medsci-skills

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What is MedSci Skills?

MedSci Skills is an open-source Agent Skills collection for clinical research — the manuscript and the medical-AI model alike — designed to be driven directly by AI coding agents (Claude Code, Codex, Cursor, and GitHub Copilot). It helps physician-researchers and biomedical/medical-engineering investigators move from literature search, study design, statistics, and figures to reporting-guideline compliance, citation/reference auditing, numerical-consistency checks, and response-to-reviewer workflows — combining agentic writing with deterministic integrity gates for submission-grade biomedical research. As of v5.0 it adds a model-engineering lane: choose a paper-grounded architecture, scaffold a reproducible, leakage-safe PyTorch training repo, and validate, document, and evaluate a medical-imaging or LLM/MLLM model so the work reaches a paper — it ships a minimal runnable default model for a forward-pass smoke test and integrates MONAI / nnU-Net / timm / torchvision for production-grade models, rather than reimplementing the ecosystem. Clinical AI model research engineering is in scope; it is not a diagnostic tool, an autonomous author, or a general AI-scientist platform, and every output requires human-expert verification. New here? See the 3 workflows below, the FAQ, the research connectors it calls (keyless public APIs — nothing to set up in the common case), and the scope boundary.

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Quick Start

No terminal? Use the classroom installer ZIP — download, unzip, double-click the installer, then restart your agent app (see Installation).

Have a terminal? Fastest path — one command, nothing to clone:

npx medsci-skills install        # copies every skill into your agent's folder

Recommended (especially for clinicians): add --enable-update-notify so Claude Code shows a one-line "update available" notice when a new version ships — otherwise you stay on the version you installed and are never told. (No terminal at all? The classroom installer below turns this on for you.)

npx medsci-skills install --enable-update-notify        # install + in-app update reminders

Have git? Install every skill in three commands:

git clone https://github.com/Aperivue/medsci-skills.git
mkdir -p ~/.claude/skills
cp -r medsci-skills/skills/* ~/.claude/skills/

Restart Claude Code, then start with /orchestrate — it classifies your request and routes you to the right skill. Full install options (Codex, Cursor, individual skills) are in Installation.

Install as a Claude Code plugin

Prefer plugins? One line adds the marketplace; /plugin then lets you browse nine category plugins and enable the ones you want:

/plugin marketplace add Aperivue/medsci-skills
/plugin            # browse nine category plugins; enable the ones you want

Plugin	Covers
medsci-literature	Literature search, full-text retrieval, Zotero sync, reference-integrity audits
medsci-data	Study design, variable operationalization, sample size, data cleaning, de-identification, codebooks, dataset versioning
medsci-modeling	Architecture selection, reproducible model-scaffold repos, model-validation audits, Model Card/Datasheet, model & LLM/MLLM evaluation
medsci-analysis	Statistics, figures, batch/cross-national/replication analysis, meta-analysis
medsci-writing	IMRAD & protocol drafting, AI-pattern removal, AI-search optimization, reviewer responses
medsci-review	Self-review, peer review, reporting-guideline compliance
medsci-submission	Submission packaging, journal selection, ICMJE/IRB form filling, grant proposals
medsci-project	Orchestration, project intake/management, gap & topic discovery, author strategy
medsci-presentation	Presentations/PPTX, PDF/document rendering, environment setup, skill publishing

Install a single category and invoke its skills under that namespace:

/plugin install medsci-analysis@medsci-skills
/medsci-analysis:analyze-stats

All eight plugins share the same repository source, so this groups and enables skills by category — it is not a partial download. The marketplace tracks main, so a plugin's version is its git commit.

Want just one capability? Two skills are also published as focused standalone repos (generated mirrors; this repo stays the source of truth), each installable on its own with /plugin marketplace add Aperivue/<repo>:

• Aperivue/verify-refs — catch fabricated/mismatched citations (PubMed + CrossRef).
• Aperivue/check-reporting — audit a manuscript against the bundled EQUATOR reporting guidelines and risk-of-bias tools.

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Start here: 3 workflows

New users don't need all the skills at once. Most work starts as one of three workflows. Each runs through /orchestrate or by invoking the named skills in order; all outputs require human-expert review.

Workflow A — Manuscript pre-submission audit. Use when a manuscript is nearly ready and you want it checked before a reviewer sees it. Skills: /self-review → /check-reporting → /verify-refs → /sync-submission. In: your manuscript (+ refs.bib, tables/figures). Out: anticipated reviewer comments, an item-by-item reporting-guideline audit, a citation-integrity report, and a submission-package drift check. Safety: it flags issues; you fix and verify them.

Workflow B — Data to manuscript package. Use when you have a cleaned dataset and need a full draft. Skills: /clean-data → /analyze-stats → /make-figures → /write-paper → /check-reporting → /find-journal. In: a cleaned CSV/parquet

• a research question. Out: reproducible analysis code, publication-ready figures, an IMRaD draft, a reporting checklist, and a journal shortlist. Safety: statistics and claims must be verified against your data; the toolkit never fabricates numbers or references.

Workflow C — Systematic review / meta-analysis. Use when you are running an SR/MA. Skills: /meta-analysis (with /search-lit, /make-figures, /check-reporting). In: a research question + search strategy. Out: PROSPERO-style protocol scaffolding, screening/extraction structure, PRISMA-consistent counts and diagram, pooled-estimate figures, and a manuscript draft. Safety: screening and extraction decisions stay with the human review team.

Live Demos: Four Study Types, Four Full Pipelines

Four public datasets. Four study types. Demos 1–3 each produce a complete manuscript, publication-ready figures, and a reporting-compliance audit; Demo 4 runs the medical-AI model-engineering lane end to end (scaffold → gates → training → evaluation → interpretability).

Demo	Dataset	Study Type	Compliance
Demo 1: Wisconsin BC	sklearn built-in	Diagnostic accuracy	STARD 2015
Demo 2: BCG Vaccine	metafor::dat.bcg (13 RCTs)	Meta-analysis	PRISMA 2020
Demo 3: NHANES Obesity	CDC NHANES 2017-18	Epidemiology (survey)	STROBE
Demo 4: PneumoniaMNIST CNN	medmnist (CC BY 4.0)	Medical-AI model engineering (CNN)	CLAIM / TRIPOD+AI

Demo 1: Diagnostic Accuracy — Wisconsin Breast Cancer

from sklearn.datasets import load_breast_cancer
data = load_breast_cancer()  # 569 samples, zero download

Output from orchestrate --e2e (see full demo):

Full output list — manuscript, figures, STARD flow, checklist (click to expand)

Output	Description
Manuscript	IMRAD draft, ~1,800 words
Title Page	STARD title page with key points
DOCX	Submission-ready Word document
ROC Curve	3-model comparison with DeLong 95% CIs
Confusion Matrices	Per-model confusion matrices at threshold 0.5
STARD Flow	D2-generated STARD 2015 flow diagram
Reporting Checklist	STARD 2015 — 60.9% compliance (14/23 applicable)
Self-Review	Initial 82 (REVISE) → 88 (PASS) after 1 fix iteration; final 0 major / 1 minor
Pipeline Log	7-step E2E execution trace

Pipeline: analyze-stats → make-figures → write-paper → AI pattern scan → check-reporting (STARD) → self-review → DOCX build → present-paper

Demo 2: Meta-Analysis — BCG Vaccine Efficacy

library(metafor)
data(dat.bcg)  # 13 RCTs, 357,347 participants (Colditz et al. 1994)

Output from orchestrate --e2e (see full demo):

Full output list — manuscript, forest/funnel plots, PRISMA flow, checklist (click to expand)

Output	Description
Manuscript	Pooled RR = 0.489 (95% CI: 0.344–0.696), ~2,200 words
Title Page	PRISMA title page with key points
DOCX	Submission-ready Word document
Forest Plot	13 studies, RE model (REML), 300 dpi
Funnel Plot	Small-study / publication-bias visual
PRISMA Flow	D2-generated PRISMA 2020 flow diagram
Reporting Checklist	PRISMA 2020 — 57.1% (24/42) at check-reporting → 61.9% (26/42) after self-review fix
Self-Review	Initial 78 → 82 (REVISE) after 1 fix iteration; 3 major / 4 minor (majors are out-of-scope RoB/GRADE/references)
Pipeline Log	7-step E2E execution trace

Pipeline: analyze-stats (R metafor) → make-figures → write-paper → AI pattern scan → check-reporting (PRISMA 2020) → self-review → DOCX build → present-paper

Demo 3: Epidemiology — NHANES Obesity & Diabetes

# Pre-processed NHANES 2017-2018 CSV included
# 5,010 US adults after exclusions

Output from orchestrate --e2e (see full demo):

Full output list — manuscript, OR forest plot, STROBE flow, checklist (click to expand)

Output	Description
Manuscript	Adjusted OR = 3.03 (95% CI: 2.29–4.02), ~1,850 words
Title Page	STROBE title page with key points
DOCX	Submission-ready Word document
OR Forest Plot	Adjusted odds ratios for 7 variables
Study Flow	D2-generated participant flow diagram
Reporting Checklist	STROBE — 83.3% compliance (25/30 applicable)
Self-Review	ACCEPT-WITH-NOTES after 1 fix iteration; 0 genuine majors remaining
Pipeline Log	7-step E2E execution trace

Pipeline: analyze-stats → make-figures → write-paper → AI pattern scan → check-reporting (STROBE) → self-review → DOCX build → present-paper

Project Folder Structure

Each demo (and real project) follows this role-based folder layout:

project/
├── data/                          # Input data
│   └── raw_data.csv
├── analysis/                      # /analyze-stats + /make-figures outputs
│   ├── tables/
│   ├── figures/
│   │   └── _figure_manifest.md
│   ├── _analysis_outputs.md
│   └── analyze.py
├── manuscript/                    # /write-paper outputs
│   ├── manuscript.md
│   ├── manuscript_final.docx
│   └── title_page.md
├── qc/                            # Quality verification
│   ├── reporting_checklist.md     # /check-reporting
│   ├── self_review.md             # /self-review
│   └── _pipeline_log.md
├── submission/                    # Post-journal-selection (manual trigger)
│   └── {journal_short}/
│       ├── cover_letter.md
│       ├── checklist.md
│       └── peer_review.md
└── presentation/
    └── presentation.pptx

The E2E pipeline (orchestrate --e2e) produces everything up to qc/. The submission/ directory is created after journal selection via /find-journal.

