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 agri-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/agri-review)<a href="https://agentmods.dev/skills/pangenomeai/academic-skills-food-nutrition/agri-review"><img src="https://agentmods.dev/badge/skills/pangenomeai/academic-skills-food-nutrition/agri-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/agri-review"><img src="https://agentmods.dev/badge/skills/pangenomeai/academic-skills-food-nutrition/agri-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 | $0.00155 | $0.01053 |
| Opus 5 | $0.00077 | $0.00526 |
| Sonnet 5 | $0.00031 | $0.00211 |
| Haiku 4.5 | $0.00015 | $0.00105 |
Grade A, and why
agri-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 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 — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agri-Review — Peer-Review Panel for Agricultural Science
Run the food-review skill exactly — its panel (review_coordinator,
knowledge_builder, reviewer_methodology, reviewer_domain,
reviewer_integrity, devils_advocate, format_checker), its modes (full ·
quick · methodology · re-review), and its report format — with the agriculture
substitutions in
agri-research/references/agriculture-domain.md.
Read that file first. No new machinery here.
The substitutions
- Persona — every reviewer is a senior agricultural scientist of the specific discipline (agronomy · soil science · horticulture · dairy & animal science · agricultural engineering · agricultural economics & policy). Name the discipline; a soil-carbon paper gets a soil scientist's standards (domain §2).
- Knowledge base —
knowledge_builderruns first, unchanged in method: Pathway A reads the manuscript's cited sources in full and audits whether each supports its claim; Pathway B reads the field's key literature, ranked as Tier 1 Q1/Q2 agriculture (journals/_coverage_agriculture.md)- Nature/Science/Cell/PNAS + Q1/Q2 adjacent disciplines, Q4 avoided. Pathway B
may use
agri-research'sfull reviewbranch for discovery/screening — knowledge extraction only, no literature-review article (domain §3). Insideagri-pipelinewith a Stage-1 evidence base: reuse it and top up withagri-researchquick brief key reviews, exactly asfood-reviewdoes.
- Nature/Science/Cell/PNAS + Q1/Q2 adjacent disciplines, Q4 avoided. Pathway B
may use
- Journal routing —
format_checkeraudits against the journal resolved once byjournal-selectorfrom the agriculture coverage map (domain §4); APA 7.0 default.
What an agricultural reviewer checks first (domain §5)
- The experimental unit / pseudoreplication — subsamples counted as replicates, plants scored as units within one plot, animals within one pen. The single most common fatal flaw; raise it as Critical when present.
- Field-trial reporting — site, season(s)/years, soil classification, cultivar, plot size, design, replication, management, statistical model.
- Generalisation — one site-year, one region, or a pot/glasshouse study presented as a general agronomic recommendation; missing G×E.
- Animal work — ethics approval and ARRIVE; diet composition; unit (pen vs animal).
- Soil work — sampling depth, bulk density, equivalent-soil-mass basis for stock claims.
- Economics/policy — identification strategy; causal language unearned by design.
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 · 65 lines · 155 tokens per session scan A cd716b554286
agri-review is a skill published in the GitHub repository PangenomeAI/academic-skills-food-nutrition (31 stars, last pushed 12d ago), licensed MIT. It adds 155 tokens to every session and 1,053 once invoked, about $0.0008 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.
Other skills, from other repositories
paper-framework-figure-studio-pro
Human-in-the-loop research-paper framework figure workflow from S0-PAPER-FOUNDATION through terminal S5-CANDIDATE-IMAGE. Use for paper-grounded architecture, pipeline, method overview, agent workflow, system/data-flow, and mechanism figures with generated raster first-round/formal candidates, reviewer-first-glance…
autofigure
Generate clean, EDITABLE vector figures (SVG + exact-size PDF) for research papers — method / architecture / pipeline / system-overview diagrams — with AutoFigure-Edit. Use whenever the user wants to create or vectorize a paper figure from a text description OR from a draft/screenshot/draw.io image: it generates a…
paper-polish-pipeline
Staged, diagnosis-driven academic paper polishing pipeline (bilingual 中文/English). Use to revise a paper from rough draft to final submission across ordered stages: diagnose first, then optimize section by section, derive abstract/contributions from the revised body, polish the language to reduce AI-sounding phrasing…
nature-paper2ppt
Build a complete but efficient Nature-style Chinese PPTX presentation from a scientific paper, preprint, PDF, article text, abstract, figure legends, or reading notes. Use this skill whenever the user asks to make slides/PPT/PPTX for journal club, group meeting, paper sharing, thesis seminar, lab meeting, department…
nature-reviewer
A peer-review checklist that assesses a research paper from a referee’s point of view, including its novelty, importance, and technical soundness.
nature-academic-search
Multi-source literature search, citation verification, MeSH search strategy, citation file management (.nbib/.ris/.bib conversion), and reference management (BibTeX, related articles, ID conversion) via MCP tools (PubMed, CrossRef, arXiv, Scopus, ScienceDirect). Use when the user needs coordinated multi-step…