Agri deep research

Skill PangenomeAI/academic-skills-food-nutrition/agri-deep-research

Open, MIT-licensed food & nutrition science research skills for Claude Code, Codex, and MiniMax Agent — multi-agent literature/systematic review, journal-aware writing, peer review, and figures, plus author-guideline skills for 150+ journals. Initiated by the Food Science Group, University of Melbourne.

Install

npx -y skills add PangenomeAI/academic-skills-food-nutrition --skill agri-deep-research

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

25 days oldThe repository was created 25 days ago. New is not bad, but a brand new repository carrying a familiar-sounding name is the shape a typosquat arrives in, and there has been no time for anyone else to find a problem with it.
17 stars17 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Deep research producing a fully written, source-validated literature review on an agricultural question, as a senior agricultural scientist of the relevant discipline: scope, design the method, discover and screen by journal ranking, validate every source, extract and verify evidence, synthesize, stress-test, then write and format the review through an editorial and integrity loop. Same 12-subagent machinery as food-deep-research, grounded in agriculture and multidisciplinary literature (Q1/Q2 preferred, Q4 avoided). Use standalone for an agricultural deep dive, or as the engine called by agri-research. Triggers: deep research agriculture, investigate this agronomy question, agricultural literature review, state of the evidence in soil science, deep dive crop research.

SKILL.md

3.7 KB, as published. Nobody here has run it

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_architect designs the method to that discipline's conventions.
Evidence base — source_screener ranks 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 — bibliography and compiler format via journal-selector using 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.

Inherited unchanged (not optional)

Four-gate citation verification (scripts/verify_citations.py), privacy scan, academic style + AI-tell removal (food-paper/references/writing-style.md with human-writing.md), and the mandatory AI-use disclosure. Also the full-text-access first move — food-deep-research's highlighted, one-time request for the user's EndNote .Data folder / reference PDFs, and full-text extraction via the ladder before the evidence loop (food-research/references/full-text-access.md).