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What's New

v5.21 — verification-layer batch, mostly promoted from real submission failures. A marked (tracked-changes) manuscript is now built by driving Word's Compare from the command line and proved by a round trip — accepting every revision must reproduce the revised paper exactly, rejecting every revision must reproduce the original — and the gate is move-aware, because Word encodes a relocated paragraph as w:moveFrom/w:moveTo and an insert-and-delete-only verifier calls a good file corrupt. Every detector's qc/*.json now names the detector that wrote it, so an artifact can be traced back to the check that produced it (a CI-enforced contract). Two /verify-refs precision defects: a Unicode hyphen in a surname fired MISMATCH — the verdict that means fabricated author — on a correct reference, and a Better BibTeX brace-protected surname was read as a corporate author, silently skipping the author check the tool exists to perform. New gates: publisher markup in a .bib title (<scp>WHO</scp> renders as garbage and no gate looked at the printed title), complete/quasi separation before a logistic model is fitted (a pathognomonic sign has an empty cell by construction; glm returns OR = 0.00, p = 0.99, and an AUC that gets reported), and a probe + gate for manuscripts claiming a system improved itself (which rung of the verification hierarchy said so?). /find-cohort-gap now accepts a local codebook — an institutional registry or EMR export, not only a named public cohort — enumerating variables verbatim with provenance rather than letting a model summarise them. Additive and backward-compatible; 55 skills / 46 guidelines / 61 integrity detectors / 23 domain-probe modules. (See the CHANGELOG for v5.0–v5.20.)

v5.20.1 — audit-driven coherence fixes. A real /orchestrate --e2e state-transition bug (the pipeline halted at step 3 because it required a DOCX that is only rendered at step 7), all 55 skills made routable from the single entry point with a CI reachability gate, and a README plugin-count that had drifted from the marketplace SSOT (now gated). No skill/detector change.

v5.20 — reviewer-arithmetic gates. Five deterministic self-review detectors promoted from a peer-review cycle, each recomputing what a manuscript already prints: check_table_percentages (an n (%) cell vs its column denominator), check_nested_group_comparison (a P value comparing an analysed subset against the parent cohort that contains it — nested, invalid), check_reported_p_from_counts (rebuilds each 2×2 row and recomputes Fisher / Pearson χ² ± Yates in pure stdlib, calibrating the family on rows that reproduce), check_dta_denominators (sensitivity/specificity denominators vs the reference-standard category counts, behind a matching grand total), and check_paired_difference_estimator (an odd-n integer-scale median cannot be non-integer; a zero-width CI; an unnamed estimator). Plus /peer-review request-type discipline (disclosure vs computation) and impossibility-claim verification. Additive and backward-compatible; 55 skills / 46 guidelines / 57 integrity detectors / 22 domain-probe modules. (See the CHANGELOG for v5.0–v5.19.)

v5.19 — reviewer-safety + reporting-checklist batch. A PDF hidden-text / prompt-injection guard for /peer-review — a PyMuPDF extractor plus a stdlib detector that catch a review-steering instruction hidden in a submitted PDF (white-on-white text, sub-visible fonts, off-page glyphs, invisible render mode, or a document-metadata field) before an LLM ingesting the text layer can be steered by it, and emit visible-only text to feed a model instead of the raw PDF; plus the TARGET (target-trial emulation; Cashin et al., JAMA 2025) and REMARK (prognostic tumour-marker; McShane et al.) reporting checklists. Additive and backward-compatible; 55 skills / 46 guidelines / 52 integrity detectors / 22 domain-probe modules. (See the CHANGELOG for v5.0–v5.18.)

v5.18 — reliability & workflow-integrity batch. A new deterministic response-claim gate for /revise and /peer-review (verifies a response letter's "we added / we now cite X" against the revised manuscript body — a claimed-but-absent edit is caught before it reaches a reviewer; detectors 50 → 51), a reframe / headline-change survivor scan (--retired-term / --old-value, finds stale framing or superseded values left in the body, supplement, and figure legends after a reframe), a pre-drafting backbone full-text readiness gate for /write-paper (the backbone article is used, not merely detected), a skill-registry consistency validator (capabilities.yml ⇄ skill.yml, CI-enforced), AI-tool citation-framing guidance for /academic-aio, and Demo 4 (PneumoniaMNIST CNN, the model-engineering lane end to end). Additive and backward-compatible; 55 skills / 46 guidelines / 51 integrity detectors / 22 domain-probe modules. (See the CHANGELOG for v5.0–v5.17.)

v5.17 — model-engineering produce-side depth, completion. Deployment safety + the final roadmap/candidate items: a new uncertainty-imaging skill + check_uncertainty_reporting gate (calibrated per-case uncertainty / OOD guard on a held-out set / abstention at a pre-specified target / calibration-under-shift — for a deployment-framed model), an MLOps wiring reference for model-scaffold (experiment tracking, config/data/environment versioning, CI-for-ML — pointing to W&B / MLflow / nnU-Net, reimplementing nothing), and an architecture-zoo graph-neural-net family card (GCN / GraphSAGE / GAT / GIN / BrainGNN for brain connectomes) that closes the last candidate gap. The six-item produce-side depth roadmap and its candidate list are now complete. Additive and backward-compatible; 55 skills / 46 guidelines / 50 integrity detectors / 22 domain-probe modules. (See the CHANGELOG for v5.0–v5.16.)

v5.16 — model-engineering produce-side depth, clinical fine-tuning focus (Items 3–4 of the produce-side depth roadmap). A new radiomics-ml skill + check_radiomics_ml gate for the most common solo-doable clinical-ML workflow — radiomics / tabular features → any classical learner (LASSO / SVM / random forest / XGBoost / …) → a clinical outcome, no GPU — with a learner-agnostic nested-CV / calibration / stability gate; plus a model-scaffold fine-tuning mode (--task finetune + --from-pretrained) that adapts a pretrained backbone on collected clinical data with a frozen→unfrozen schedule, discriminative learning rates, and a pretrained-weight provenance record (PRETRAINED.md + a PRETRAINED_PROVENANCE_MISSING verdict on the existing check_training_hygiene, plus a MedSAM-adaptation + train-only diffusion-augmentation guide). Additive and backward-compatible; 54 skills / 46 guidelines / 49 integrity detectors / 22 domain-probe modules. (See the CHANGELOG for v5.0–v5.15.)

v5.15 — model-engineering produce-side depth. Two new skills that produce what the review lane previously only audited: preprocess-imaging (DICOM/NIfTI data prep + a check_preprocessing_leakage gate that extends the split-leakage moat upstream to the data stage) and explainability (Grad-CAM / saliency held to the reviewer bar — Adebayo sanity checks, quantitative localisation vs ground truth, attribution-not-validation framing, via check_explainability_report). Plus a by-stage skill index, multi-host README/About (Claude Code · Codex · Cursor · Copilot), copy-paste citation ergonomics, and a real-project precision fix. Additive and backward-compatible; 53 skills / 46 guidelines / 48 integrity detectors / 22 domain-probe modules. (See the CHANGELOG for v5.0–v5.14.)

v4.10 — reviewer-coverage expansion reverse-engineered from high-IF, CC-BY papers (learn-only under the reverse_engineer/ license firewall), plus a clinician-friendly update path. Additive and backward-compatible; 45 skills / 46 guidelines / 36 detectors / 15 domain-probe modules (was 12):

• Three new reviewer domain-probe modules (/peer-review + /self-review, vendored byte-identical): Mendelian randomization (MR1–MR8 — IV assumptions, pleiotropy-robust sensitivity suite, Steiger, sample overlap, NLMR, drug-target colocalization), polygenic risk score (PG1–PG8 — ancestry portability, base/target leakage, incremental value over the clinical model, screening-vs-discrimination, calibration), and network meta-analysis (NM1–NM8 — transitivity, incoherence, SUCRA over-interpretation, CINeMA/GRADE-NMA, component-NMA additivity). Plus observational O17 (agnostic many-exposure-scan multiplicity: ExWAS/EWAS/MWAS).
• Two reporting-guideline checklists (36 → 38): STROBE-MR and PGS-RS / PRS-RS, with study-type routing. Four new /analyze-stats analysis guides (multiplicity, MR, PRS, NMA) and a /clean-data implausible-value + cross-field validity reference.
• Clinician-friendly update reminders — the classroom installers enable the in-app "update available" notice + one-click Desktop updater by default; the npx/manual paths print how to turn it on; the install guide recommends npx medsci-skills install --enable-update-notify.

v4.9 — analysis-integrity hardening promoted from real review cycles, plus journal-mechanics additions. Additive and backward-compatible; still 45 skills / 46 guidelines, analysis-integrity detectors 32 → 36:

• Four new gates — a duplicate-bibliography check (check_reference_duplication.py) for the hybrid [@key] + hand-typed ## References build that renders the list twice; a cross-script binning / composite-indicator consistency check (check_binning_consistency.py, BINNING_DRIFT / DERIVED_DEF_DRIFT) for a derived categorical or composite indicator defined inconsistently across analysis scripts; a float citation-order check (check_citation_order.py) for numbered Tables/Figures not first cited in ascending order per series; and an audit-dump leak gate (/sync-submission) that blocks a /check-reporting output mistakenly attached as a submission file.
• KJR technical-check conventions + percentage-decimal style, reader-allocation-under-burden and generative-image-as-study-object reporting (/design-ai-benchmarking, /check-reporting), and a Liver International CSL with that journal's submission mechanics (/manage-refs).

v4.8 is the review-harvest batch — deterministic detector hardening promoted from real-manuscript review cycles. Additive and backward-compatible; still 45 skills / 46 guidelines, analysis-integrity detectors 30 → 32:

• Two new gates — check_supplement_hygiene.py lints the rendered supplement / tables / caption files (not just the manuscript) for §-labels, placeholders, build markers, response-letter framing, and unresolved bodysupplement cross-references; check_null_calibration.py flags a headline negative/equivalence claim made without a minimum-detectable-effect / power / equivalence statement.
• Four detector false-positive fixes — gates no longer fire on a recommended colorblind-safe palette, author-footnote § daggers, a correctly-hedged disclaimer, or a tier-label digit; each with a regression fixture and three newly CI-wired test suites.
• Nine reviewer-side domain probes (SR/MA, observational, diagnostic, AI-overclaiming, survival) plus a /design-study design-stage ceiling gate for perceptual/reader-AI studies and a reusable confidence-weighted-rating→AUC monotonicity template.

