MedSci Skills

56 skills that actually work. Built by a physician-researcher, tested on real publications.

MedSci Skills is an end-to-end research tool for physician and medical-engineering researchers — design → scaffold → validate → publish — for the clinical manuscript and the medical-AI model behind it. Its moat is the compliance layer — 46 reporting guidelines and risk-of-bias tools, reference/citation verification, and deterministic integrity gates before peer review — now extended by a model-engineering lane that scaffolds reproducible, leakage-safe training repos and audits model validation. Clinical AI model research engineering is in scope; a general AI-scientist platform is not. It competes on clinical submission reliability, not skill count.

Topic Discovery → Literature Search → Full-Text Retrieval → Study Design → Sample Size → Protocol → De-identification → Data Cleaning → Statistics → Figures → Writing → Humanize → Compliance → Journal Selection → Peer Review → Revision → Presentation

Created & maintained by Yoojin Nam, MD Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea

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.

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

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.

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 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):

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):

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):

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.

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 body↔supplement 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-code↔prose 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} index↔file 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 Cas