Aperivue
OrganizationAgent Skills for medical research — literature search, reporting-guideline & citation checks, statistics, publication figures, submission. Works with Claude Code, Codex, Cursor & GitHub Copilot. Built by a physician-researcher, tested on real publications. MIT.
Categories
Indexed Skills (52)
analyze-stats
Statistical analysis for medical research papers. Generates reproducible Python/R code with publication-ready tables and figures. Supports diagnostic accuracy, inter-rater agreement, meta-analysis, survival analysis, survey data, group comparisons, regression, propensity score, and repeated measures.
check-reporting
Check manuscript compliance with medical research reporting guidelines. Supports 47 guidelines including STROBE, STROBE-MR, RECORD, REMARK (prognostic tumor-marker studies), TARGET (target trial emulation), GATHER (burden-of-disease / health-estimate modeling), CONSORT, CONSORT-AI, STARD, STARD-AI, TRIPOD, TRIPOD+AI, TRIPOD-LLM, PGS-RS, ARRIVE, PRISMA, PRISMA-DTA, PRISMA-P, PRISMA-ScR (scoping reviews), CARE, SPIRIT, SPIRIT-AI, CLAIM, DECIDE-AI, MI-CLEAR-LLM, SQUIRE 2.0, CLEAR, MOOSE, GRRAS, SWiM, AMSTAR 2, CHEERS 2022, CROSS (survey studies), SRQR and COREQ (qualitative research), and risk of bias tools (QUADAS-2, QUADAS-C, RoB 2, ROBINS-I, ROBINS-E, ROBIS, ROB-ME, PROBAST, PROBAST+AI, NOS, COSMIN, RoB NMA). Generates item-by-item assessment with PRESENT/MISSING/PARTIAL status.
humanize
Detect and remove AI writing patterns from academic manuscripts and response-to-reviewers letters. Scans for 27 common AI-generated text patterns and rewrites flagged passages to sound naturally human-written while preserving technical accuracy, bounding how much of the text a rewrite is allowed to touch.
make-figures
Generate publication-ready figures and visual abstracts for medical research papers. Supports ROC curves, forest plots, CONSORT/STARD/PRISMA flow diagrams, calibration plots, Kaplan-Meier curves, Bland-Altman plots, confusion matrices, pipeline diagrams, and journal-specific visual/graphical abstracts (python-pptx template-based).
meta-analysis
Systematic review and meta-analysis pipeline for medical research. Covers protocol registration (PROSPERO), search strategy, screening, data extraction, risk of bias assessment (QUADAS-2/ROBINS-I), statistical synthesis (bivariate/HSROC for DTA, random-effects for intervention), and PRISMA-compliant reporting. Supports both DTA and intervention meta-analyses.
orchestrate
General-purpose research orchestrator. Routes ambiguous or multi-step requests to the right skill(s) from the medsci-skills bundle. Use when the user describes a research goal without naming a specific skill, or when a task spans multiple skills.
peer-review
Peer review assistant for medical journals. Generates structured review drafts with journal-specific formatting. Constructive developmental tone with systematic manuscript analysis.
self-review
Pre-submission self-review for the user's own manuscripts, applying a reviewer perspective. Systematic check across 10 categories with research-type branching. Outputs Anticipated Major/Minor Comments with severity framing and optional R0 numbering for /revise pipeline integration.
profile-imaging
Profile a medical-imaging dataset before any modelling decision is made — the acquisition grid, voxel spacing and orientation spread, the intensity domain, which label values are actually present, how much of the volume the target occupies, and how large the target is in millilitres — then gate that profile against the researcher's declared plan. Catches, at the point where it is still cheap, the dataset facts that otherwise surface after a training run: a "test set" that carries no ground truth, labels whose grid does not match their image, a stray label index, a target occupying a fraction of a percent while accuracy is planned as a metric, and acquisition heterogeneity nobody declared a resampling decision for. Emits a dataset-profile JSON and a deterministic gate that reads it (stdlib-only, so an audit travels with the JSON). It describes the data and audits the plan against it; it does not preprocess, split, or train.