v4.7 is the self-update foundation — physician-researchers stay current without GitHub, git, or a terminal. Additive and backward-compatible; still 45 skills / 46 guidelines / 30 detectors:

• Transactional, crash-recoverable installer. Each install runs through a durable journal state machine recovered on the next run (roll back / forward-clean / fail-closed), with per-target SHA-256 inventories — your modified or third-party skills are backed up and never clobbered or auto-deleted.
• One-click self-updater (~/.medsci-skills/updater/, install.py --check-update). Verifies the download against the github.com API digest and never extractall()s (per-entry rejection of traversal / symlink / duplicate / zip-bomb + an allowlist & per-file hash). The release pipeline injects a verified provenance.json, attests build provenance, runs on a protected release environment, and verifies each ZIP round-trips through the updater's own safe-extract before publishing.
• Opt-in update notice (off by default): install.py --enable-update-notify shows a one-line "update available" message at Claude Code session start — no telemetry, reads nothing about your session, installs nothing. --disable-update-notify / MEDSCI_NO_UPDATE_CHECK=1 turn it off. (Honest scope: the digest/attestation detect transport tampering, not a compromised publisher account — see SECURITY.md.)

v4.6 is a maintainability, governance, and review-depth release — still 45 skills / 46 guidelines; analysis-integrity detectors 28 → 30, domain probes 11 → 12:

• Fairness / equity / subgroup-performance probe (EQ0–EQ6) for AI/prediction/diagnostic studies that claim cross-population performance, plus two new detectors: an AI-disclosure + data/code-availability check (/sync-submission) and a structured-summary-box conformance check (/academic-aio).
• Governance + answer-engine layer: ROADMAP.md, MAINTAINERS.md, SECURITY.md, a maintainer workflow + release checklist, an AEO/GEO docs/faq.md, a "Start here: 3 workflows" + "Validation status" section in this README, and a new maturity field (official / experimental / community) on every skill.
• Token diet (pilot): write-paper Phase 7 integrity audits moved to a load-on-demand reference (~2,559 tokens saved per invocation). Positioning now leads with the compliance moat rather than skill count.

v4.5 deepens the review + submission surface with no new skill or reporting-guideline count (still 45 skills / 46 guidelines); analysis-integrity detectors 27 → 28:

• /clean-data + /analyze-stats — reverse-coded-item / negative-alpha detector. A multi-item Likert scale with a negatively-worded item must be recoded (min+max) − x before the scale total or Cronbach's alpha is computed; left un-recoded, the item correlates negatively with the rest of the scale and alpha collapses (often negative). A negative alpha is a coding bug, not a "multidimensional construct." New stdlib-only check_reverse_coding.py returns REVERSE_CODING_LIKELY / REVERSE_CODING_SUSPECT / OK from per-item item-rest correlations + raw alpha; the Likert summary template gains a --reverse-items recode flag.
• /peer-review + /self-review — SR/MA + DTA + prediction-model probe batch. sr_ma.md P12 risk-of-bias table row-sum traffic-light figure-matrix reconciliation and P13 included-study reference-list completeness; diagnostic_accuracy.md D7 index-test-as-enrollment-criterion circularity; clinical_prediction_model.md CP5 intended-use horizon leakage and CP6 development/CV vs held-out/external validation-nomenclature conflation. Vendored byte-identical into /self-review.
• /sync-submission — embedded absolute-path leak scan. A word/*.xml attribute (e.g. a pandoc-embedded image's <pic:cNvPr descr="…">) carrying an absolute home-dir path (/Users/…, /home/…) is a username leak invisible to a rendered-text scan; now flagged as docx_embedded_abs_path under check_asset_anonymization.py.

v4.4 adds reviewer/analysis depth with no new skill or reporting-guideline count (still 45 skills / 46 guidelines / 27 detectors):

• /author-strategy — trajectory-archetype classification (optional). Classifies a queried author's PubMed trajectory into abstract career archetypes (A1 infrastructure builder, A2 methodology rule-maker, A3 clinical→AI hybrid, A4 SR/MA volume engine, A5 large-consortium participation, A6 device/technique depth, + a computed composite) as an explainable, multi-label, confidence-scored heuristic — not an objective verdict. The rubric is a single canonical YAML (the narrative doc is generated from it); scores exclude unavailable signals (h-index/citation/venue-tier → [VERIFY], never fabricated); a disambiguation gate binds an approved corpus_manifest.json to the CSV (csv + PMID-set hashes) so a surname alone never classifies, and target-author attribution never borrows a co-author's ORCID/affiliation.
• /peer-review + /self-review — Image-Synthesis / cross-modality probe (IS1–IS4) for studies that synthesize one imaging modality from another and claim the output carries the target's information, plus a reviewer-side reference-integrity spot-check.
• /verify-refs — OpenAlex tertiary index recovers conference-proceedings / non-DOI citations (NeurIPS/ICLR/ACL) that fall through PubMed and CrossRef, the free analogue of a portal's second index.

v4.3 hardens the cross-sectional / observational cohort review surface end-to-end, much of it reverse-engineered from real CC-BY cohort papers (learn-only under the license firewall) — no new skill or reporting-guideline count (still 45 skills / 46 guidelines); analysis-integrity detectors 25 → 27:

• Observational probes O1 → O14 (/peer-review + /self-review, vendored) — over-adjustment / analysis-unit clustering / outcome construct-validity (O7–O9), overlapping-subset gradient (O10), complex-survey design & weighting for NHANES/KNHANES (O11), data-driven threshold / "inflection-point" mining (O12), cross-sectional mediation temporal-order & sequential-ignorability (O13), and interaction scale — additive RERI/AP/S vs multiplicative (O14). Plus a new clinical-prediction-model probe module CP1–CP4 and survival S9 (panel-data / multistate variance).
• Two new detectors (25 → 27) — check_wordcount_cap.py (the revision-inflation trap: body vs journal cap) and check_paren_spans.py (em-dash→paren conversions that wrap a whole sentence). Plus a check_confounding_completeness.py upgrade (DB-codeprose alias map, SMD-from-mean±SD, exposure-defining-covariate exemption), a check_cohort_arithmetic.py ANALYSIS_UNIT_UNDISCLOSED check, a check_scope_coherence.py cross-sectional-yield lexicon, and a verify-refs corporate/collective-author render-abort fix.
• Analysis & submission tooling — /analyze-stats gains mediation and interaction & effect-modification guides; /sync-submission gains assemble_supplement.py (S{N} indexfile integrity) and a /revise body-word-count exit gate; /render-pdf-doc gains a scan_glyph_coverage.py xelatex silent-glyph-drop scan.

v4.2 builds out the case-report capability end-to-end, grounded in real CC-BY case reports (learn-only under the license firewall) — no new skill or reporting-guideline count (still 45 skills / 46 guidelines); journal profiles 68 → 73:

• Case-report + case-series writing — /write-paper gains a CARE narrative + 150-word-abstract case-report exemplar, a case-series paper type (methods-light mini-cohort, all-cases summary table, counts-not-rates), and adverse-event/pharmacovigilance (Naranjo/WHO-UMC causality) and diagnostic-pitfall/mimic subtypes.
• Radiology / imaging-led track — a dedicated exemplar_case_report_radiology.md (per-modality technique→findings→impression, structured-reporting lexicons BI-RADS/LI-RADS/PI-RADS/TI-RADS/Lung-RADS/O-RADS, quantitative threshold honesty, an interventional-radiology procedure/complication subtype, DICOM de-identification) plus a /make-figures annotated multimodality imaging-panel exemplar.
• Case-report reviewer probe — /peer-review + /self-review ship a vendored case-report domain probe CR1–CR9 (novelty/consent/n=1 causality, case-series design, adverse-event causality, imaging-led discipline).
• Where to submit — compact /find-journal profiles for Journal of Medical Case Reports, Cureus, Radiology Case Reports, BMJ Case Reports, and BJR Case Reports, and /check-reporting CARE notes for adverse-event and case-series subtypes.

v4.1 ships distribution levers and a submission pre-flight gate — analysis-integrity detectors 24 → 25 (still 43 skills):

• Claude Code plugin marketplace — /plugin marketplace add Aperivue/medsci-skills, then /plugin discovery of nine medsci-* category plugins generated from the catalog SSOT (.claude-plugin/marketplace.json).
• MedSci-Audit detector registry — the deterministic verification layer is now a named, enumerated, citable suite (MEDSCI_AUDIT.md + generated metadata/detectors_catalog.json, six audit families).
• Hero-skill standalone mirrors — scripts/sync_hero_skill.py mirrors a focused skill to its own star-funnel repo; first two live: Aperivue/verify-refs and Aperivue/check-reporting.
• Placeholder/marker gate — check_placeholders.py flags leftover [@NEW:] / [version] / TODO / template-URL markers before submission (the 25th detector).
• Submission pre-flight gate — preflight_gate.py bundles the existing detectors + /verify-refs into one halt-on-failure command (qc/preflight_gate_report.json, non-zero exit on any blocker) — the single last step before freeze.

v4.0 extends the project's own deterministic, no-drift SSOT discipline to the public storefront and finishes the detector backlog — bringing the analysis-integrity detector count in skills/ to 24 (still 43 skills):

• SSOT to the storefront — a generated, machine-readable metadata/skills_catalog.json (slug + research-lifecycle category + one-line description per skill) is now the source the aperivue.com storefront vendors, gated offline so the public site can never silently drift behind the repo (gen_skills_catalog_json.py --check).
• Asset/figure anonymization — /sync-submission scans figure-generating scripts, figure-PDF rendered text, and docx/PDF metadata authors for the institution/author leaks a body-text scan misses (check_asset_anonymization.py).
• Cross-artifact staleness — flags supplement values that disagree with the corrected body, and reporting checklists built against an older manuscript version (check_cross_artifact_stale.py; check_checklist_version.py with a target_manuscript/source_sha256 checklist contract).
• Survival reporting — /analyze-stats emits median survival with its 95% CI, a Cox events-per-variable gate, and cluster-robust SE for nested observation units.

