Use when starting or inheriting a radiology research project and need to know what it is. Classifies the project type, summarizes its current state, lists missing inputs, recommends next steps and scaffolds lightweight project memory files.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when references in a .bib file (often from /search-lit) should land in Zotero and Obsidian. Syncs them to the Zotero library, writes Obsidian literature notes and extracts cross-cutting concept notes once enough accumulate. A folder of PDFs is /obsidian-paper-vault.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when a paper needs publication-ready figures or a visual abstract. Makes ROC, forest, calibration, Kaplan-Meier and Bland-Altman plots, CONSORT/STARD/PRISMA flow diagrams, confusion matrices, pipeline diagrams and journal visual abstracts, checking the journal's AI-image policy first.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when managing a manuscript project over time. Scaffolds the project structure, tracks writing progress across phases, maintains project memory files, and generates submission checklists and backwards timelines (commands init, status, sync-memory, checklist, timeline).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when running a systematic review and meta-analysis, DTA or intervention. Covers PROSPERO protocol, search, screening, extraction, risk of bias (QUADAS-3, RoB 2, ROBINS-I), bivariate/HSROC or random-effects pooling, forest plots, heterogeneity and PRISMA reporting. Topic scouting is /ma-scout.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when designing or auditing how an LLM or multimodal LLM is evaluated on a clinical task (report generation, VQA, text extraction). Covers reference standard, clinical-efficacy metrics beyond BLEU/ROUGE, hallucination, contamination and prompt sensitivity. Imaging models are /model-assessment.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when validating or evaluating a trained medical-imaging model. Audits split leakage and validation design, computes task-correct held-out metrics (Dice + HD95, AUROC + AUPRC, FROC, calibration), and covers uncertainty/OOD and Grad-CAM explainability, each with a gate.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when a trained medical-imaging model needs its documentation. Fills a Model Card and a Datasheet for its dataset from facts you supply, adds a METRIC-informed data-quality pass and gates that no required section is empty. Never invents numbers, provenance or licence.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Renamed to /model-assessment in v6 (removed in v7).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when you need a runnable PyTorch training repo for a medical-imaging task (segmentation, classification, detection, synthesis, self-supervised, or fine-tuning a pretrained backbone). Emits a patient-level seed-locked split, train/evaluate scripts, config and a Methods stub.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when choosing the model for a medical-imaging study. Picks a paper-grounded architecture family (CNN/ViT, U-Net/nnU-Net, detection, SAM/foundation, GNN), then vets the concrete repo or checkpoint for licence, version pin, weight provenance and benchmark overlap.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Renamed to /model-selection in v6 (removed in v7).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Renamed to /model-assessment in v6 (removed in v7).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when a target journal has no profile yet. Reads the journal's author guidelines and writes a detailed /write-paper profile plus a compact /find-journal profile, public or user-local private, in the canonical format with quality gates.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when the user describes a research goal without naming a skill, or the task spans several skills. Classifies the request, plans the order and routes to the right medsci-skills skill(s) instead of producing their output itself.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when reviewing someone else's manuscript for a journal, such as after a review invitation or for a revised R1/R2 version. Drafts a structured, constructive review in the journal's format. Never for your own manuscript; that is /self-review.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when a manuscript needs a copy-edit for consistency and non-native English clarity. Flags abbreviation, US/UK spelling, en-dash range, P/p, hyphenation, number-style and unit-spacing issues, then polishes style only. AI-tell removal is /humanize.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Renamed to /imaging-data in v6 (removed in v7).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Renamed to /imaging-data in v6 (removed in v7).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when building or auditing a radiomics or tabular clinical-ML prediction model with a classical learner (LASSO, SVM, random forest, XGBoost and similar). Enforces nested CV, dimensionality control, in-fold feature selection, feature stability, calibration and external validation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when rendering a Markdown document (English or Korean) such as a proposal, IRB cover letter, handout or reference table to PDF via pandoc and xelatex, with auto-fitted table widths and CJK fonts. Not for manuscripts with a bibliography (/manage-refs) or Word forms.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when auditing a manuscript item by item against a reporting guideline or risk-of-bias tool. Covers 49 reporting guidelines and risk-of-bias tools (STROBE, CONSORT, STARD, TRIPOD+AI, PRISMA, QUADAS and more), marking each item PRESENT, PARTIAL or MISSING. Not a reviewer critique (/self-review).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when data needs statistical analysis. Runs reproducible Python/R code for Table 1, diagnostic accuracy, agreement, regression, survival, propensity score, survey-weighted and repeated-measures models, with publication tables. Sample size is /calc-sample-size; pooling studies is /meta-analysis.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when looking for research topics a longitudinal cohort database can answer (NHIS, UK Biobank, an institutional EMR or registry). Profiles the cohort, matches PI expertise, scans literature saturation and returns ranked topic proposals with gap evidence.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.