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PangenomeAI/academic-skills-food-nutrition/food-research/SKILL.md

food-research

Run a comprehensive, multi-source literature and evidence-synthesis workflow for food & nutrition science. Use when the user wants to research a food/nutrition topic in depth, do a literature review, build an evidence brief, screen and synthesize many sources, verify citations, or scope a systematic review. Coordinates food-science databases, preprints, semantic search, and food-safety/regulatory sources; runs a four-layer search, two-phase screening, and cross-source synthesis via subagents; gr

Source repository stars
28
Declared platforms
0
Static risk flags
0
Last source update
2026-08-24
Source checked
2026-08-28

Decision brief

What it does: where it fits

Build a broad, defensible understanding of a topic by searching many sources, screening them consistently, and synthesizing across them. Original work; no third-party research text is reused. Architecture informed by open community literature-search skills (see Acknowledgements…

Best for

  • Four streams share the same search/screening machinery but differ in depth. Three of them (quick brief, full review, deep research) prioritize sources by journal ranking via journalranker; the systematic stream does not…

Not for

  • Tasks that require unconfirmed production actions or broad system permissions.
  • Environments where the pinned source and install steps cannot be inspected.

Compatibility matrix

Platform support, with evidence labels

PlatformStatusEvidenceWhat to check
CodexNot declaredNo explicit evidencePortability before use
Claude CodeNot declaredNo explicit evidencePortability before use
CursorNot declaredNo explicit evidencePortability before use
Gemini CLINot declaredNo explicit evidencePortability before use
Open the compatibility checker

Installation

Inspect first. Install second.

The source command is displayed only when detected. A safe inspection prompt is always available so your agent can explain every action before execution.

Source-detected install commandSource
npx skills add https://github.com/PangenomeAI/academic-skills-food-nutrition --skill "food-research"
Safe inspection promptEditorial

Inspect the Agent Skill "food-research" from https://github.com/PangenomeAI/academic-skills-food-nutrition/blob/bb0d5298ebce9df3548c8c7b23923afb4f82fd84/food-research/SKILL.md at commit bb0d5298ebce9df3548c8c7b23923afb4f82fd84. List every install step, command, network request, credential, file read/write, external action, and rollback step. Explain whether it fits my task. Do not install or execute anything until I approve.

Workflow

What the source asks the agent to do

  1. 01

    Full review (default)

    When it wakes: the user wants a thorough, citable review/evidence brief — "do a literature review on…", "comprehensive review of…", "survey the field of…", "build an evidence brief on…", "review the evidence for…" — or…

    When it wakes: the user wants a thorough, citable review/evidence brief — "do a literature review on…", "comprehensive review of…", "survey the field of…", "build an evidence brief on…", "review the evidence for…" — or…How it runs (full subagent pipeline): dispatch subagents in this order (independent retrieval runs in parallel):searchstrategist → search plan (concepts, controlled vocabulary, Boolean strings, source list).
  2. 02

    Step 1 — Frame the question (searchstrategist)

    Interventions/nutrition: PICO (Population, Intervention/Exposure, Comparator, Outcome).

    Interventions/nutrition: PICO (Population, Intervention/Exposure, Comparator, Outcome).Composition/process/safety: define the food matrix, factor/treatment, and measured response.State scope, timeframe, languages, and exclusions. Break the question into concepts and list synonyms + controlled-vocabulary terms per concept.
  3. 03

    Step 2 — Plan the sources

    Cover several source classes so the picture isn't skewed by one index: - Bibliographic: FSTA (Food Science & Technology Abstracts — the core food index), PubMed/MEDLINE, Web of Science, Scopus, CAB Abstracts, AGRICOLA, AGRIS (FAO). - Preprints: bioRxiv, ChemRxiv, agriRxiv. - Sem…

    Bibliographic: FSTA (Food Science & Technology Abstracts — the core food index), PubMed/MEDLINE, Web of Science, Scopus, CAB Abstracts, AGRICOLA, AGRIS (FAO).Preprints: bioRxiv, ChemRxiv, agriRxiv.Semantic / aggregators: CrossRef, Semantic Scholar, Consensus, Dimensions, Lens.org.
  4. 04

    Step 3 — Four-layer search (sourcescout)

    1. Layer 1 — structured search: Boolean/keyword + controlled vocabulary across the bibliographic databases (target 100–500 raw hits). Apply date/language filters. 2. Layer 2 — backward chaining: mine the reference lists of the key reviews and seminal papers for older frequently-…

    Layer 1 — structured search: Boolean/keyword + controlled vocabulary across the bibliographic databases (target 100–500 raw hits). Apply date/language filters.Layer 2 — backward chaining: mine the reference lists of the key reviews and seminal papers for older frequently-cited work.Layer 3 — forward chaining: "cited by" from seminal works to catch the latest research.
  5. 05

    Step 4 — Two-phase screening & appraisal (screenerappraiser)

    Phase A — title/abstract: apply inclusion/exclusion; narrow to 30–50 candidates.