v3.8.0 adds an evaluation/ harness suite that validates the instrument itself — deterministic detector recall on programmatically seeded defects (E1), fresh-clone manifest reproducibility (E4), claim audit-trail completeness (E5), host-portability and metadata-drift checks (E6/E7/E8), and a cost/time table (E3) — each writing a self-describing, reproducible run package. An LLM-comparator (E2) and a self-review convergence harness (E9) ship runnable but are NOT executed in this release. This release also reconciles the README Live-Demos numbers with the v3.7.0 clean-room demo artifacts. Catalog unchanged (still 43 skills, 21 detectors).

v3.7.0 adds three deterministic, stdlib-only detectors on top of the v3.6.0 panel-derived gates — bringing the analysis-integrity detector count in skills/ to 21 — without broadening the catalog (still 43 skills):

• Reference adequacy — /self-review and /write-paper now check that a draft cites enough references in the right sections and that every named method (a competing-risk model, multiple imputation, the E-value, an eGFR equation) carries a citation — the adequacy layer that complements /verify-refs's integrity layer (check_reference_adequacy.py).
• Panel lens-diversity — /self-review --panel post-processes its reviewers so the cost buys breadth, not a louder echo (check_panel_diversity.py).
• Generated-code quality — /analyze-stats lints emitted analysis scripts for reproducibility slop (missing seed, hard-coded data literals, absolute paths, in-place source overwrite) (check_generated_code.py).

Plus a publish-time skill-worthiness gate (/publish-skill) and public adoption/impact tracking (IMPACT.md).

v3.6.0 lands 18 gates from a 13-project panel self-review (158 traces → 12 recurring defect patterns), without broadening the catalog (still 43 skills). Six new stdlib detectors join the existing trio — deterministic where a grep is clean, prose/probe where the call needs a human:

• Cohort & pool arithmetic — /self-review recomputes incidence rates from events ÷ person-years, balances STROBE exclusion cascades, and checks ordinal tier/stratum partitions for disjointness (check_cohort_arithmetic.py); /meta-analysis locks patient/lesion aggregate totals and requires re-run evidence for any "fixed" audit note.
• Claim artifact scope — Methods Results disk coverage (a run-but-unreported analysis is flagged), an endpoint conclusion scope gate (a cross-sectional design cannot license a surveillance claim; a binary surrogate is not a care directive), and a reviewer-team 3-way that makes an LLM-as-reviewer fatal.
• Statistical & reporting contracts — a CI/estimand output contract (quantile/proportion/sHR must carry a 95% CI; Cox EPV gate; proportion-MA Egger ban + prediction interval), interval-censoring / PH-violation / CIF-horizon survival rules, reporting-framework base+extension naming, a classical-style body lint, a PROSPERO ID format gate, and a pagination-placeholder citation gate.

Earlier in this series: analysis-integrity guards (confounding completeness, claim-vs-artifact, structural-zero handling), a multi-agent /self-review --panel mode, and shared domain-probe modules vendored byte-identical into /peer-review and /self-review with a CI drift gate.

---

Why This Repo?

	MedSci Skills	Broad skill aggregators
Citation quality	Every reference passes reference-verification gates (PubMed / Semantic Scholar / CrossRef) and citation-audit workflows before inclusion.	No verification -- citations generated from model memory
Pipeline integration	Skills call each other in defined chains. design-study -> calc-sample-size -> write-protocol.	Standalone stubs with no cross-skill interaction
End-to-end coverage	From IRB protocol to journal submission: sample size, data cleaning, analysis, writing, compliance, journal selection, cover letter.	Gaps at every transition -- no protocol, no journal matching, no cover letter
Battle-tested	Used on real manuscript submissions by a practicing physician-researcher	Unknown provenance and validation
Depth per skill	150-600 lines of documentation + bundled reference files (curated journal profile library, checklists, formula sheets, code templates)	Typically thin SKILL.md templates

MedSci-Audit — the verification edge in the first rows above is a named suite of 36 deterministic detectors (citation & reference integrity, cohort & pool arithmetic, scope/estimand contracts, reporting compliance, and more) that catch fabricated or drifted content before a manuscript reaches a reviewer. See MEDSCI_AUDIT.md for the suite, its six families, and its evaluation evidence.

---

What This Is NOT

This is not a broad scientific-tooling library — for cheminformatics, structural biology, or genomics pipelines, see K-Dense scientific-agent-skills. It is not a biomedical-skill aggregator — for a broad curated collection, see OpenClaw Medical Skills. For how MedSci Skills compares to these catalogs, see docs/competitive_positioning.md. For verified cross-agent install paths (Claude Code, Codex, Cursor, GitHub Copilot), see docs/host_compatibility.md.

MedSci Skills is opinionated and narrow on purpose: a single physician-researcher's medical-manuscript pipeline, biased toward radiology, diagnostic accuracy, observational EMR studies, and systematic review / meta-analysis. If you write IMRAD manuscripts for clinical journals, audit reporting compliance against EQUATOR guidelines, or run SR/MA workflows end-to-end, this is built for you. For wet-lab protocols, drug discovery, or single-cell genomics, the repos above are better fits.

---

Skills

Per-skill reference: docs/skills/ — one page per skill (what it does, when it activates, its Quality Card — purpose, safety boundaries, known limitations, validation, evidence — and bundled resources), generated from each SKILL.md + skill.yml. See docs/skills/AUDIT.md for how the skills are validated.

ML / DL method coverage: docs/method_coverage_map.md — which machine-learning and deep-learning method families (CNN / transformer / segmentation / detection / foundation / diffusion / SSL for imaging; the full classical family — penalised regression, SVM, k-NN, trees, boosting [XGBoost / LightGBM / CatBoost], MLP, ensembles, clustering — for radiomics/tabular; LLM/MLLM) map to which skills for selection, production, validation, interpretation, and reporting.

                              ┌─────────────────────────────────┐
                              │  orchestrate: single entry point │
                              │  classifies intent, routes to    │
                              │  the right skill or chains them  │
                              └───────────────┬─────────────────┘
                                              │
                  ┌───────────────────────────┼───────────────────────────┐
                  │                           │                           │
            intake-project              (main pipeline)             grant-builder
            (new/messy projects)              │                    (proposals)
                  │                           │
                  ▼                           ▼
                                    ┌── calc-sample-size ──┐
                                    │                      ▼
ma-scout -> search-lit -> fulltext-retrieval -> design-study ──> write-protocol -> manage-project
   │            │
   │            └── find-cohort-gap (DB variables → literature gap → ranked topic proposals)
   │                                    │
   │                                    ▼
   │                         deidentify -> clean-data -> analyze-stats -> make-figures -> write-paper
   │                                                        │                                │
   │                                           replicate-study (paper → new DB)         humanize
   │                                           cross-national (parallel survey)              │
   │                                           batch-cohort (N × M matrix)                   ▼
   │                                                                          find-journal <── self-review
   │                                                                               │                    │
   │                                                                               │                    ▼
   │                                                                               │          humanize -> academic-aio (AI-search visibility)
   │                                                                               ▼
   │                                                    [cover-letter] -> check-reporting -> revise -> present-paper
   │                                                                                                       │
   └── meta-analysis                                                                                  peer-review
                         lit-sync (Zotero + Obsidian sync)     author-strategy (PubMed profile analysis)

                              ┌─────────────────────────────────────────────┐
                              │  publish-skill: package any skill above for │
                              │  open-source distribution (PII audit,       │
                              │  license check, generalization)             │
                              └─────────────────────────────────────────────┘
                              ┌─────────────────────────────────────────────┐
                              │  add-journal: add new journal profiles to   │
                              │  the database (write-paper + find-journal   │
                              │  dual profile generation with quality gates)│
                              └─────────────────────────────────────────────┘

By research stage

All 55 skills, grouped by where they fit in the clinical-manuscript and medical-AI lifecycle. Full descriptions are in the table below; one page per skill lives in the per-skill reference.

Stage	Skills
Discover & scope	ma-scout · find-cohort-gap · search-lit · fulltext-retrieval · lit-sync · author-strategy
Design & plan	design-study · calc-sample-size · define-variables · write-protocol · fill-protocol · design-ai-benchmarking
Data & analysis	deidentify · clean-data · generate-codebook · version-dataset · analyze-stats · batch-cohort · cross-national · replicate-study
Medical-AI model engineering	preprocess-imaging · architecture-zoo · model-scaffold · model-validation · model-evaluation · uncertainty-imaging · explainability · radiomics-ml · model-card · mllm-eval
Write & visualize	write-paper · make-figures · review-paper · present-paper · humanize · polish-language · academic-aio
Comply & verify	check-reporting · self-review · verify-refs · manage-refs
Submit & respond	find-journal · add-journal · sync-submission · revise · peer-review · fill-icmje-coi
Orchestrate & manage	orchestrate · intake-project · manage-project · meta-analysis · grant-builder · publish-skill · render-pdf-doc · setup-medsci

