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-papergit 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-paper)<a href="https://agentmods.dev/skills/pangenomeai/academic-skills-food-nutrition/agri-paper"><img src="https://agentmods.dev/badge/skills/pangenomeai/academic-skills-food-nutrition/agri-paper/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-paper"><img src="https://agentmods.dev/badge/skills/pangenomeai/academic-skills-food-nutrition/agri-paper.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 | $0.00931 |
| Opus 5 | $0.00086 | $0.00465 |
| Sonnet 5 | $0.00035 | $0.00186 |
| Haiku 4.5 | $0.00017 | $0.00093 |
Grade A, and why
agri-paper 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 — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agri-Paper — Manuscript System for Agricultural Science
Run the food-paper skill exactly — its 12 subagents (intake,
literature_lead, question_framer, data_curator, statistician,
viz_designer, structure_architect, argument_builder, draft_writer,
polisher, citation_manager, internal_reviewer), all modes (full · plan ·
outline · section · stats · revise · format-convert · polish), and its output
contracts — with the agriculture substitutions in
agri-research/references/agriculture-domain.md.
Read that file first. No new machinery here.
The substitutions
- Persona — a senior agricultural scientist of the specific discipline; name it and write to its standards (domain §2).
- Evidence base —
literature_leadcallsagri-research(notfood-research);citation_managerprefers Tier 1 agriculture + multidisciplinary sources, Q4 avoided (domain §3). - Journal routing — resolve the target journal once via
journal-selectorusing the agriculture coverage map (domain §4); APA 7.0 if 'generic'.
Figures still route through food-figure at the journal spec — it is
domain-neutral (bar/box, dose–response, kinetics, PCA, heatmaps, forest plots all
apply to agricultural data). Revision still follows
food-paper/references/revision-response.md; peer review calls agri-review.
Agricultural reporting (domain §5) — enforce in Methods and Results
- Field trials: site (coordinates), season(s)/years, soil type and classification, cultivar/genotype, plot size, design (RCBD/split-plot/Latin square) and replication, agronomic management, and the statistical model.
- The experimental unit stated explicitly — plot/pen, not plant/animal; subsamples are not replicates (pseudoreplication is the classic reviewer kill).
- G×E and season-to-season variation addressed, or the limitation stated; a single site-year does not support a general recommendation.
- Animal work: ethics approval + ARRIVE; housing, diet composition, unit.
- Soil work: sampling depth/strategy, bulk density, method, equivalent-soil-mass basis where stocks are claimed.
- Units: SI; yield t/ha with moisture basis; nutrient rates kg/ha; mean ± SD/SEM with n and the test.
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 · 57 lines · 173 tokens per session scan A afed5930e38c
agri-paper 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 931 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 2026-08-30.
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