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-deep-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-deep-research)<a href="https://agentmods.dev/skills/pangenomeai/academic-skills-food-nutrition/agri-deep-research"><img src="https://agentmods.dev/badge/skills/pangenomeai/academic-skills-food-nutrition/agri-deep-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-deep-research"><img src="https://agentmods.dev/badge/skills/pangenomeai/academic-skills-food-nutrition/agri-deep-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 | $0.00154 | $0.00888 |
| Opus 5 | $0.00077 | $0.00444 |
| Sonnet 5 | $0.00031 | $0.00178 |
| Haiku 4.5 | $0.00015 | $0.00089 |
Grade A, and why
agri-deep-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 — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agri-Deep-Research — Source-Validated Reviews for Agricultural Science
Run the food-deep-research skill exactly — its 12-subagent team
(research_scope, research_architect, investigator, source_screener,
source_verifier, bibliography, claim_verifier, synthesizer, critic,
compiler, editor, ethics_reviewer), both loops (evidence loop and
compile↔review loop), and its source discipline — 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 apply its standards (domain §2).
research_architectdesigns the method to that discipline's conventions. - Evidence base —
source_screenerranks agriculture + multidisciplinary literature: Tier 1 = Q1/Q2 of the seven agriculture categories (journals/_coverage_agriculture.md) + Nature/Science/Cell/PNAS + Q1/Q2 adjacent disciplines; Tier 2 = Q3 for gaps; Q4 avoided. FAO/USDA/CGIAR/EFSA and extension sources are evidence with a source and date (domain §3). - Journal routing —
bibliographyandcompilerformat viajournal-selectorusing the agriculture coverage map (domain §4); APA 7.0 by default.
Source discipline (inherited, non-negotiable)
Investigation and claim-checking operate only on validated sources — those that
passed source_screener (ranking) and source_verifier (existence, venue
legitimacy, retraction, predatory check). Every claim carries a source and locator;
inference is labelled as inference; [EVIDENCE GAP] rather than filling from memory.
Agricultural rigour
Apply domain §5 — the critic should attack the usual agricultural weak points:
single site-year generalised to a recommendation, pseudoreplication (subsamples
treated as replicates), pot-to-field extrapolation, missing G×E, and causal language
unearned by the 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 · 56 lines · 154 tokens per session scan A c7ff086497c9
agri-deep-research is a skill published in the GitHub repository PangenomeAI/academic-skills-food-nutrition (31 stars, last pushed 12d ago), licensed MIT. It adds 154 tokens to every session and 888 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.
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