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agri-deep-research

agri-deep-research

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 edit…

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Skill 문서

GitHub

name: agri-deep-research description: "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." metadata: version: "1.0.0" verified: "2026-07" delegates_to: food-deep-research related_skills: [agri-research, agri-paper, agri-pipeline, journal-selector, food-deep-research] references: - ../agri-research/references/agriculture-domain.md

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

  1. 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.
  2. 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).
  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).

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