    Phase A — title/abstract: apply inclusion/exclusion; narrow to 30–50 candidates.Phase B — full text: read the semantically strong and borderline items; land 15–30 (more for systematic).Quality rubric (score each source): study design & rigor; replication and whether n is biological (not pseudo-replicated); method validation (LOD/LOQ, recovery, controls, appropriate standards); journal quality and pred…

Permission review

Static risk signals and limitations

No configured static risk pattern was detected

This is not proof of safety. Runtime behavior, indirect dependencies, and hidden external systems are outside the static scan.

Evidence record

Why each signal appears

EvidenceSourceComputedTestedEditorial
SignalValueEvidence typeMeaning
Quality score95/100ComputedDocumentation, specificity, maintenance, and trust rules
Repository stars28SourceRepository attention, not individual Skill quality
Compatibility0 platformsSourceDeclared in the catalog source record
Usage guideautomated source guideEditorialGenerated or reviewed according to the visible evidence level

Pinned source

Provenance and original SKILL.md

Repository
PangenomeAI/academic-skills-food-nutrition
Skill path
food-research/SKILL.md
Commit
bb0d5298ebce9df3548c8c7b23923afb4f82fd84
License
MIT
Collected
2026-08-28
Default branch
main
View the original SKILL.md

Food-Research — Comprehensive Evidence Synthesis for Food & Nutrition Science

Build a broad, defensible understanding of a topic by searching many sources, screening them consistently, and synthesizing across them. Original work; no third-party research text is reused. Architecture informed by open community literature-search skills (see Acknowledgements in the repo README).

Streams — pick one and when to use it

Four streams share the same search/screening machinery but differ in depth. Three of them (quick brief, full review, deep research) prioritize sources by journal ranking via journal_ranker; the systematic stream does not (inclusion is by pre-specified eligibility, not prestige).

StreamUse it when…DepthJournal-ranking filter
quick briefYou need fast orientation on a topic — "what's known about X", a starting point, a scoping glance.One search pass; top sources; key open questions. May run inline without subagents.Yes — Tier 1 only, usually
full reviewYou want a thorough narrative review manuscript (the default).Four-layer search + two-phase screening + synthesis → write manuscript (writer) → review loop (reviewer) → Word (.docx).Yes — Tier 1 preferred, Tier 2 to fill gaps
deep researchThe question extends beyond the literature — regulatory landscape, market/technology state, an open-ended "investigate this" — or you want an iterative, verified deep dive on a subtopic.Calls the food-deep-research skill (scope → plan → investigate → verify → synthesize → critique loop); its literature portion still passes through journal ranking.Yes — for the literature portion
systematicYou need a reproducible, auditable PRISMA review / meta-analysis with a protocol, ≥3 databases, dual independent screening, and risk-of-bias (OHAT) — i.e. a defensible, publishable systematic review.Full systematic_reviewer pipeline (protocol → sr_search → dual 3-step sr_screener + sr_moderator → PRISMA → data_extractor results table → risk_of_bias OHAT → sr_synthesisreviewer loop → writer Word .docx).No — eligibility-based inclusion

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

Synthesis quality depends on reading the actual articles, not abstracts. At the start (any stream), 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 in the coverage note. Full rules: food-fetch/SKILL.md and references/full-text-access.md.

Overall flow

flowchart TD
    Q[Research question] --> M{Which stream?}
    M -- quick / full --> S1[search_strategist]
    M -- deep research --> DR[food-deep-research skill<br/>scope, plan, investigate,<br/>verify, critique loop]
    M -- systematic --> SR[systematic_reviewer<br/>PRISMA pipeline]
    S1 --> S2[source_scout<br/>four-layer search + dedup]
    S2 --> S3[screener_appraiser<br/>two-phase screening + quality tags]
    S3 --> JR[journal_ranker<br/>Tier 1 preferred; Tier 2 to fill gaps;<br/>avoid Tier 4]
    DR --> JR
    JR --> SY[synthesis<br/>evidence matrix, grading, gaps, coverage advisory]
    SY --> WR[writer<br/>manuscript, APA7 / target journal]
    WR --> RV[reviewer<br/>editorial + integrity]
    RV -- revise --> SY
    RV -- accept --> DOCX[Final review manuscript .docx]
    SR --> SROUT[Systematic-review manuscript .docx<br/>PRISMA + OHAT bias + per-RQ synthesis]

Both the full review and systematic streams finish by writing a manuscript, passing it through the reviewer loop, and delivering a Word document (writer). The quick brief and deep research streams do not (quick brief returns a short brief; deep research is handled by the food-deep-research skill).

