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.

Claude Code, Codex, and MiniMax Agent (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 (all) · ./install.sh claude · ./install.sh codex · ./install.sh minimax. The installer registers the Claude Code plugin, and for Codex (${CODEX_HOME:-~/.codex}/skills/) and MiniMax Agent (Mavis; ${MAVIS_SKILLS_DIR:-~/.mavis/skills}/) it installs each skill flat (…/skills/<name>/SKILL.md, so the agent discovers it) plus the shared journals/ and scripts/ directories so cross-skill references resolve. Restart the app so it rescans skills. (Override the location with CODEX_HOME / MAVIS_SKILLS_DIR, or add via MiniMax's in-app Skill Creator/import.)

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.
• food-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/food-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, plus 35 multidisciplinary / cross-discipline journals food & nutrition researchers publish in (Nature, Science, Cell, and PNAS families, eLife, PLOS, ES&T, Gut, etc.). See journals/_coverage.md, journals/_coverage_nutrition.md, and journals/_coverage_multidisciplinary.md.
• journals/* — 24 publisher-tiered author-guideline skills covering the Food Science & Technology, Nutrition & Dietetics, and multidisciplinary 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 Python (matplotlib/seaborn/subplot_mosaic/ statsmodels) and R (ggplot2/patchwork/ComplexHeatmap/ggrepel + svglite/cairo_pdf/ ragg) template libraries, curated colourblind-safe palettes, per-figure provenance + captions, and a QA gate. Exports journal-ready SVG/PDF/TIFF. For schematics/graphical abstracts, an opt-in AI-image route (Gemini/ChatGPT/ Claude) with a structured prompt method — never for data figures. A runnable synthetic/illustrative Figure-story gallery provides four final 11- or 12-panel food-science figures spanning active packaging, probiotic storage, trained sensory analysis, and analytical-method validation. Each narrative progresses from a code-drawn experimental schematic through topic-specific raw and derived evidence to an explicitly illustrative synthesis panel, with deterministic source-data generation, PDF + PNG exports, captions, and trace cards. The gallery also documents an AI schematic-route test and its QA/reproducibility safeguards.

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.

Using AI responsibly for academic work

AI can make mistakes. Large language models can produce fluent but incorrect statements, invented references, and unsupported conclusions. You — the researcher — are responsible for the integrity of your work.

These skills are research assistants, not authors. Treat every AI-generated statement as a draft to be verified, never as established fact.

Your responsibilities

• Validate everything. Check every claim, number, and citation against the primary source before you use or submit it. This project enforces an anti-fabrication grounding rule and ships a runnable check (scripts/verify_citations.py; see faithfulness-and-citation.md) — use them, but they do not replace your own judgement.
• Never present unverified AI output as your own knowledge or as fact; do not submit AI-written text you have not checked and understood.
• Disclose your use of AI honestly, following your venue's and institution's policy — most journals and universities now require an AI-use statement (see declarations-guide.md).
• Follow your institution's academic-integrity rules.

University of Melbourne policy (for UoM staff and students)

This project is initiated by the Food Science Research Team at the University of Melbourne; UoM users must comply with University policy on acknowledging and using generative AI. Always check the current policy and your course/coordinator's specific requirements:

• Acknowledging use of AI tools and technologies
• Academic integrity
• Guidelines for allowing student GenAI use in assessment
• Writing with GenAI
• Studying with GenAI
• Organising with GenAI
• Using GenAI effectively

Further reading

• Sarkar, R. (2026). Why AI can't be trusted to write scientific reviews. Nature. https://doi.org/10.1038/d41586-026-01616-3
• Using AI responsibly in scientific publishing. (2026). Nature Methods (editorial). https://doi.org/10.1038/s41592-026-03020-1

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.

Contributors and institutions

• Zijian Liang — Food Science Research Team, University of Melbourne.

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), Light-skills (MIT), academic-figure-skills and academic-figure-generator (MIT), Awesome-Journal-Skills (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, evidence/citation verification gates, and publication figure styles) were drawn on; all wording here is our own, so this project is free of their license obligations and is offered under MIT.