Available Now

Skill	What It Does
orchestrate	Single entry point for the full bundle. Classifies your request and routes to the right skill -- or chains multiple skills for multi-step workflows. Full Pipeline Mode runs analyze-stats → make-figures → write-paper → check-reporting → self-review end-to-end. --e2e flag for fully autonomous execution with post-skill validation and halt-on-failure.
find-cohort-gap	Research gap finder for longitudinal cohort databases. Profiles cohort strengths, matches PI expertise, scans literature saturation via 6-Pattern scoring, and outputs ranked topic proposals with comparison tables and one-pagers. Works with any cohort: NHIS, UK Biobank, institutional EMR, health checkup registries.
search-lit	PubMed + Semantic Scholar + bioRxiv search with anti-hallucination citation verification. Token-efficient error handling -- CrossRef failures are silently batched, not repeated. BibTeX output tags each entry with verified/verified_by/verified_on fields so downstream skills can trust the citation provenance.
verify-refs	Pre-submission reference audit for .md, .docx, .bib, or .tsv inputs. Extracts references, verifies DOI/PMID via CrossRef/PubMed when available, and writes qc/reference_audit.json as the sole output — row-level status (OK / MISMATCH / UNVERIFIED / FABRICATED) lives inside the JSON records[] block. /search-lit produces candidate BibTeX; /lit-sync owns manuscript/_src/refs.bib.
fulltext-retrieval	Batch open-access PDF downloader. Unpaywall → PMC → OpenAlex → CrossRef pipeline. OA-only -- no paywall bypass. Input: DOI list or TSV. Optional PDF→Markdown conversion via pymupdf4llm for token-efficient LLM analysis of academic papers.
check-reporting	Manuscript compliance audit against 46 reporting guidelines and risk of bias tools (STROBE, STROBE-MR, RECORD, STARD, STARD-AI, TRIPOD, TRIPOD+AI, TRIPOD-LLM, PGS-RS, CHEERS 2022, CROSS, SRQR, COREQ, PRISMA, PRISMA-DTA, PRISMA-P, PRISMA-ScR, MOOSE, ARRIVE, CONSORT, CONSORT-AI, CARE, SPIRIT, SPIRIT-AI, CLAIM, DECIDE-AI, SQUIRE 2.0, CLEAR, GRRAS, MI-CLEAR-LLM, SWiM, AMSTAR 2, QUADAS-2, QUADAS-C, RoB 2, ROBINS-I, ROBINS-E, ROBIS, ROB-ME, PROBAST, PROBAST+AI, NOS, COSMIN, RoB NMA). Machine-readable JSON summary with compliance_pct and fixable_by_ai flags for automated pipeline integration.
analyze-stats	Statistical analysis code generation (Python/R) for diagnostic accuracy, DTA meta-analysis (bivariate/HSROC), inter-rater agreement, survival analysis, demographics tables, regression (logistic/linear), propensity score (matching/IPTW/overlap weighting), and repeated measures (RM ANOVA/GEE/mixed models). Calibration mandatory for prediction models.
meta-analysis	Full systematic review and meta-analysis pipeline (8 phases). DTA (bivariate/HSROC) and intervention meta-analysis. Protocol to submission-ready manuscript with PRISMA-DTA compliance.
make-figures	Publication-ready figures and visual abstracts: ROC curves, forest plots, PRISMA/CONSORT/STARD flow diagrams, Kaplan-Meier curves, Bland-Altman plots, confusion matrices, and journal-specific visual/graphical abstracts (python-pptx template-based). Communication-first design principles (Nat Hum Behav 2026 — key message, audience, cognitive load, figure-vs-table decision) and five flow-diagram production lessons (official-template fidelity, VML fallback PDF export, docx XML escape, sequential placeholder mapping, version freeze); critic rubric Section G adds 5 communication-first checks. --study-type auto-generates the full required figure set; structured _figure_manifest.md output for downstream pipeline consumption; D2 enforced as default for flow diagrams.
design-study	Study design review: identifies analysis unit, cohort logic, data leakage risks, comparator design, validation strategy, and reporting guideline fit.
design-ai-benchmarking	Design and validity review for benchmarking AI system(s) against a human-expert panel: evaluation-question and arm definition, decoupled multi-dimensional rubrics with anchors, planted calibration probes (positive-control / known-bad / instability / mechanism-contradiction), reviewer-panel construction with per-reviewer randomization, inter-rater reliability targets with separate control-item reliability, LLM-as-judge vs human-as-judge adjudication, construct-independence guards, and a structured JSON rating-export schema. Locks the rubric before data collection.
model-validation	Design or audit the clinical-validation study for an engineer-built medical-imaging model (segmentation / classification / detection): patient-level split disjointness and the data-leakage taxonomy, tuning-on-test, internal vs genuine external validation, comparator design, single-run vs multi-seed variance, task-correct metric selection (Metrics Reloaded), test-set sizing, and CLAIM 2024 / TRIPOD+AI / STARD-AI reporting fit. Ships a deterministic split-leakage gate that proves patient disjointness by set arithmetic on the emitted split table. Integrates with MONAI / nnU-Net — does not replace them.
preprocess-imaging	Design or audit the data-preparation stage of a medical-imaging model — DICOM/NIfTI intake, resampling, intensity normalization, and the augmentation plan — so the pipeline is leakage-safe before model-scaffold builds the training repo. Emits a declarative preprocessing manifest and a deterministic data-stage leakage gate (check_preprocessing_leakage) that catches what the split table cannot see: a dataset-level normaliser fit on non-train data (NORMALIZATION_LEAKAGE), a data-fitted transform run before the split (PREPROCESS_BEFORE_SPLIT), and a patient's slices crossing splits (PATIENT_CROSS_SPLIT). Integrates MONAI / TorchIO transforms; never reimplements them or touches real patient data.
model-scaffold	Generate a reproducible, runnable PyTorch training repo for a medical-imaging task — segmentation (U-Net), classification, detection, image-to-image synthesis, self-supervised pretraining, or fine-tuning a pretrained backbone (transfer learning) — the missing middle link between choosing an architecture and validating a trained model. Emits a patient-level seed-locked split as an auditable artifact, a task-appropriate model, train/evaluate scripts that seed every RNG and infer under eval mode, a config, requirements, a reproducibility record, and a Methods stub with VERIFY placeholders (no fabricated numbers). Fine-tuning mode (--task finetune) adds a frozen→unfrozen schedule, discriminative learning rates, and a pretrained-weight provenance record (PRETRAINED.md), with a MedSAM-adaptation + train-only diffusion-augmentation guide. Reproducibility holds by construction; ships a check_training_hygiene AST gate (RNG seeding, eval-mode inference, train-split-only loaders, pretrained-provenance) + a network-free build→validate challenge. Integrates with MONAI / nnU-Net / TorchIO / timm / torchvision for production-grade models.
architecture-zoo	"Which architecture for which research question" decision tool: maps task (classification / segmentation / detection / transfer), modality, data scale, and class imbalance to a paper-grounded architecture shortlist. Curates the foundational curriculum (ResNet / DenseNet / EfficientNet / ViT / Swin; U-Net / 3-D U-Net / Attention & Residual U-Net / nnU-Net / Mask R-CNN; SAM/MedSAM / TotalSegmentator / BiomedCLIP / DINO / MAE / SimCLR) — each with core idea, when-to-use, medical-imaging use, reference implementation, validation setup, and the matching model-scaffold template. Advisory; teaches archetypes, not a live SOTA leaderboard.
model-card	Generate the documentation an engineer-built medical-imaging model must carry — a Model Card (Mitchell et al. 2019), a Datasheet for its dataset (Gebru et al. 2021), and a METRIC-informed data-quality pass — filled from user-supplied facts (never fabricated), then verify every required section is present and non-empty with a deterministic completeness gate (check_model_card_complete). Model Card / Datasheet are documentation standards vendored as templates, not counted reporting checklists.
model-evaluation	Compute and report task-correct held-out metrics for a trained medical-imaging model — segmentation (Dice + a boundary metric HD95/NSD, per structure), classification (AUROC + AUPRC + sensitivity/specificity with bootstrap CIs at the deployment prevalence), or detection (FROC/mAP with a stated IoU criterion) — plus calibration and subgroup slices. Emits a per-case table for analyze-stats and gates the metric choice against Metrics Reloaded / CLAIM 2024 (check_metric_reporting). Numbers come only from executed code.
explainability	Produce or audit the interpretability/explainability analysis of a medical-imaging model — Grad-CAM / Grad-CAM++ / attention-rollout / saliency / integrated-gradients — so it clears the rigor bar: mandatory Adebayo sanity checks (model- and data-randomisation), a quantitative localisation metric against ground truth (IoU / pointing game / Dice) instead of eyeballed examples, a cohort-level result, and attribution framing rather than "proof the model is correct". Emits an explainability-report manifest + a deterministic gate (check_explainability_report): SALIENCY_AS_VALIDATION, NO_SANITY_CHECK, NO_LOCALIZATION_METRIC (Major); INSUFFICIENT_SANITY, CHERRY_PICKED_EXAMPLES, MISSING_METHOD (Minor). Integrates captum / pytorch-grad-cam; never reimplements them or touches real patient data.
radiomics-ml	Produce or audit a radiomics / tabular clinical-ML study — features → random forest / XGBoost / regularised logistic → clinical outcome — the most common solo-doable clinical-ML workflow (no GPU, no engineer). Emits a pipeline manifest + a deterministic rigor gate (check_radiomics_ml): NO_NESTED_CV, HIGH_DIM_LOW_EVENTS, SELECTION_OUTSIDE_CV (Major); NO_FEATURE_STABILITY, NO_CALIBRATION, NO_EXTERNAL_VALIDATION (Minor). pyradiomics/IBSI settings, nested CV with in-fold selection, ICC stability, SHAP, calibration + decision curve, CLEAR/TRIPOD+AI/PROBAST-AI reporting. Integrates scikit-learn / xgboost / pyradiomics; never reimplements them.
uncertainty-imaging	Design or audit the uncertainty / out-of-distribution / selective-prediction layer of a deployment-framed medical-imaging model — so a clinical-use claim carries calibrated per-case uncertainty (MC-dropout / deep ensemble / conformal / Bayesian), an OOD guard validated on a held-out OOD set, an abstention rule at a pre-specified operating point, and calibration checked under distribution shift. Emits an uncertainty manifest + a deterministic gate (check_uncertainty_reporting): POINT_PREDICTION_NO_UNCERTAINTY, CONFORMAL_NO_COVERAGE_VALIDATION, OOD_NO_HELDOUT_SET (Major); ENSEMBLE_NOT_INDEPENDENT, MCDROPOUT_DISABLED_AT_INFERENCE, SELECTIVE_NO_TARGET, NO_CALIBRATION_UNDER_SHIFT (Minor). Integrates MAPIE / captum / OOD scorers; never reimplements them or touches real patient data.
mllm-eval	Model-agnostic evaluation harness (closed API or open weights) for an LLM/MLLM on a clinical task — radiology report generation, VQA, clinical text extraction — covering the adjudicated reference standard, clinical-efficacy metrics (RadGraph-F1 / CheXbert-F1 beyond BLEU/ROUGE), faithfulness/hallucination, pretraining-contamination, prompt sensitivity, and a reader study; gates the plan with check_mllm_eval_completeness and routes the reviewer audit to the MLLM probe.
intake-project	Classifies new research projects, summarizes current state, identifies missing inputs, and recommends next steps.