Stream detail — invocation & subagent call sequence

Quick brief

  • When it wakes: the user wants fast orientation, not an exhaustive review. Phrases like "give me a quick brief on…", "what's known about…", "quick overview of…", "brief me on…", "orient me on…", "TL;DR of the research on…". Also the default when the user asks a scoped factual research question and signals speed ("quickly", "just the highlights").
  • How it runs (lightweight — may be inline, no subagents required):
    1. Frame the question in one line (concepts + scope).
    2. One search pass over 2–3 high-yield sources (PubMed/Consensus/CrossRef via MCP, else web search) — no four-layer expansion.
    3. Apply journal_ranker Tier 1 only — keep Q1/Q2 food-science & nutrition, Nature/Science/Cell, and Q1/Q2 other-discipline hits; ignore the rest unless nothing Tier 1 exists.
    4. Skim-appraise (relevance + obvious rigor red flags) — no full rubric.
    5. Write a short brief: 3–6 key findings with citations, the consensus vs open questions, and 2–3 sources to read next.
  • Subagents: optional. Run inline for speed; only spin up source_scout if the topic is broad. journal_ranker is applied as a filter step, not necessarily a separate dispatch.

Full review (default)

  • When it wakes: the user wants a thorough, citable review/evidence brief — "do a literature review on…", "comprehensive review of…", "survey the field of…", "build an evidence brief on…", "review the evidence for…" — or asks to research a topic without signalling that speed matters.
  • How it runs (full subagent pipeline): dispatch subagents in this order (independent retrieval runs in parallel):
    1. search_strategist → search plan (concepts, controlled vocabulary, Boolean strings, source list).
    2. source_scout → four-layer search + dedup → candidate set (parallel per source).
    3. screener_appraiser → two-phase screening + quality rubric → included set with High/Medium/Low tags.
    4. journal_ranker → prioritize by tier (Tier 1 preferred; Tier 2 only to fill gaps; avoid Tier 4).
    5. synthesis → evidence matrix, grading, contradiction resolution, coverage advisory, gaps.
    6. writer → write the review manuscript (APA 7.0 default, or target journal via journal-selector).
    7. reviewer → editorial + integrity review; if not Accept, loop back to synthesis/writer to revise, then re-review (cap ~2–3).
    8. writer → export the accepted manuscript to Word (.docx).
    • Output: a finished review manuscript (.docx) + annotated bibliography + .bib/.ris.

Deep research

  • When it wakes: "deep research on…", "investigate … thoroughly", "I need a deep dive / full briefing on…", or a question extending beyond the literature (regulatory, market, technology landscape). Calls the food-deep-research skill; its literature portion still passes through journal_ranker.

Systematic

  • When it wakes — use the systematic stream when the user needs a defensible, reproducible, publishable systematic review, signalled by any of:
    • Explicit terms: "systematic review", "systematic literature review", "PRISMA", "meta-analysis".
    • A methodological requirement: "follow a protocol / PROSPERO", "two independent reviewers / dual screening", "with risk of bias", "OHAT", "PRISMA flow diagram".
    • A rigor/audit intent: the user wants the review to be reproducible and auditable (every search string, screening decision, and exclusion reason recorded), not just a narrative overview.
    • If the user only wants a broad narrative overview, use full review instead; if unsure which they want, ask one question ("narrative review or a full PRISMA systematic review with risk-of-bias?").
  • How it runs: the systematic_reviewer orchestrator drives protocol → sr_search (≥3 databases) → dual independent three-step screening (sr_screener ×2 + sr_moderator) → PRISMA flow → data_extractor results table → risk_of_bias (OHAT) → sr_synthesisreviewer loop → writer Word .docx. Journal ranking is not applied (eligibility-based inclusion).

Subagents (dispatch, don't inline)

Run these as subagents (via the Agent tool). Layers that are independent — e.g. per-source retrieval — run in parallel.