polish-language
Academic English consistency linting and non-native (ESL) language polish for medical manuscripts. Deterministically flags abbreviation define-once violations, US/UK spelling drift, hyphen-vs-en-dash numeric ranges, P/p case, hyphenation variants, small-number style, and value/unit spacing, then guides a style-only clarity pass that never alters numbers, citations, or scientific meaning. Distinct from humanize (AI-tell removal) and check-reporting (guideline items).
revise
Parse peer reviewer comments and generate a structured Response to Reviewers document with tracked manuscript changes. Classifies comments as MAJOR/MINOR/REBUTTAL, coordinates new analyses with /analyze-stats and /make-figures, and produces cover letter for editor.
preprocess-imaging
Design or audit the data-preparation stage of a medical-imaging model — DICOM/NIfTI intake, resampling and intensity normalisation, 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 that catches the leaks a split table cannot see: a dataset-level normaliser fit on non-train data, any data-fitted transform run before the split, and the same patient's slices crossing splits. Integrates MONAI / TorchIO transforms; it does not reimplement them, and it never runs preprocessing on real patient data.
model-sourcing
Vet the concrete third-party model a study will be built on — this repository, this revision, this checkpoint — not the architecture family. Records a model dossier (source and version pin, licence and the file it was read from, intended use, pretrained-weight provenance, model task vs study task, reported validation, what the model was developed on, your evaluation arms) and gates it deterministically. Catches what a licence check and a citation count cannot: an evaluation arm sitting on the benchmark the model was developed or tuned on, so the arm reads like validation while being closer to a training-set score. Also an evaluation set inside a pretraining corpus, an unstated or use-incompatible licence, an unpinned revision, and a hardware claim never executed. It vets an artifact; it never downloads or runs one.
architecture-zoo
Choose a model architecture for a medical-imaging research question before scaffolding. Maps the task (classification, segmentation, detection, transfer), modality and dimensionality, labelled-data scale, and class imbalance to a shortlist of architectures, each grounded in its source paper with a when-to-use, a medical-imaging use, a reference implementation, the typical validation setup, and the matching model-scaffold template. Covers the foundational curriculum (ResNet, DenseNet, EfficientNet, ViT, Swin; U-Net, 3-D U-Net, Attention/Residual U-Net, nnU-Net (+ ResEnc/MedNeXt/STU-Net), Mask R-CNN; SAM/MedSAM, nnInteractive/VISTA3D interactive-3D, TotalSegmentator, BiomedCLIP, DINO/MAE/SimCLR; and graph neural nets — GCN/GraphSAGE/GAT/GIN/BrainGNN — for brain connectomes). It teaches archetypes and the task-to-architecture logic (including the "scale the CNN, new≠better" rigour caveat), not a live SOTA leaderboard.
calc-sample-size
Interactive sample size calculator for medical research. Decision-tree guided test selection, reproducible R/Python code, effect size interpretation, and IRB-ready justification text. Supports diagnostic accuracy, agreement, proportions, continuous outcomes, survival, ANOVA, logistic regression, and non-inferiority/equivalence designs.
design-study
Study design and validity review for radiology and medical AI research. Identifies analysis unit, cohort logic, leakage risks, comparator design, validation strategy, and reporting guideline fit before drafting or submission.
academic-aio
Medical AI paper optimization for AI search engines (Perplexity, ChatGPT web, Elicit, Consensus, SciSpace) and RAG-based literature tools. Applies when drafting or reviewing titles, abstracts, structured summary boxes (Key Points / Research in Context / Plain-Language Summary), manuscripts for high-impact medical AI journals (Lancet Digital Health, Radiology, Radiology-AI, npj Digital Medicine, Nature Medicine), preprints (medRxiv/arXiv), GitHub README + CITATION.cff + Zenodo archives, and Hugging Face model/dataset cards. Integrates TRIPOD+AI, CLAIM 2024, STARD-AI, TRIPOD-LLM, DECIDE-AI reporting requirements with generative engine optimization (GEO) principles. Produces a visible pass/fail checklist.
