Academic Skills for Food & Nutrition Science

AI research assistant for food science and nutrition — Claude Code & Codex
skills for literature review, systematic review (PRISMA & meta-analysis), data
analysis and statistics, scientific figures, journal formatting, and peer
review. Food-science research automation, end to end.

Original, MIT-licensed Claude Code skills for the food & nutrition research
lifecycle — research → write → review → revise → finalize — where each core
skill is a multi-subagent system and a master pipeline orchestrates them,
with built-in knowledge of food & nutrition journal author guidelines and a
food-science figure workflow.

This open project was initiated by the Food Science Group at the University of
Melbourne, and we warmly welcome food & nutrition research groups from around
the world to use, adapt, and contribute to it. MIT-licensed and open source.

Install

Claude Code (one command):

claude plugin marketplace add PangenomeAI/academic-skills-food-nutrition && \
claude plugin install academic-skills-food-nutrition@academic-skills-food-nutrition

Then restart Claude Code (or run /plugin). Update later with
claude plugin update academic-skills-food-nutrition.

Both Claude Code and Codex (one command via the installer):

curl -fsSL https://raw.githubusercontent.com/PangenomeAI/academic-skills-food-nutrition/main/install.sh | bash

Or, from a local clone: ./install.sh (both) · ./install.sh claude · ./install.sh codex.
The installer registers the Claude Code plugin and copies the skills bundle into
Codex's skills directory (${CODEX_HOME:-~/.codex}/skills/), preserving the repo
structure so cross-skill references resolve.

Skills

Core workflow

• food-research — comprehensive, multi-source literature discovery and
  evidence synthesis for food & nutrition (FSTA, PubMed, Web of Science, Scopus,
  AGRICOLA, preprints, semantic search; EFSA/FDA/USDA/Codex for safety and
  regulatory evidence). Four-layer search, two-phase screening, and synthesis via
  subagents; grades evidence and maps gaps. Four streams — quick brief, full
  review, deep research, systematic. The first three prioritize sources by
  journal ranking (journal_ranker: Q1/Q2 food-science & nutrition, plus
  Nature/Science/Cell families and Q1/Q2 in any other discipline = highest
  priority; Q3 second; Q4 avoided). The full review and systematic streams
  finish by writing a manuscript, running an editorial + integrity reviewer
  loop, and exporting a Word (.docx) (APA 7.0 default, or a target journal via
  journal-selector). The PRISMA 2020 systematic review stream adds a fixed
  protocol, ≥3 databases (Web of Science/Scopus/PubMed), dual independent
  three-step screening (title → abstract → full text) with a moderator, a PRISMA
  flow diagram, a results table, and OHAT risk-of-bias (in vitro / human /
  animal); it uses eligibility-based inclusion rather than journal ranking.
• deep-research — source-validated literature-review engine (scope → design
  → discover → screen by journal ranking → validate every source →
  extract & verify evidence → synthesize → stress-test → write & format →
  editorial + integrity review loop) with a 12-subagent team. Outputs a finished,
  formatted review (APA 7.0 by default, or a target journal's style via
  journal-selector). Runs standalone or as food-research's deep-dive engine.
• food-paper — whole-process manuscript system (12 subagents) covering the
  full research lifecycle: understand the field (calls food-research), frame
  research questions, curate data, run statistics, build figures &
  tables (calls food-figure), construct arguments and discussion, draft,
  polish, manage citations, and self-review (calls food-review) —
  journal-aware throughout (APA 7.0 default, or a target journal via
  journal-selector).
• food-review — multi-reviewer peer-review panel (coordinating editor +
  methodology, domain/novelty, and integrity/ethics reviewers + a devil's
  advocate) with a formatting-compliance check against the target journal
  (APA 7.0 default, or a specific journal via journal-selector), ending in an
  editorial decision + revision checklist + response-letter skeleton.
• food-pipeline — master orchestrator that routes a project to the
  specialist skills (each with its own subagent team) and enforces quality gates:
  journal selection → research (food-research/deep-research) → write & analyze
  (food-paper → food-figure) → peer review (food-review) → revise →
  re-review → finalize, with mandatory author decision points.

Journal knowledge

• journal-selector — asks which journal you're targeting (or reads it from
  your request) and loads that journal's constraints. Covers the Food Science &
  Technology (60) and Nutrition & Dietetics (59) journal lists (see
  journals/_coverage.md and
  journals/_coverage_nutrition.md).
• journals/* — 19 publisher-tiered author-guideline skills covering both
  the Food Science & Technology and Nutrition & Dietetics journal lists
  (Elsevier, Wiley, Nature Portfolio, Springer, Taylor & Francis, MDPI, RSC, ACS,
  Annual Reviews, Oxford, Emerald, KeAi/Tsinghua, Codon, BioMed Central,
  Cambridge, Frontiers, plus a niche-publisher skill). Each lists the journals it
  covers (see journals/_coverage.md and
  journals/_coverage_nutrition.md), their
  limits, structure, reference/citation style, and a submission checklist.

Figures

• food-figure — comprehensive figure system: analyzes your data
  (scripts/analyze_data.py profiles a CSV/TSV and recommends the best figure
  type), then renders submission-grade graphics in Python or R at the target
  journal's spec. Covers all common scientific figure types (bar/box/violin,
  line/kinetic, scatter/regression, Bland-Altman, sensory radar, chromatograms,
  TPA/rheology, dose-response, survival, PCA/PLS-DA, clustered heatmaps, forest,
  microscopy plates, multi-panel), with nine reference docs plus data-profiler and
  backend-preference scripts. Exports journal-ready SVG/PDF/TIFF.

How it fits together

Name a journal ("I want to publish on Food Chemistry" / "format for LWT") and
journal-selector loads that journal's rules; food-paper writes to them and
re-flows the reference list into the journal's citation style; any figure
request goes to food-figure at the journal's DPI and column width. Ask for the
whole thing and food-pipeline runs research → write → review → revise with
checkpoints.

Coverage

All 60 target journals map to a publisher-tiered skill, verified by
scripts/check_journal_coverage.py. Full
map: journals/_coverage.md.

Author-guideline details record a Source: URL and a Verified: date. Publisher
pages change and several block automated access — confirm exact numeric limits at
the source before submitting; structure and reference styles are the stable part.

Contributing

We welcome contributions from food & nutrition research groups worldwide. If your
team would like to contribute or collaborate, please contact the development team
at [email protected].

Branching model — please read: main is release-only; never push to it
directly. Do your work on the development branch and open a pull request
to merge development → main, so changes are tracked and reviewed. Always keep
README.md and CHANGELOG.md up to date in the same PR.

Full, machine-actionable instructions for collaborators and their AI coding agents
are in AGENTS.md (see also CONTRIBUTING.md).

Key documents: README · CHANGELOG · LICENSE.

License & community

MIT — see LICENSE. Free for any use, including commercial. This open
project was initiated by the Food Science Group, University of Melbourne
(PangeZAU / PangenomeAI). Contributions from food & nutrition research groups
worldwide are warmly welcomed — open an issue or pull request.

Acknowledgements

This is original, independently written work released under MIT. It was informed
by — but contains no code or text from — earlier community projects exploring
academic-research and scientific-figure skills for Claude Code, including the
nature-skills collection (Apache-2.0), deer-flow (MIT), and
academic-research-skills (CC-BY-NC-4.0). Only non-copyrightable workflow
concepts (e.g. multi-source search, layered retrieval, staged screening,
parallel extraction, PRISMA structure, subagent teams) were drawn on; all wording
here is our own, so this project is free of their license obligations and is
offered under MIT.