  1. search_strategist — turns the question into a search plan: concepts, synonyms/controlled vocabulary (MeSH, FSTA/CAB thesaurus terms), Boolean strings per database, filters, and the source list.
  2. source_scout — executes the four-layer retrieval across sources, records hit counts, and deduplicates into one candidate set.
  3. screener_appraiser — two-phase screening + the food-science quality rubric; outputs the included set with quality tags.
  4. journal_ranker — prioritizes the screened sources by journal ranking (Q1/Q2 food-science & nutrition, plus Nature/Science/Cell families and Q1/Q2 in any other discipline = highest; Q3 second; Q4 avoided). Used by quick brief, full review, and deep research only — never inside a systematic review.
  5. synthesis — evidence matrix, contradiction resolution, evidence grading, gap analysis, and the coverage advisory.
  6. writer — writes the review manuscript and exports Word (.docx) (APA 7.0 default, or target journal via journal-selector). Full review + systematic.
  7. reviewer — combined editorial + integrity review with a revision loop. Full review + systematic.

Systematic-review subagents (systematic stream only): 8. systematic_reviewer — PRISMA orchestrator (protocol → search → dual screening → PRISMA → extraction → risk of bias → synthesis → review → Word). 9. sr_search — ≥3 databases (Web of Science, Scopus, PubMed preferred); combine + deduplicate; log all strings/counts. 10. sr_screener — run as two independent instances; three steps (title → abstract → full text) with per-record include/exclude + reasons. 11. sr_moderator — after each step, compares the two screeners, resolves conflicts, keeps PRISMA counts. 12. data_extractor — pulls the results table (by research question) from the final shortlist. 13. risk_of_bias — OHAT risk-of-bias assessment (in vitro / human / animal) by default. 14. sr_synthesis — PRISMA description → risk-of-bias results → per-RQ synthesis; formats APA 7.0 or target journal.

For a quick brief you may run the workflow inline without subagents.

Step 1 — Frame the question (search_strategist)

  • Interventions/nutrition: PICO (Population, Intervention/Exposure, Comparator, Outcome).
  • Composition/process/safety: define the food matrix, factor/treatment, and measured response.
  • State scope, timeframe, languages, and exclusions. Break the question into concepts and list synonyms + controlled-vocabulary terms per concept.

Step 2 — Plan the sources

Cover several source classes so the picture isn't skewed by one index:

  • Bibliographic: FSTA (Food Science & Technology Abstracts — the core food index), PubMed/MEDLINE, Web of Science, Scopus, CAB Abstracts, AGRICOLA, AGRIS (FAO).
  • Preprints: bioRxiv, ChemRxiv, agriRxiv.
  • Semantic / aggregators: CrossRef, Semantic Scholar, Consensus, Dimensions, Lens.org.
  • Safety & regulatory / grey: EFSA, US FDA, USDA (incl. FoodData Central), Codex Alimentarius, WHO, EU/national food-standards bodies.
  • Chemistry / bioactives: PubChem, ChEMBL, FooDB, Phenol-Explorer.
  • Methods / standards: AOAC, ISO.

Tooling: use whatever literature MCP tools are connected (e.g. PubMed, Consensus, bioRxiv, CrossRef, Scopus/ScienceDirect) for live retrieval; fall back to web search for any source without a tool. Record which tool/source produced each result so the search is reproducible.

Step 3 — Four-layer search (source_scout)

  1. Layer 1 — structured search: Boolean/keyword + controlled vocabulary across the bibliographic databases (target 100–500 raw hits). Apply date/language filters.
  2. Layer 2 — backward chaining: mine the reference lists of the key reviews and seminal papers for older frequently-cited work.
  3. Layer 3 — forward chaining: "cited by" from seminal works to catch the latest research.
  4. Layer 4 — semantic / cross-disciplinary: related-article and semantic tools to catch methodologically or disciplinarily adjacent work (chemistry, engineering, nutrition, microbiology) that keyword search misses.
  • Deduplicate by DOI/title across sources. Record the hit count at each layer.
  • Stop when the search saturates — e.g. ≥3 of: no new themes appearing, citation loops closing, timeframe covered, key authors/venues all seen, new hits <10% novel.

Step 4 — Two-phase screening & appraisal (screener_appraiser)

  • Phase A — title/abstract: apply inclusion/exclusion; narrow to ~30–50 candidates.
  • Phase B — full text: read the semantically strong and borderline items; land ~15–30 (more for systematic).
  • Quality rubric (score each source): study design & rigor; replication and whether n is biological (not pseudo-replicated); method validation (LOD/LOQ, recovery, controls, appropriate standards); journal quality and predatory/fabrication check; relevance to the question; recency/currency. Tag each source High / Medium / Low.
  • Universal gates (relevance, methodological soundness, predatory/fabrication) are never waived; only publication-type/recency expectations flex by subfield.

