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QualityPython

Food Review

by PangenomeAI

Multi-reviewer peer-review system that simulates an editorial panel for food and nutrition manuscripts, grounding critiques in cited sources and field literature. Use before submission for a rigorous check.

16 stars1 forksAdded 2026/07/16
academic-writingai-agentsanthropicclaude-codeclaude-skillscodexfoodfood-sciencefood-science-innovationfood-technologyliterature-reviewllmmeta-analysisnutritionnutrition-sciencepeer-reviewresearch-toolsscholarly-publishingscientific-figuressystematic-review

Documentation

README

Food-Review β€” Multi-Reviewer Peer Review for Food & Nutrition Manuscripts

Give the author the review a good food-science journal would return, from a panel rather than a single voice. Original work; architecture informed by open community peer-review skills (see the repo README Acknowledgements).

Modes

  • full (default) β€” the whole panel: three domain reviewers + devil's advocate + format check, synthesized by the coordinator into an editorial decision.
  • quick β€” coordinator + one blended reviewer pass; a fast readiness verdict.
  • methodology β€” deep dive by reviewer_methodology only.
  • re-review β€” re-assess a revised manuscript against the prior reports and the author's response, verifying each point was addressed.

Panel (dispatch via the Agent tool; reviewers run in parallel)

  1. review_coordinator (editor-in-chief) β€” sets the target journal + scope, dispatches knowledge_builder, then the reviewers, synthesizes their reports, resolves disagreement, and issues the decision.
  2. knowledge_builder β€” runs first: reads the manuscript's cited sources (Pathway A) and the field's key literature (Pathway B) into a shared knowledge base so the panel judges the science from knowledge, not impression.
  3. reviewer_methodology β€” design, statistics, reproducibility, validation.
  4. reviewer_domain β€” novelty, significance, scope fit, domain correctness (food/nutrition science).
  5. reviewer_integrity β€” data & citation integrity, food-safety/ethics, reporting completeness.
  6. devils_advocate β€” adversarial challenge to the paper's central claim.
  7. format_checker β€” formatting & reference-style compliance vs the target journal.

Ground the panel first β€” the knowledge base

Reviewers must understand the topic and its background before they critique it. knowledge_builder runs before the reviewers and builds one knowledge base from:

  • A β€” the manuscript's own citations: retrieve and read the full cited articles, extract what each actually shows, and audit whether it supports the claim it is attached to.
  • B β€” the field's key literature: extract the manuscript's keywords and research disciplines, search the literature for the field's key work (may use the food-research full review branch for discovery/screening β€” but knowledge extraction only, no literature-review article).

A + B give the panel the state of the art, standard methods and benchmark values, consensus vs contested points, a novelty map, and gaps β€” so novelty and correctness are judged, not guessed. Never summarize a source that was not retrieved; mark abstract-only and unretrievable items. In quick mode, build a light version (Pathway A spot-checks on the load-bearing citations).

Inside food-pipeline (Stage 1 already ran): don't search the field twice β€” reuse the Stage-1 evidence base in place of the Pathway-B search, topped up with food-research quick brief to find the field's key review publications and read them in full; knowledge base = Stage-1 knowledge + key-review knowledge (Pathway A still runs). Standalone food-review is unaffected and always builds the full A + B.

Workflow

flowchart TD
    A[Manuscript in] --> B[review_coordinator<br/>resolve target journal + scope]
    B --> JS[journal-selector<br/>ask once β†’ journal or 'generic'/APA 7.0]
    B --> KB[knowledge_builder<br/>A: read cited sources<br/>B: key field literature]
    KB --> KBase[(Knowledge base<br/>state of the art Β· benchmarks Β·<br/>novelty map Β· cited-source audit)]
    KBase --> R1[reviewer_methodology]
    KBase --> R2[reviewer_domain]
    KBase --> R3[reviewer_integrity]
    KBase --> R4[devils_advocate]
    JS --> FC[format_checker]
    R1 --> C[review_coordinator<br/>synthesize + decision]
    R2 --> C
    R3 --> C
    R4 --> C
    FC --> C
    C --> O[Panel report:<br/>per-reviewer reports + format check +<br/>editorial decision + revision checklist +<br/>response-letter skeleton]

Formatting / target journal

The review_coordinator first establishes the target journal by calling journal-selector, which asks the user which journal the manuscript targets (they may answer 'generic' β†’ APA 7.0). This is asked once: the resolved journal's structure, limits, and reference/citation style are recorded and reused, and format_checker audits the manuscript against them. Don't re-ask unless the user names a different target journal; reuse the choice if food-pipeline already resolved one.

Output β€” a Word (.docx) report, not Markdown

A consolidated review report delivered as a .docx in the canonical structure of references/report-format.md: header (manuscript, target journal, editorial decision, colour legend) β†’ overall assessment β†’ Part A editing report by category β†’ summary β†’ Part B scientific-quality comments + editorial decision + residual items β†’ Part C figure/table consistency audit. Every concern carries a stable issue ID (A#/B#/C#/D#, SQ#, FC#) and a Response (<type>) line β€” Recommendation or, for a fix that needs the author's data/decision, Editor query β€” with a precise location (P## / Table / Figure). Critique the work, not the author.

Markdown is a working format, never the deliverable. Convert with Pandoc (pandoc report.md -o report.docx) or the docx skill; if neither is available, say so and hand over the Markdown with the conversion command β€” never claim a .docx you did not produce. Apply the colour legend as real Word formatting and leave no Markdown syntax in the file. Inside food-pipeline this report is the single document that food-paper later fills responses into β€” no separate response letter is created (see references/report-format.md).

When the manuscript is a Word (.docx) file (or equivalent — LibreOffice / Pages / Google Docs), also deliver the manuscript itself with margin comments. In addition to the report, insert the panel's concerns as Word review comments anchored to the exact text they target (one comment per concern, prefixed with the reviewer lens + severity), using the word processor's Review/Comments feature. See references/word-review-comments.md. If the manuscript isn't a Word doc or no Word tooling is available, deliver a location→comment table instead and say so.

References (load as needed)

  • references/report-format.md β€” canonical review report + revision-log format (Parts A/B/C, issue IDs, Response (<type>) taxonomy, editor queries, colour legend).
  • references/review-criteria.md β€” what each reviewer checks (food-tuned).
  • references/quality-rubrics.md β€” 1–5 scoring per dimension + weights.
  • references/editorial-decisions.md β€” review_coordinator: Accept/Minor/Major/Reject logic + overrides.
  • references/word-review-comments.md β€” insert margin comments into a Word/.docx manuscript (in addition to the report).
  • references/ethics-integrity-checklist.md β€” reviewer_integrity (canonical; shared with food-deep-research).
  • food-paper/references/statistics-reporting.md β€” reviewer_methodology: stats red flags.
  • food-paper/references/faithfulness-and-citation.md β€” reviewer_integrity: verify every citation is real (four-gate) and every claim is source-bound; flag any fabricated/unsupported content.
  • food-paper/references/privacy-and-confidentiality.md β€” check the review report has no local paths/secrets before delivery; scripts/privacy_scan.py.

Handoff

Feeds food-paper (revise mode) for the author to act on; part of the food-pipeline review→revise loop.

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