add-journal
Add a new journal to the MedSci Skills profile database. Extracts metadata from author guidelines, generates write-paper (detailed) and find-journal (compact) profiles in canonical format with quality gates.
author-strategy
PubMed author profile analysis. Author name → PubMed fetch → study-type classification → visualization → strategy report → optional trajectory-archetype classification.
batch-cohort
Generate N analysis scripts from a single methodology template × multiple exposure/outcome combinations. The "80-person team" pattern — same validated method, swap variables only. Produces batch R/Python code + summary matrix.
clean-data
Interactive data profiling and cleaning assistant for medical research. Three-stage workflow (profile, flag, code-generate) with user approval gates at each step. Handles missing values, outliers, duplicates, and type mismatches in CSV/Excel clinical data. Does NOT auto-clean — all decisions require researcher confirmation.
contribute
Offer your local changes back to the project — a journal profile you added, a checklist item you fixed, a skill you adapted to your department — as a pull request or an issue, without ever typing a git command. Detects what you changed against the installed version, scans it for patient data and identifiers, shows you every line, and sends nothing until you confirm. Also files feedback: a detector that fired wrongly, a step that failed on your file.
cross-national
End-to-end cross-national comparison study using KNHANES + NHANES + CHNS (or other parallel surveys). Variable harmonization, parallel weighted analysis, and comparison tables. Supports 2-country (KR+US) and 3-country (KR+US+CN) designs.
define-variables
Literature-grounded variable operationalization for observational research. Turns a data dictionary + research question into a citation-backed table of exposure/outcome/covariate definitions, cutoffs, and DB variable mappings. Prevents ad-hoc phenotype definitions that invite reviewer rejection. Bridges /search-lit output into /write-protocol Methods.
deidentify
De-identify clinical research data before LLM-assisted analysis. Standalone Python CLI detects PHI via regex + heuristics with 10 country locale packs (kr, us, jp, cn, de, uk, fr, ca, au, in). Interactive terminal review. No LLM touches raw data — the script runs locally without any network or AI calls.
design-ai-benchmarking
Design and validity review for studies that benchmark one or more AI systems against a human-expert panel as the reference. Covers the evaluation question and arm definition, decoupled multi-dimensional rubrics with anchors, planted calibration probes, reviewer-panel construction, inter-rater reliability targets, LLM-as-judge versus human-as-judge adjudication, construct-independence guards, and a structured rating-export schema. Use before data collection on an AI-vs-expert evaluation.
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 a reviewer expects: 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 rather than cherry-picked cases, and attribution framing rather than "proof the model is correct". Emits an explainability-report manifest and a deterministic rigor gate. Integrates captum / pytorch-grad-cam; it does not reimplement them, and never runs a model on real patient data.
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" + final certification ☒ using a synthetic seed template shipped with the skill, then clones the seed per author with Date, Name, and Manuscript Title replaced. Designed for the common case of hospital-based observational research where no author has real financial conflicts; the circulated forms become "reply 'no changes' + sign" for most authors and only flag those who need to amend.
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. Pairs with write-protocol — write-protocol drafts the scientific content, fill-protocol renders it into the institutional template. Korean-aware (CJK eastAsia font enforcement, table cantSplit) but works for any language template.
find-cohort-gap
Research gap finder for longitudinal cohort databases. Profiles cohort strengths, matches PI expertise, scans literature saturation, and outputs ranked topic proposals with gap evidence. Works with any cohort: NHIS, UK Biobank, institutional EMR, health checkup registries, or disease-specific registries.
find-journal
Journal recommendation engine for medical manuscripts. 2-pass matching against a curated public profile library plus any user-local private profiles, enriched with detailed write-paper profiles for top-5 output. Returns ranked recommendations with scope fit rationale, AI disclosure policy, and homepage links. Impact-factor and APC figures in a profile are point-in-time and may be stale — verify current metrics at the journal site. A pre-ranking acceptance-readiness pre-flight scans the manuscript for design-ceiling, unfixable-defect, and importance-risk signals to add an acceptance-feasibility axis alongside scope fit, and the output includes a reject-fallback cascade plan.