grant-builder	Structures grant proposals: significance, innovation, approach, milestones, and consortium roles.
present-paper	Academic presentation preparation: paper analysis, supporting research, speaker scripts, slide note injection, and Q&A prep.
publish-skill	Convert personal Claude Code skills into distributable, open-source-ready packages. PII audit, license compatibility check, generalization, and packaging workflow.
write-paper	Full IMRAD manuscript pipeline (8 phases). Outline to submission-ready manuscript with critic-fixer loops, AI pattern avoidance, and journal compliance. Anti-interpretation guardrails in Results; interactive Discussion planning with anchor paper input. Case report mode (CARE 2016, 1000-word short-form). Optional cover letter generation (Phase 8+). LLM Disclosure: auto-generates disclosure statements in Methods, Acknowledgments, and Cover Letter (opt-out via --no-llm-disclosure). --autonomous flag skips all user gates for fully automated manuscript generation; Phase 2 auto-calls /make-figures --study-type with manifest verification; Phase 7 enforces strict sequential QC chain (check-reporting → search-lit → self-review fix loop → DOCX build).
review-paper	Scaffold and draft a literature review — narrative (SANRA), scoping (PRISMA-ScR + JBI), or systematic (PRISMA 2020). Asks for the spine axis (modality / task / lifecycle), builds a 7-part skeleton with a required Intro scope/non-overlap block, per-section summary-table stubs, and an evaluation-metrics critique subsection, then wires reporting/registration and hands off to /self-review (RV1-RV8) → /check-reporting → /verify-refs → /humanize. Never invents citations.
self-review	Pre-submission self-review from reviewer perspective. 10 categories with research-type branching (AI, observational, educational, meta-analysis, case report, surgical). Anticipated Major/Minor format with severity framing and optional R0 numbering for /revise pipeline. --json structured output with fixable_by_ai flags; --fix mode auto-applies text fixes (max 2 iterations).
revise	Response to reviewers with tracked changes. Parses decision letters, classifies comments as MAJOR/MINOR/REBUTTAL, generates point-by-point responses and cover letter.
sync-submission	SSOT-to-submission drift audit and journal package helper. Treats submission/{journal}/ as derived output, records source hashes in .journal_meta.json, and blocks freezing drifted packages.
manage-project	Research project scaffolding and progress tracking. Commands: init, status, sync-memory, checklist, timeline. Backwards submission timelines and pre-submission checklists. init --zotero-collection NAME auto-creates the Zotero collection via pyzotero and wires the library_id/collection_key into the project contract.
calc-sample-size	Interactive sample size calculator with decision-tree guided test selection. Covers 11 designs (diagnostic accuracy, t-test, ANOVA, chi-square, McNemar, logistic regression, Cox regression EPV, survival, ICC, kappa, non-inferiority/equivalence). Generates reproducible R/Python code and IRB-ready justification text.
find-journal	Journal recommendation engine. 2-pass matching: compact profiles for scoring, write-paper profiles for top-5 enrichment. Covers 30+ medical specialties, with a user-local private tier for personal-use profiles. No cached IF/APC -- you verify current metrics at journal sites. Post-rejection re-targeting mode.
add-journal	Add new journal profiles to the database. Extracts metadata from author guidelines, generates both write-paper (detailed) and find-journal (compact) profiles in canonical format with quality gates. Batch mode for adding multiple journals in one session.
deidentify	De-identify clinical research data before LLM-assisted analysis. Standalone Python CLI (no LLM) with 10 country locale packs (kr, us, jp, cn, de, uk, fr, ca, au, in). Detects PHI via regex + heuristics. Interactive terminal review, pseudonymization, date shifting, mapping file generation. Custom locale support via --locale-file.
clean-data	Interactive data profiling and cleaning assistant. Three-stage workflow: profile your CSV/Excel data, flag issues (missing values, outliers, duplicates, type mismatches), then generate cleaning code for approved actions only. PHI/PII safety warnings built-in.
write-protocol	IRB/ethics protocol generator. Produces 4 core sections (Background, Study Design, Sample Size Justification, Statistical Plan) with full prose. 6 remaining sections provided as structured skeletons with TODO markers for institution-specific content. Korea/US/EU regulatory guidance.
replicate-study	Replicate an existing cohort study on a different database. Extracts methodology from a source paper, maps variables via harmonization table, generates analysis code, and produces a replication difference report. Validated on KNHANES/NHANES cross-national replication.
cross-national	End-to-end cross-national comparison study. Variable harmonization, parallel weighted survey analysis (no data pooling), and country-stratified comparison tables. Built-in KNHANES + NHANES coding references.
batch-cohort	Generate N analysis scripts from one validated template × multiple exposure/outcome combinations. The "80-person team" pattern: same method, swap variables only. Self-adjustment prevention, EPV checks, Bonferroni correction, and summary heatmaps. Validated with 18 combinations on KNHANES 2018.
humanize	Detect and remove AI writing patterns from academic manuscripts. Scans for 18 common patterns (significance inflation, AI vocabulary, copula avoidance, etc.) and rewrites flagged passages while preserving technical accuracy. Density target: <2.0 instances per 1000 words.
author-strategy	PubMed author profile analysis. Fetches publication data via E-utilities, classifies study types (GBD, SR/MA, NHIS, AI/ML, etc.), generates 7 visualizations, and produces a strategy report with replication opportunities.
peer-review	Structured peer review drafting for medical journals. Systematic manuscript analysis, journal-specific formatting (RYAI, INSI, EURE, AJR, KJR), conciseness targets (500-800 words), and pre-submission QC checklist. Constructive developmental tone.
ma-scout	Meta-analysis topic discovery and feasibility assessment. Two modes: (A) Professor-first — profile → pillar analysis → MA gaps, (B) Topic-first — question → landscape scan → co-author matching. Multi-source validation (PubMed, PROSPERO, bioRxiv) with realistic k estimation (15-30% discount).
lit-sync	Sync research references from .bib files to Zotero library + Obsidian literature notes. Concept extraction from 10+ literature notes with cross-cutting theme discovery. Works after /search-lit or standalone.
academic-aio	AI search engine (Perplexity / ChatGPT web / Elicit / Consensus / SciSpace) and RAG visibility checklist for medical AI papers. Integrates TRIPOD+AI, CLAIM, STARD-AI, TRIPOD-LLM, DECIDE-AI reporting anchors with generative-engine-optimization (GEO) principles. Covers title, abstract, structured summary boxes (Key Points / Research in Context / Plain-Language Summary), preprints, GitHub README, CITATION.cff, Zenodo, and Hugging Face model/dataset cards. Explicit defense against LLM citation fabrication (Agarwal 2025, Nat Commun). Produces a visible PASS/PARTIAL/FAIL checklist; never applies edits silently. Pairs with write-paper Phase 4/6/7, runs after self-review + humanize.
polish-language	Academic English consistency linting and non-native (ESL) clarity polish. A deterministic linter (lint_consistency.py) flags abbreviation define-once violations, US/UK spelling drift, hyphen-vs-en-dash numeric ranges, P/p case and impossible P = 0.000, hyphenation variants, small-number style, and value/unit spacing — then a gated, style-only clarity pass fixes wording without ever changing numbers, citations, or scientific meaning. Distinct from humanize (AI-tell removal) and check-reporting (guideline items); bundles a reproducible challenge card.
manage-refs	Reference lifecycle as a single skill: citekey .bib validation, journal-CSL pandoc rendering (render_pandoc.sh), manuscript rendered DOCX cross-reference QC (check_xref.py --strict is the submission gate), [N] [@key] marker conversion, and native Zotero CWYW field-code injection for co-author Word workflows. Hybrid 3-phase strategy (pandoc draft → CWYW transition → Zotero CWYW for circulation/revision/submission). Sole writer of manuscript_final.docx and qc/xref_audit.json. Split out of write-paper Phase 7.6 so revise, peer-review, sync-submission, and find-journal can render directly without depending on a sibling skill.
render-pdf-doc	Render non-bibliography academic markdown (proposal, briefing handout, anchor doc, IRB cover, reference table) to publication-quality PDF via pandoc + xelatex with CJK font fallback (Apple SD Gothic Neo on macOS, Noto Sans CJK KR on Linux) and content-proportional pipe-table column widths. Boundary opposite of manage-refs (bibliography-driven). Spun off from write-paper Phase 7.6.
define-variables	Literature-grounded variable operationalization for observational research. Turns a data dictionary plus research question into a citation-backed table of exposure / outcome / covariate definitions, cutoffs, and DB-variable mappings. Tier 0 dictionary-first rule prevents ad-hoc phenotype definitions that invite reviewer rejection. Bridges /search-lit output into /write-protocol Methods.
generate-codebook	Generate a citable data dictionary / codebook from a tabular dataset (CSV/TSV/Excel/Parquet/Stata/SAS). Profiles every variable — role, type, level frequencies, range/quantiles, missingness — into codebook.md + codebook.json. Flags coded variables whose level meanings are unknown as [NEEDS DICTIONARY] rather than guessing them, feeding /define-variables and the dictionary-first workflow.
version-dataset	Dataset version control for reproducibility. Builds a deterministic content-hash manifest (file SHA-256 + tabular schema + per-column value hashes), verifies a later copy to detect drift (schema / row-count / value changes), and diffs two manifests. Locks "which version of the data the results came from"; also reproducibility-locks the bundled demos.
fill-protocol	Fill institutional Word form templates (.doc / .docx) for IRB protocols, ethics applications, grant proposals, and other structured research documents while preserving the original styles, table layouts, fonts, and page geometry. Korean-aware (CJK eastAsia font enforcement, table cantSplit) but works for any-language template. Pairs with write-protocol (content) — fill-protocol renders the content into the institutional template.
fill-icmje-coi	Batch-generate per-author ICMJE Conflict of Interest Disclosure Forms (coi_disclosure.docx) for manuscript submission. Pre-fills all 13 disclosure items as "☒ None" plus the final certification using a synthetic seed template, then clones the seed per author with Date / Name / Manuscript Title replaced. Designed for the common case of hospital-based observational research where no author has real financial conflicts; circulated forms become "reply 변경 없음 + sign" for most authors and only flag those who need to amend.
setup-medsci	Diagnostic checklist for the MedSci Skills runtime. Verifies Python, R, Node, the agent host, Git, Zotero, and configured MCP servers, then prints a pass/fail table with links to the right setup doc for any missing component. Read-only — installs nothing.

