-checklist.md 1.7 KB
- references/quality-rubrics.md 1.6 KB
- references/report-format.md 7.2 KB
- references/review-criteria.md 3.0 KB
- references/word-review-comments.md 4.3 KB
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.
- 8d ago First seen · 135 lines · 173 tokens per session scan A b2fb1314e693
food-review is a skill published in the GitHub repository PangenomeAI/academic-skills-food-nutrition (31 stars, last pushed 12d ago), licensed MIT. It adds 173 tokens to every session and 2,207 once invoked, about $0.0009 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 2026-09-04.
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