fulltext-retrieval
Batch download open-access PDFs by DOI using legitimate OA APIs (Unpaywall, PMC, OpenAlex, Crossref). Optional PDF→Markdown conversion for token-efficient LLM analysis.
generate-codebook
Generate a citable data dictionary / codebook from a tabular dataset (CSV/TSV/Excel/Parquet/Stata/SAS). Profiles every variable — role, type, units placeholder, level frequencies, range/quantiles, missingness — and emits 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.
grant-builder
Grant and challenge proposal support for radiology and medical AI projects. Structures significance, innovation, approach, milestones, and consortium roles while keeping claims evidence-based and executable.
intake-project
Intake and normalize a new radiology research project. Classifies project type, summarizes current state, identifies missing inputs, recommends next steps, and scaffolds lightweight project memory files.
lit-sync
Sync research references from .bib files to Zotero library + Obsidian literature notes. Extract cross-cutting concept notes when enough literature accumulates. Works after /search-lit or standalone.
ma-scout
Meta-analysis topic discovery and feasibility assessment. Professor-first (profile → gap) or Topic-first (question → gap → co-author). Pre-protocol phase from idea to ranked topic list.
manage-project
Research project management for medical manuscripts. Scaffold project structure, track writing progress across phases, maintain project memory files, generate submission checklists and backwards timelines. Commands: init, status, sync-memory, checklist, timeline.
manage-refs
Cross-cutting reference manager for medical manuscripts. Single entry point for citation-key validation, journal-CSL pandoc rendering, manuscript ↔ DOCX cross-reference QC, marker conversion (``[N]`` ↔ ``[@key]``), and native Zotero CWYW field-code injection. Replaces the inline reference-handling that previously lived in ``/write-paper`` Phase 7.6 and is reused by ``/revise``, ``/peer-review``, ``/sync-submission``, and any skill that produces a journal submission. Audit-only verification stays in ``/verify-refs`` — this skill writes (renders, injects, converts); that skill only reads.
mllm-eval
Design or audit a model-agnostic evaluation harness for an LLM or multimodal LLM on a clinical task (radiology report generation, visual question answering, clinical text extraction/classification) — the adjudicated reference standard, clinical-efficacy metrics (RadGraph-F1 / CheXbert-F1 beyond BLEU/ROUGE), faithfulness and hallucination, pretraining-contamination of public benchmarks, prompt-sensitivity and determinism, answer-matching, and a reader study — and gate the plan for those axes. Works on a closed API or open weights. Never fabricates outputs or scores, and never reports n-gram overlap as clinical correctness.
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, then verify every required section is present and non-empty before the card ships to a repo, Hugging Face card, or manuscript supplement. Never fabricates numbers, provenance, consent, or licence; unfilled fields stay flagged. Ships a deterministic completeness gate. Model Card and Datasheet are documentation standards vendored here as templates, not counted reporting checklists.
model-evaluation
Compute and report task-correct held-out metrics for a trained medical-imaging model — segmentation (Dice plus a boundary metric such as HD95 or NSD, per structure), classification (AUROC plus AUPRC and sensitivity/specificity with bootstrap CIs at the deployment prevalence), detection (FROC or mAP with a stated IoU criterion), interactive/promptable segmentation (the interaction-count, convergence, and per-case-time axes a static Dice omits), or generative/synthesis image evaluation (similarity plus the downstream-task efficacy similarity alone cannot establish) — plus calibration and subgroup slices. Emits a per-case results table that analyze-stats turns into publication tables, and gates the metric choice against Metrics Reloaded, CLAIM 2024, and Park et al. 2024 (no pixel accuracy for segmentation, no bare accuracy under imbalance, no static Dice for an interactive method, no similarity-only claim for a generative model). Numbers come only from executed code, never hand-typed.