Installation

No terminal? Use the classroom installer ZIP. Download, unzip, double-click the installer, then restart your desktop agent app.

Option 1: Classroom installer (recommended for non-programmers)

Windows:

https://github.com/Aperivue/medsci-skills/releases/latest/download/medsci-skills-classroom-windows.zip

macOS:

https://github.com/Aperivue/medsci-skills/releases/latest/download/medsci-skills-classroom-macos.zip

After unzipping:

• Windows: double-click installers/install-windows.cmd
• macOS: double-click installers/install-macos.command

This turnkey install also turns on in-app update reminders and adds an "Update MedSci Skills" Desktop icon, so you are told when a new version ships and can update with one click — no terminal needed (see Updating).

Then restart Claude Code Desktop, Codex Desktop, or Cursor and test with:

MedSci Skills가 설치됐는지 확인하고, 오늘 실습에 쓸 대표 스킬 5개만 보여줘.

Option 2: Install all skills manually

git clone https://github.com/Aperivue/medsci-skills.git
cp -r medsci-skills/skills/* ~/.claude/skills/

Option 3: Install individual skills manually

git clone https://github.com/Aperivue/medsci-skills.git
cp -r medsci-skills/skills/check-reporting ~/.claude/skills/

Option 4: npm / npx (terminal-friendly shortcut)

A convenience wrapper for terminal users — it copies the same skills via the dependency-free Python installer. The canonical install paths remain the plugin marketplace (Option 1's sibling above) and the git clone above; npm is just a shortcut.

npx medsci-skills install            # all hosts (Claude, Codex, Cursor)
npx medsci-skills install --target claude
npx medsci-skills list               # list bundled skills
npx medsci-skills doctor             # quick Node/Python/skill-folder check

Requires Node 18+ and (for install/doctor) python3 on your PATH.

Platform notes

• Claude Code: skills are copied to ~/.claude/skills/ (also read by GitHub Copilot and Cursor).
• Codex: skills are copied to ~/.agents/skills/ (also read by Cursor and GitHub Copilot).
• Cursor: no separate step needed — Cursor reads ~/.claude/skills/ and ~/.agents/skills/ directly. The installer can still write an optional .cursor/rules/ steering rule with --cursor-project.
• See docs/host_compatibility.md for the verified per-host install paths and their official sources.
• Windows users do not need WSL for the basic classroom workflow. Use WSL only for advanced reproducible Linux toolchains.

See docs/classroom_distribution_plan.md and docs/classroom_materials.md for instructor distribution, email templates, and first-class exercises.

Tip: Not sure which skill to use? Start with /orchestrate -- it will classify your request and route you to the right tool.

Updating

MedSci Skills updates often. You do not need GitHub, git, or the command line to stay current.

• One click (recommended for the classroom install). The classroom installer (Option 1) now sets this up for you automatically — it places an updater at ~/.medsci-skills/updater/, drops an "Update MedSci Skills" icon on your Desktop (--desktop-launcher), and turns on the in-app update reminder (below). Double-click the icon: it downloads the latest release from GitHub, verifies it, and re-installs — transactionally, so an interrupted update never corrupts your install.
• Already installed an old copy? Re-download the latest classroom ZIP once and double-click the installer; from then on the one-click updater is in place for every future update.
• Terminal users: npx medsci-skills@latest install always installs the latest.
• Just checking: python3 installers/install.py --check-update reports whether a newer version is available and installs nothing.
• Get reminded (Claude Code): python3 installers/install.py --enable-update-notify (or npx medsci-skills install --enable-update-notify) shows a one-line "update available" notice when a Claude Code session starts. The classroom installer enables this for you; for the npx/manual paths it is off by default (the installer prints how to turn it on). It checks at most once a day, reads nothing about your session, and never installs anything. Turn it off with --disable-update-notify, or silence it with MEDSCI_NO_UPDATE_CHECK=1.
• Claude Code plugin marketplace: third-party marketplace auto-update is off by default — enable it in Claude Code or run a manual plugin update.

Updates connect only to GitHub, send no information about your machine or work, and create no telemetry or tracking. Modified skills are backed up before an update and never auto-deleted. See the update privacy & data notice.

Key Features

Autonomous E2E Pipeline

orchestrate --e2e or write-paper --autonomous runs the full pipeline from data to submission-ready DOCX with bounded validation. Skills pass outputs via structured manifests (_analysis_outputs.md, _figure_manifest.md) and project artifacts (project.yaml, artifact_manifest.json, qc/status.json). If a skill fails to produce expected outputs, the pipeline halts rather than proceeding with missing data. Phase 7 enforces a strict QC chain: AI pattern removal → reporting compliance check → /verify-refs citation audit → numerical claim audit → self-review with auto-fix (max 2 iterations) → DOCX/submission build.

Anti-Hallucination Citations

Every reference produced by search-lit is verified against PubMed, Semantic Scholar, or CrossRef APIs. Existing manuscripts should then run /verify-refs, which writes a visible reference audit and blocks fabricated references before submission. No citation is ever generated from memory alone. API errors are batched silently -- no token waste from repeated failure messages.

Anti-Hallucination Numerical Claims

/meta-analysis Phase 6b, /self-review Phase 2.5a, /revise Step 2.5, and /write-paper Step 7.3a enforce a common 3-layer audit (CSV analysis script manuscript) with primary- source back-checking for pooled estimates and revision-era numbers. Hand-typed numerical matrices without CSV-coordinate comments are flagged as structural risks even when the values are currently correct, since the next revision will re-introduce the same failure mode.

Reference Safety (Phase 1)

Projects declare their source-of-truth layout in SSOT.yaml, and a qc/migration_complete marker gates strict enforcement. /verify-refs is the sole writer of qc/reference_audit.json. The MEDSCI_VERIFY_REFS_MODE env var (auto default, warn, enforce, off) controls behavior — auto blocks only when both SSOT.yaml and the migration marker are present, otherwise warns. Legacy projects freeze as warn-only; new projects opt in via scripts/migrate_project_to_ssot.py. An optional PostToolUse hook (not shipped in this repo — document only) can invoke /verify-refs automatically on manuscript saves for users who install it locally at ~/.claude/hooks/verify-refs-guard.sh; the regression suite (tests/test_phase1c_hooks.sh) runs end-to-end only when that local hook is present and is skipped otherwise. The hook gates saves under */submission/*/manuscript/*.{docx,md} and */revision/R*/…circulation….docx (enforce-eligible), and — added for issue #14 — also warn-only on pre-submission and mentor-circulation drafts that previously skipped the audit entirely: */outgoing/*.{docx,md}, */8_Review_Comments/*/outgoing/*.{docx,md}, and any */circulation/*.{docx,md}. Warn-only patterns surface a missing audit without blocking rapid iteration and never enforce, regardless of SSOT/migration state.

Meta-Analysis Failure Modes

/meta-analysis ships empirical failure-mode references (data integrity, review orchestration, submission package drift, post-submission release ops) with four automation hooks: scripts/prisma_5way_consistency.py (DI-6 PRISMA number consistency), scripts/extraction_consensus_log_init.py (DI-1 dual-extraction scaffold), scripts/tag_cleanup_gate.sh (DI-8 placeholder tag gate), and scripts/verify_package_integrity.py (SPD SHA-256 manifest for submission bundles).

46 Reporting Guidelines & RoB Tools Built-in

check-reporting includes bundled checklists for 46 guidelines and risk-of-bias tools: STROBE, STROBE-MR, RECORD, STARD, STARD-AI, TRIPOD, TRIPOD+AI, TRIPOD-LLM, PGS-RS, CHEERS 2022, CROSS, SRQR, COREQ, PRISMA 2020, PRISMA-DTA, PRISMA-P, PRISMA-ScR, MOOSE, ARRIVE, CONSORT, CONSORT-AI, CARE, SPIRIT, SPIRIT-AI, CLAIM, DECIDE-AI, SQUIRE 2.0, CLEAR, GRRAS, MI-CLEAR-LLM, SWiM, AMSTAR 2, QUADAS-2, QUADAS-C, RoB 2, ROBINS-I, ROBINS-E, ROBIS, ROB-ME, PROBAST, PROBAST+AI, NOS, COSMIN, RoB NMA. Includes Results/Discussion section boundary checks and machine-readable JSON summary for pipeline integration.

Publication-Ready Output

analyze-stats generates reproducible Python/R code for 13 analysis types -- including regression, propensity score, and repeated measures -- with mandatory calibration for prediction models. make-figures produces journal-specification figures (300 DPI, colorblind-safe palettes, proper dimensions), visual/graphical abstracts, and a tool selection guide (D2 for flow diagrams, matplotlib for data plots). --study-type auto-generates the complete figure set for each study design.

Results/Discussion Boundary Enforcement

write-paper enforces strict separation: Results contain only factual findings (no interpretation, no "why"), Discussion uses interactive anchor-paper scaffolding. The critic rubric includes a dedicated Section Boundaries pass/fail gate.

IRB Protocol to Submission in One Pipeline

design-study -> calc-sample-size -> write-protocol gives you an IRB-ready protocol. After data collection: clean-data -> analyze-stats -> write-paper -> self-review -> find-journal -> cover letter. Every transition is a defined skill handoff.

Skills Work Together

Skills call each other. check-reporting invokes make-figures for PRISMA diagrams. write-paper calls search-lit for citation verification. self-review delegates reporting compliance to check-reporting. calc-sample-size output feeds directly into write-protocol's IRB justification section.

