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Food deep research

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

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What its author says it does

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General-purpose deep research that produces a fully written, source-validated literature review on any question: scope it, design the method, discover and screen sources by journal ranking, validate every source, extract and verify evidence, synthesize, stress-test, then write and format the review (APA 7.0 by default, or a target journal's style) and polish it through an editorial + integrity review loop. Use standalone for a deep dive or literature review, or as the deep-dive engine called by food-research. Runs a 12-subagent team with iterate-to-saturation and compile↔review loops. Triggers: deep research, research this in depth, write a literature review, investigate thoroughly, comprehensive review, state of the evidence, briefing on, dig into, deep dive.

SKILL.md

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Deep-Research — Source-Validated Literature Review Engine

Answer a hard question properly and hand back a written, formatted, integrity- checked literature review — not just notes. Scope → design → discover → screen by journal ranking → validate sources → extract & verify evidence → synthesize → stress-test → write → review-loop → final report. Original work; architecture informed by open community food-deep-research skills (see the repo README Acknowledgements). Usable standalone, or as the deep-dive engine called by food-research.

Modes

  • quick brief — scope → discover → screen (Tier 1) → light synthesis → short sourced answer. Skips the full validation/compile/review loop.
  • full — the default: the complete 12-subagent pipeline below with the iterate-to-saturation and compile↔review loops, ending in a finished review.

Subagent team (dispatch via the Agent tool)

#SubagentJob
1research_scopeComprehensive scope brief: background, problem, significance, central + sub-questions, concepts, boundaries, success criteria.
2research_architectMethodology blueprint: review type, search strategy, inclusion criteria, analytical framework, reporting standard, stopping criteria.
3investigatorPass 1 discover candidate sources; Pass 2 extract evidence from validated sources only (parallel per sub-question).
4source_screenerPrioritize candidates by journal ranking (Tier 1 Q1/Q2 + Nature/Science/Cell + other-discipline Q1/Q2; Tier 2 Q3; avoid Tier 4).
5source_verifierValidate each prioritized source (existence/DOI, venue legitimacy, retraction, predatory, methodology, COI) → Source Quality Matrix.
6bibliographyDeduplicate + format references (APA 7.0 default, or target-journal style via journal-selector); build the citation map + .bib/.ris.
7claim_verifierVerify each load-bearing claim against its validated source; classify fact/hypothesis/contested/speculation.
8synthesizerEvidence matrix, thematic synthesis, conflict reconciliation, evidence grading, coverage advisory, gap agenda, narrative arc.
9criticDevil's advocate on the synthesis; loop back to investigate if gaps.
10compilerWrite & format the literature-review draft (APA 7.0 / target journal); cite by key only; no fabrication.
11editorEditorial review of the draft (5 weighted dimensions, verdict + prioritized feedback).
12ethics_reviewerIntegrity/ethics review of the draft (citation integrity, faithful representation, bias, COI, disclosure).

Workflow

flowchart TD
    A[research_scope<br/>scope brief] --> B[research_architect<br/>methodology blueprint]
    B --> C[investigator Pass 1<br/>discover candidate sources]
    C --> D[source_screener<br/>journal-ranking tiers]
    D --> E[source_verifier<br/>validate → Source Quality Matrix]
    E --> F[bibliography<br/>dedupe + format + citation map]
    E --> G[investigator Pass 2<br/>extract evidence from validated sources]
    G --> H[claim_verifier<br/>verify claims vs validated sources]
    H --> I[synthesizer<br/>matrix, themes, conflicts, grading, gaps]
    I --> J[critic<br/>stress-test synthesis]
    J -- gaps --> C
    J -- sound --> K[compiler<br/>write + format review<br/>APA 7.0 / target journal]
    F --> K
    K --> L[editor + ethics_reviewer<br/>editorial + integrity review]
    L -- minor/major revision --> K
    L -- accept --> M[Final literature review]

Two loops: (1) evidence loopcritic sends gaps back to investigator (cap ~2–3); (2) writing loopeditor/ethics_reviewer send revisions back to compiler until Accept (cap ~2–3), then deliver.

First move — set up full-text access (once)

The evidence loop reads actual articles, not abstracts. At the start, run the food-fetch first-run setup (python3 scripts/food_fetch_setup.py status): if the user hasn't set up access, surface the one-time highlighted request to provide their EndNote .Data folder (or Zotero/Mendeley / a PDF folder) or institutional access, warning that without non-open-access access the accuracy is substantially limited; save the choice so it isn't re-asked. If they chose "open-access only", remind briefly of that accuracy limit each run. Don't block — proceed at open-access + abstract level and flag paywalled sources. Full rules: food-fetch/SKILL.md and food-research/references/full-text-access.md.

Source discipline (non-negotiable)

  • Investigation and claim-checking operate only on validated sources — those that passed source_screener (ranking) and source_verifier (validity). Retracted/unresolvable sources are excluded and logged.
  • Journal ranking favors Tier 1 (Q1/Q2 food-science & nutrition, Nature/Science/Cell families, Q1/Q2 in any other discipline); Tier 2 (Q3) only to fill gaps; Tier 4 avoided.
  • Every claim carries a source and locator; inference is labelled as inference.

Formatting — resolve the target journal once

At the start, load journal-selector/SKILL.md (a shared procedure, not an installed skill), which asks which journal the review targets (they may answer 'generic' → APA 7.0). Ask once: record the choice and reuse it in bibliography and compiler to format the review to that journal's structure, limits, and reference style — don't ask again. Reuse a journal already resolved by food-research/food-pipeline; re-resolve only if the user asks to switch journals.

Principles

Every claim sourced; fact separated from interpretation; disagreement shown, not averaged; uncertainty surfaced. Upstream evidence beats parametric knowledge — mark missing evidence [EVIDENCE GAP], never fabricate.

References (load as needed)

  • references/reasoning-and-fallacies.mdsynthesizer/critic: sound argument + fallacies.
  • food-research/references/literature-sources.md — databases/APIs for investigator.
  • food-research/references/full-text-access.md — reading the actual full text for investigator/source_verifier (open access → connected tool → user PDFs → library session, legitimate access only); mark anything unread rather than summarizing.
  • food-research/references/source-quality-hierarchy.md — evidence grading for source_verifier/synthesizer.
  • food-research/references/reporting-guidelines.md — EQUATOR/PRISMA/CONSORT/STROBE.
  • food-paper/references/apa7-quickref.md — APA 7.0 for bibliography/compiler (default style).
  • food-paper/references/writing-style.md + food-paper/references/human-writing.mdcompiler: academic style + remove AI tells, applied together — academic register as a human scientist, machine tells stripped, calibrated hedging and journal form kept.
  • food-review/references/ethics-integrity-checklist.md — for ethics_reviewer.
  • food-paper/references/faithfulness-and-citation.mdgrounding + four-gate citation check for bibliography, source_verifier, claim_verifier, compiler. Never invent sources/data; run scripts/verify_citations.py.
  • food-paper/references/privacy-and-confidentiality.mdprivacy scan before delivering the report (no local paths/secrets); scripts/privacy_scan.py.

Handoff

Standalone → deliver the final review. Called by food-research → return the validated synthesis (or the finished review) to fold into the evidence brief.

Keep looking

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