model-scaffold
Generate a reproducible, runnable PyTorch training repo for a medical-imaging task — segmentation, 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 and 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 adds a frozen-then-unfrozen schedule, discriminative learning rates, and a pretrained-weight provenance record. The reproducibility guarantees hold by construction, so the build is leakage-safe before any training runs. Integrates with MONAI, nnU-Net, TorchIO, timm, and torchvision — it does not reimplement them.
model-validation
Design or audit the clinical-validation study for an engineer-built medical-imaging model (segmentation, classification, or detection) before the validation report or manuscript is written. Covers patient-level split disjointness and the data-leakage taxonomy, tuning-on-test, internal versus genuine external validation, comparator design, single-run versus multi-seed variance, task-correct metric selection, 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-assignment table. Does not build or train models — it integrates with MONAI / nnU-Net, it does not replace them.
present-paper
Academic presentation preparation — paper-driven (journal club, grand rounds, seminar) and lecture/teaching decks (course material, workshop slides, conference talks). Analyzes source material, finds supporting references, drafts audience-adapted speaker scripts, generates or augments PPTX with speaker notes, and prepares Q&A.
publish-skill
Convert a personal agent skill into a distributable, open-source-ready skill. Runs PII audit, generalization, license compatibility check, cross-platform adapter review, and packaging workflow.
radiomics-ml
Produce or audit a radiomics / tabular clinical-ML study — imaging or clinical features → any classical learner (penalised logistic [LASSO / ridge / elastic-net], SVM, k-NN, naive Bayes, LDA/QDA, decision tree, random forest, gradient boosting [XGBoost / LightGBM / CatBoost], shallow MLP, stacked ensembles) → a clinical outcome — so it clears the rigor bar reviewers expect: nested cross-validation (tuning never on the reported folds), dimensionality control for the features-far-exceed-events regime, feature selection inside the fold, feature-stability (ICC / test-retest) filtering, calibration, and external/temporal validation. The deterministic gate is learner-agnostic (it audits the pipeline, not the algorithm). Emits a pipeline manifest and the gate. The most common solo-doable clinical-ML workflow — no GPU, no engineer. Integrates scikit-learn / xgboost / lightgbm / catboost / pyradiomics; it does not reimplement them.
render-pdf-doc
Render academic Markdown documents (English or Korean) to publication-quality PDF via pandoc + xelatex. Targets non-bibliography artifacts: research proposals, IRB cover letters, briefing handouts, anchor docs (Q&A grids), and reference tables. Auto-infers pipe-table column widths from content (label column shrinks to fit, data columns share remaining width). CJK-aware font fallback for Korean text (Apple SD Gothic Neo on macOS, Noto Sans CJK KR on Linux). NOT for: manuscripts with bibliography (use /manage-refs render_pandoc.sh), Word form filling (/fill-protocol), figures (/make-figures).
replicate-study
Replicate an existing cohort study's methodology on a different database. Extracts study design from a source paper, maps variables to the target DB via harmonization table, generates analysis code, and produces a replication difference report.
review-paper
Scaffold and draft medical/AI literature reviews (narrative, scoping PRISMA-ScR, or systematic). Asks for the spine axis, builds a 7-part skeleton with a required Intro scope/non-overlap block, a summary-table stub, an evaluation-metrics critique subsection, and reporting-guideline wiring. Reuses the self-review RV1-RV9 narrative-review probes for QC. Does not invent citations.
search-lit
Literature search and citation management for medical research. Searches PubMed, Semantic Scholar, and bioRxiv/medRxiv with verified citations. Anti-hallucination — every reference verified via API before inclusion. Generates BibTeX entries.
setup-medsci
Diagnostic checklist for the MedSci Skills runtime. Verifies Python, R, Node, Claude Code, Git, Zotero, and configured MCP servers, and prints a pass/fail table with links to the right setup doc for any missing component. Read-only — does not install anything.
Bio shown is the top-scored skill's repo description as a fallback — real GitHub bios land in a future update.