Skill boundaries — which to use, and in what order

The skill set is deliberately specialized, not consolidated — each skill owns a distinct artifact or lifecycle step, so the routing stays precise. The boundaries that are easy to confuse:

• Reference pipeline — search-lit (discover candidates) → lit-sync (sole writer of refs.bib, syncs Zotero/Obsidian) → manage-refs (render CSL / inject CWYW / cross-ref QC, sole writer of the rendered DOCX) → verify-refs (read-only audit; never edits refs.bib). They are one pipeline, not four overlapping tools.
• Language passes run in order — humanize (remove AI-writing tells) → polish-language (deterministic ESL/house-style consistency: abbreviations, spelling, en-dashes, p-value case) → academic-aio (AI-search/GEO visibility). Three sequential passes with non-overlapping jobs.
• Manuscript type picks the skill — write-paper (original/IMRAD articles, case reports, MAs) vs review-paper (narrative / scoping / systematic literature reviews) vs revise (reviewer-response + tracked changes). Different structures and reporting guidelines.
• Author vs external reviewer — self-review is your own pre-submission check (anticipated comments); peer-review drafts a journal-facing review as an external reviewer. Same domain probes, different user and output.
• Project entry — intake-project classifies and scaffolds a new or messy folder; orchestrate routes a goal or task ("help me write a paper"). Start with intake-project when you have files but no structure, orchestrate when you have a task but no plan.
• Study design — design-study covers general validity (analysis unit, leakage, comparator, validation) and carries a design-stage ceiling gate for perceptual / observer / reader / visual-Turing-test / image-provenance studies; design-ai-benchmarking specializes in AI-vs-human-expert evaluation (rubrics, calibration probes, LLM-as-judge).
• Content vs template — write-protocol drafts IRB/ethics scientific content; fill-protocol renders that content into an institutional Word template without breaking its formatting.

Validation status — available vs CI-gated vs evaluated

Be precise about what "validated" means here — the three tiers are different facts:

• Available — every bundled skill and deterministic detector. The current totals are the single source of truth in metadata/catalog_counts.json and MEDSCI_AUDIT.md.
• CI-gated — detectors with a committed challenge/regression test that runs on every push via validate.yml.
• Formally evaluated — the subset measured by the canonical evaluation harness E1 in evaluation/, which is v3.8-era and validates the then-current detector subset; detectors added since are CI-tested, not yet E1-evaluated (the size of the catalog and the size of the evaluated subset are deliberately reported as separate facts — see MEDSCI_AUDIT.md).

The toolkit is designed to reduce common manuscript-preparation errors; it does not guarantee correctness and is not clinically validated.

Setup

New to Python, R, or the command line? The full step-by-step guide for clinicians is in docs/setup/:

• Mac setup — Homebrew → Python 3.11 → R → Node → Claude Code (~30 min)
• Windows setup — winget-based, no WSL required
• MCP server setup — Zotero, Google Drive, PubMed integration
• Common issues — top 10 fixes (PATH, Apple Silicon, antivirus, JSON syntax)

Verify your environment with the diagnostic skill (read-only, installs nothing):

/setup-medsci

Prints a checklist showing which components are present, which are missing, and which doc to follow for any gap.

Requirements

• An Agent Skills-compatible host — Claude Code (primary), or Codex / Cursor / GitHub Copilot (see docs/host_compatibility.md; some live-data workflows rely on Claude MCP servers)
• Python 3.9+ (for statistical analysis and figure generation)
• R 4.0+ with meta (>=7.0), metafor (>=4.0), mada (>=0.5.11) packages (for meta-analysis)

Use Cases

"I have data and want a complete manuscript with zero manual steps."

/orchestrate --e2e      # Autonomous: analyze → figures → write → QC → DOCX

Or equivalently: /write-paper --autonomous if analysis and figures already exist.

"I have a diagnostic accuracy study draft and need to check compliance."

/design-study          # Review study design for leakage and bias
/analyze-stats         # Generate DTA statistics (sensitivity, specificity, AUC with CIs)
/make-figures          # Create ROC curve + STARD flow diagram
/check-reporting       # Audit against STARD checklist

"I'm starting a meta-analysis and need to find relevant studies."

/search-lit            # Systematic search across PubMed + Semantic Scholar
/fulltext-retrieval    # Batch download open-access PDFs for included studies
/meta-analysis         # Full DTA or intervention MA pipeline
/make-figures          # Forest plot + PRISMA flow diagram
/check-reporting       # Audit against PRISMA-DTA checklist

"I need to present a paper at journal club."

/present-paper         # Analyze paper, find supporting refs, draft speaker script

"I need to submit an IRB protocol for a new study."

/search-lit            # Background literature for rationale
/design-study          # Validate study design, identify bias risks
/calc-sample-size      # Power analysis with IRB justification text
/write-protocol        # Generate 4 core sections + 6 skeleton sections

"I have an interesting case to publish."

/write-paper           # Case report mode (CARE 2016, 1000-word short-form)
/self-review           # Pre-submission self-check
/find-journal          # Which journal accepts case reports in this field?

"My paper was rejected. Where else should I submit?"

/find-journal          # Exclude rejected journal, recommend alternatives
/write-paper           # Generate new cover letter (Phase 8+)

"I have messy clinical data that needs cleaning before analysis."

/deidentify            # Remove PHI from clinical data (standalone Python, no LLM)
/clean-data            # Profile dataset, flag issues, generate cleaning code
/analyze-stats         # Run statistics on cleaned data
/make-figures          # Publication-ready figures

"I want to write a grant proposal for a radiology AI project."

/design-study          # Validate study design before writing
/grant-builder         # Structure significance, innovation, approach
/search-lit            # Find supporting literature with verified citations

Contributing

Contributions are welcome — and most are one small, self-contained file that a template walks you through. You do not need to understand the whole pipeline to add value. Pick a good first issue, or start from one of these:

Want to add…	How	Issue
A CSL citation style for a journal that lacks one	drop one .csl into manage-refs/citation_styles/	#117
A de-identification locale pack for one more country	add one patterns file under deidentify/	#116
A README translation (e.g., zh-CN)	one translated README file	#119
A figure exemplar (ROC, KM, forest, Bland–Altman, confusion matrix…)	one make-figures/references/exemplar_plots/*.md anatomy model	#118
A journal profile (submission rules for a journal we don't cover)	/add-journal, or copy an existing journal_profiles/*.md	#115
A reporting checklist or peer-review exemplar	one reference file in the matching skill	#120

Each of these adds exactly one new file — none requires editing a count, a generated page, or the build config, and the catalog-consistency check auto-derives profile counts from disk so it won't flag you. No assignment needed: just open a PR — the first one that passes CI wins, and a maintainer handles any bookkeeping in review. See the 5-minute first-PR quickstart to get going.

Every contribution is gated the same way the maintainers are: it must be a self-contained file, pass the CI (validate.yml — PII scan, structure, catalog consistency), and carry no patient or author identifiers. See CONTRIBUTING.md for the PR checklist and the PII/publication hygiene rules. New ideas that don't fit a template? Open a skill request or a detector request.

Governance: ROADMAP.md (priorities + scope boundary), MAINTAINERS.md (roles — clinical authority stays with the founder), docs/maintainer_workflow.md (review + release process), and SECURITY.md (vulnerability reporting + the medical-scope boundary). A change that touches a medical/research claim needs Clinical-Lead review. Community members who have reported, verified, or shaped the project are credited in CONTRIBUTORS.md.

In the Wild

Adoption is tracked openly in IMPACT.md (stars, forks, traffic, release downloads — snapshotted weekly into metrics/traffic_log.csv) and academic use is logged in docs/citations.md.

Used MedSci Skills in your research? Please let us know. It helps other researchers find the toolkit — and we list it (with your permission).

Citation

If MedSci Skills helped produce your manuscript, protocol, or analysis, please cite it — software citation is how a tool like this earns academic recognition, and it takes one line.

In your manuscript (Methods or Acknowledgements — cite the version you actually used):

Reporting-guideline compliance, reference verification, and pre-submission integrity checks were assisted by MedSci Skills (version X.Y.Z; https://github.com/Aperivue/medsci-skills; archived at Zenodo, https://doi.org/10.5281/zenodo.20155321).

BibTeX (the software, and the preprint describing its design):

@software{nam_medsci_skills,
  author    = {Nam, Yoojin},
  title     = {{MedSci Skills: Claude Code Skills for the Medical Research Lifecycle}},
  year      = {2026},
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.20155321},
  url       = {https://github.com/Aperivue/medsci-skills}
}

@article{nam2026agentic,
  author  = {Nam, Yoojin and Jeong, Jinhoon and Kim, Namkug},
  title   = {{Deterministic Integrity Gates for LLM-Assisted Clinical Manuscript
             Preparation: An Auditable Biomedical Informatics Architecture}},
  year    = {2026},
  journal = {arXiv preprint arXiv:2606.09500},
  url     = {https://arxiv.org/abs/2606.09500}
}

The Zenodo concept DOI 10.5281/zenodo.20155321 always resolves to the latest release; CITATION.cff carries the machine-readable metadata (GitHub's "Cite this repository" button reads it).

Used it in published or in-review work? Tell us via the "Used in research" issue template — with your permission it is added to docs/citations.md.

Disclaimer

These skills are research productivity tools. They do not provide clinical decision support, medical advice, or diagnostic recommendations. All outputs should be reviewed by qualified researchers before use in any publication or clinical context.

Acknowledgements

• make-figures Critic Loop is inspired by PaperBanana (Zhu et al., Automating Academic Illustration for AI Scientists, arXiv:2601.23265, 2025) and by prior self-refinement research — Self-Refine (Madaan et al., 2023), Reflexion (Shinn et al., 2023), and Constitutional AI (Anthropic, 2022). The implementation in this repository is a clean-room reconstruction specialized for medical publication figures; no code, prompts, or configurations are derived from PaperBanana's repository.
• Reporting-guideline checklists bundled with check-reporting are redistributed under their original Creative Commons licenses (see each checklist for attribution).
• Wong colorblind-safe palette: Wong B. Points of view: Color blindness. Nature Methods 8:441 (2011).

License

MIT License. See LICENSE for details.

Bundled reporting guideline checklists retain their original Creative Commons licenses. See each checklist file for attribution.

Optional dependency: pdf_to_md.py uses pymupdf4llm (AGPL-3.0). Not bundled -- installed separately by the user via pip install pymupdf4llm.

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About

Built by Aperivue -- tools for medical AI research and education.

Runs anywhere the Agent Skills standard is supported -- not Claude Code alone. Claude Code, OpenAI Codex, Cursor, and GitHub Copilot are all verified against each host's own docs: one install, four hosts, no per-host fork (host compatibility).

If you find this useful, consider giving it a star. It helps other researchers discover these tools.