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-researchgit 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-research)<a href="https://agentmods.dev/skills/pangenomeai/academic-skills-food-nutrition/agri-research"><img src="https://agentmods.dev/badge/skills/pangenomeai/academic-skills-food-nutrition/agri-research/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-research"><img src="https://agentmods.dev/badge/skills/pangenomeai/academic-skills-food-nutrition/agri-research.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 | $0.00169 | $0.00957 |
| Opus 5 | $0.00084 | $0.00478 |
| Sonnet 5 | $0.00034 | $0.00191 |
| Haiku 4.5 | $0.00017 | $0.00096 |
Grade A, and why
agri-research 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 — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agri-Research — Evidence Synthesis for Agricultural Science
Run the food-research skill exactly — its streams, subagents
(search_strategist, source_scout, screener_appraiser, journal_ranker,
synthesis, writer, reviewer, and the full systematic_reviewer PRISMA/OHAT
pipeline), gates, and output contracts — with the agriculture substitutions in
references/agriculture-domain.md. Read that
file first. This skill adds no new machinery; it changes who is working, on what
evidence, for which journal.
The substitutions
- Persona — a senior agricultural scientist of the specific discipline (agronomy · soil science · horticulture · dairy & animal science · agricultural engineering · agricultural economics & policy · agriculture multidisciplinary). Name the discipline and apply its standards (domain §2).
- Evidence base — agriculture + multidisciplinary literature, ranked by
journal_ranker: Tier 1 = Q1/Q2 of the seven agriculture categories (journals/_coverage_agriculture.md, 230 journals) + Nature/Science/Cell/PNAS + Q1/Q2 of adjacent disciplines; Tier 2 = Q3 for gaps only; Q4 avoided. Authoritative non-journal sources (FAO, USDA, CGIAR, EFSA, extension services) count as evidence with a source and date (domain §3). - Journal routing — via
journal-selector, using the agriculture coverage map (domain §4).
Streams (as food-research)
- quick brief — fast orientation; Tier 1 only.
- full review — the default: four-layer search → two-phase screening → synthesis
→ manuscript →
reviewerloop → Word.docx. - deep research — calls
agri-deep-research(notfood-deep-research). - systematic — full PRISMA + OHAT pipeline; inclusion by pre-specified eligibility, never journal ranking.
Agricultural rigour
Apply domain §5 throughout — field-trial reporting (site, season/years, soil, cultivar, design, replication), the experimental unit (plot/pen, not plant/animal — pseudoreplication is the classic error), G×E and season-to-season variation, ARRIVE for animal work, and no extrapolation from pot to field or region to region.
What ships with it
1 file 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.
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 · 60 lines · 169 tokens per session scan A 202698dbd06c
agri-research is a skill published in the GitHub repository PangenomeAI/academic-skills-food-nutrition (31 stars, last pushed 12d ago), licensed MIT. It adds 169 tokens to every session and 957 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…