Use when drafting an IRB or ethics research protocol. Writes Background, Study Design, Sample Size and Statistical Plan in full prose and leaves institution-specific sections as TODO skeletons. Filling an institutional Word form is /fill-protocol.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when a manuscript comes back with reviewer or editor comments. Numbers every comment, classifies it MAJOR/MINOR/REBUTTAL, drafts a point-by-point response with tracked manuscript changes, routes new analyses to /analyze-stats and writes the cover letter.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when you must prove an analysis ran on the intended data or lock a dataset version. Builds a deterministic content-hash manifest (file SHA-256, schema, per-column value hashes), verifies later copies against it for drift, and diffs two manifests.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when writing a literature review article (narrative, scoping PRISMA-ScR or systematic). Scaffolds a 7-part skeleton with a scope and non-overlap statement, summary-table stubs and reporting-guideline wiring, without inventing citations. Original research is /write-paper.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when checking your own manuscript before submission from a reviewer's perspective. Returns anticipated Major/Minor comments with fixes, including numerical, citation and leakage checks, with an optional multi-reviewer panel. Someone else's paper is /peer-review.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when applying a published cohort study's methodology to a different database. Extracts the design from the source paper, maps variables to the target database with a harmonization table, generates the analysis code and reports every forced deviation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when finding papers or building a reference list. Searches PubMed, Semantic Scholar and bioRxiv/medRxiv, includes only references verified through an API, and generates BibTeX. Auditing an existing reference list is /verify-refs.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when building, auditing or freezing a journal submission package from the canonical manuscript. Detects drift between the source and the per-journal submission copy, builds byte-preserving packages with manifests, and records current, stale or frozen status.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when checking whether a manuscript's references are real. Audits each citation against PubMed and CrossRef, flags fabricated or mismatched entries and writes qc/reference_audit.json. Audit-only; never edits references or refs.bib. Citation-key checks are /manage-refs.
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 drafting a medical research manuscript or any IMRAD section. Runs an 8-phase pipeline from outline to submission-ready draft for original articles, AI validation studies, case reports, meta-analyses, technical notes and more. Checking a draft is /self-review.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when a skill fails for a missing tool or the environment needs checking. Diagnoses Python, R, Node, Claude Code, Git, Zotero and configured MCP servers and prints a pass/fail table with links to the setup docs. Read-only; installs nothing.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when a manuscript or response-to-reviewers letter reads as AI-written. Scans for 27 AI writing patterns and rewrites flagged passages, preserving technical accuracy and bounding how much text changes. Not general copy-editing; that is /polish-language.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when drafting a grant or challenge proposal for a radiology or medical AI project, including a Korean government industry-academia plan. Structures significance, innovation, approach, aims, milestones and consortium roles, keeping claims evidence-based and executable.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when comparing an exposure-outcome association across countries with parallel national surveys (KNHANES, NHANES, CHNS). Harmonizes variables, runs parallel weighted analyses and builds comparison tables for 2-country (KR+US) or 3-country (KR+US+CN) designs.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when you need full-text PDFs for a list of DOIs, such as a meta-analysis screening set. Batch-downloads open-access copies via Unpaywall, PMC, OpenAlex and Crossref, lists paywalled papers for manual access, and can convert PDFs to Markdown.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when an institutional Word form (.doc/.docx IRB protocol, ethics application, grant template) must be filled without breaking its styles, tables, fonts or page layout. Renders content drafted by /write-protocol into the template; CJK-aware.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when planning how many patients or cases a study needs before data collection (power analysis, IRB justification). Walks a decision tree to the right test and returns reproducible R/Python code and IRB-ready justification text. Analyzing collected data is /analyze-stats.
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 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 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.