Step 4.5 — Prioritize by journal ranking (journal_ranker) — quick / full / deep only

  • Tier every screened source: Tier 1 = Q1/Q2 in Food Science & Technology or Nutrition & Dietetics, any Nature/Science/Cell-family journal, or Q1/Q2 in any other WoS discipline/multidisciplinary category; Tier 2 = Q3; Tier 3 = Q4 (avoid).
  • Prefer the highest tier that covers each point — if Tier 1 sources suffice, don't include Tier 2/3 for it; drop to Tier 2 only when Tier 1 is insufficient; use Tier 3 only when nothing better exists, and flag it.
  • Uses references/journal-priority.csv for food/nutrition quartiles; JCR knowledge for other fields.
  • Skip this step entirely in the systematic stream — inclusion there is by eligibility, not journal ranking.

Step 5 — Synthesis (synthesis)

  • Evidence matrix: source × theme grid showing coverage density and method spread.
  • Integrate & resolve conflicts: weigh by design and rigor; separate consistent findings from contested ones; explain disagreements (matrix, method, dose, population).
  • Grade the evidence: prefer systematic reviews/RCTs for health/nutrition claims; require standardized measurement (AOAC/ISO) for compositional/process claims. State confidence and why.
  • Coverage advisory: flag when >70% of sources share one publication year, region, food matrix, method, or venue family — a bias risk.
  • Gaps: under-powered areas, missing methods, population/geographic voids; propose the next study.

Deliverables

An evidence brief containing: question & scope; reproducible search strategy (sources, Boolean strings, filters, dates); screening funnel with counts; annotated bibliography (per source: design, findings, relevance, quality tag, intended paper section); literature/evidence matrix; graded conclusions; coverage advisory; and a gap list. Export references as .bib/.ris (deduplicated) for reuse.

Deep dives

For a subtopic that needs open-ended investigation beyond the literature (e.g. regulatory landscape, market/technology state), call the food-deep-research skill and fold its sourced synthesis back into the evidence brief.

References (load as needed)

  • references/literature-sources.md — databases + APIs (FSTA/PubMed/WoS/Scopus/CrossRef/OpenAlex + EFSA/FDA/USDA) for search_strategist/source_scout/sr_search.
  • references/full-text-access.md — reading the actual full text (open access → connected tool → user PDFs → library session), legitimate access only; for source_scout/screener_appraiser and any step that needs more than an abstract.
  • references/source-quality-hierarchy.md — evidence grading for screener_appraiser/synthesis.
  • references/reporting-guidelines.md — EQUATOR/PRISMA/CONSORT/STROBE for the systematic stream and appraisal.
  • references/ohat-risk-of-bias.md — full OHAT tool (11 questions, 4-point scale, design applicability incl. corrected in-vitro Q3/Q4 = NA, and in-vitro criteria) for risk_of_bias.
  • food-paper/references/writing-style.md + food-paper/references/human-writing.mdwriter: academic style + remove AI tells, applied together — write in the field's academic register as a human scientist, and strip machine tells (inflated significance, vague attribution, stock vocabulary, hedge stacking) while keeping calibrated hedging and journal form.
  • food-paper/references/faithfulness-and-citation.mdgrounding + four-gate citation check. Every finding, number, and citation traces to a real source; never fabricate. scripts/verify_citations.py audits the reference set.
  • food-paper/references/privacy-and-confidentiality.mdprivacy scan before delivering the brief/report (no local paths/secrets); scripts/privacy_scan.py.

Handoff

Sources tagged and assigned by section feed food-paper (Introduction and Discussion evidence, reference list) and are orchestrated by food-pipeline.

Food & nutrition rigor notes

Watch for pseudo-replication (analytical replicates as biological n); matrix effects and single-cultivar/single-batch over-generalization; unvalidated assays; and undisclosed funding/conflicts, which are common and material here.

Frequently asked questions

What to verify before installation and use

What does the food-research source document cover?

Build a broad, defensible understanding of a topic by searching many sources, screening them consistently, and synthesizing across them. Original work; no third-party research text is reused. Architecture informed by open community literature-search skills (see Acknowledgements…

How do I install food-research?

The source record exposes this install command: npx skills add https://github.com/PangenomeAI/academic-skills-food-nutrition --skill "food-research". Inspect the command and pinned source before running it.

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