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Systematically maps mechanism evidence for a disease from molecules to pathways, cell types, tissues, biological consequences, and clinical phenotypes. Always use this skill when a user needs a layered mechanism evidence chain rather than a flat summary or immediate gap analysis. Formal literature citations must be real and verifiable.

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Clarifies a vague clinical or biomedical research idea into a structured, bounded, searchable, researchable, and testable question. Always use this skill whenever a user has an early-stage clinical or research thought, an over-broad topic, an ill-defined evidence question, or an unclear problem statement that must be translated into a question framing suitable for literature retrieval, evidence synthesis, gap analysis, study design, or downstream protocol planning. Never jump straight to answering the substantive medical question unless the user explicitly asks for that. Focus first on question framing, boundary setting, and downstream-ready formulation.

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Builds professional search strategies for PubMed, Embase, Web of Science, and similar databases. Use when a user needs to construct a MeSH-based Boolean query, design a systematic review search, expand a concept with synonyms, apply study-type or date filters, or adapt a query across multiple databases. Also triggers when the user says "help me search for papers on X", "build a search strategy", "what are the MeSH terms for", or "I need a systematic review search string".

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Identifies translationally meaningful paths for bioinformatics findings by mapping omics or computational discoveries to diagnosis, stratification, prognosis, treatment-response, monitoring, or target-nomination use cases, while auditing bridge evidence, assayability, and validation burden. Use this skill when a user wants to know whether a bioinformatics finding can be framed as a stronger translational topic without overclaiming clinical relevance. Always separate statistical signal from translational value, and never imply clinical utility, targetability, or validation depth without explicit evidence support.

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Reverse-engineers the methods section of a biomedical paper into a structured, reproducible workflow. Use this skill when a user wants to understand how a study was actually executed, extract data sources, inclusion/exclusion logic, preprocessing, analytical sequence, software/tools, validation path, and critical parameters, or build a replication checklist from a paper, abstract, DOI, PMID, title, screenshot, or partial methods text. Do not treat this as generic summarization. Focus on reconstructing the operational method pipeline, surfacing missing reproducibility details, and distinguishing explicitly reported steps from inferred or unresolved ones. Never fabricate references, methods details, identifiers, software versions, parameters, datasets, or validation steps.

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Finds translational opportunities that connect basic-research discoveries to clinically meaningful use cases such as diagnosis, stratification, prognosis, treatment response prediction, monitoring, or therapeutic development. Use this skill when a user wants to turn a mechanism finding, pathway signal, cellular phenotype, experimental observation, or omics discovery into a stronger translational research direction. Always separate mechanistic relevance from translational usability, and never present a basic finding as clinically actionable unless the evidence supports that level.

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Use when building a weighted gene co-expression network from a bulk expression matrix and a sample group file, filtering variable genes by MAD, identifying co-expression modules with WGCNA, correlating modules with traits, and exporting module-level plots and gene tables. NOT for single-cell RNA-seq, differential expression testing, methylation analysis, or datasets that are too small for WGCNA after quality control.

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Use when running prognostic survival analysis on a clinical cohort with time-to-event data to estimate univariate and multivariable Cox proportional hazards models, export result tables, and generate forest plots. NOT for: nomogram construction, calibration curves, time-dependent ROC analysis, or model training/feature selection beyond the built-in univariate screening rule.

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Use when performing sample-level dimensionality reduction and visualization on abundance or OTU-style matrices with a companion group file, generating UMAP and/or t-SNE coordinates and plots for group separation assessment. NOT for: differential expression testing, single-cell workflows requiring dedicated embeddings pipelines, or analyses without a sample grouping file.

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Use when performing time-dependent ROC curve analysis for survival data with follow-up time, event status, and a numeric marker. Supports CSV/TXT/TSV/Excel input, `risk_score` as the default marker unless `--marker_col` is provided, parameter validation, standardized output directories, AUC table export, ROC point export, and PDF figure generation.

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Use when analyzing transcription factor (TF) regulatory networks using Dorothea database. Input gene list, identify regulating transcription factors, generate TF-Target network visualization. For: transcription factor enrichment analysis, gene regulatory network research.

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Use when you need a standardized R CLI workflow to run two-class SVM-RFE feature ranking on an expression-like matrix, choose an informative feature count from cross-validated error, and generate reproducible ranking and error plots. NOT for regression, multi-class classification, missing-value imputation, or remote data fetching.

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Use when estimating immune infiltration from bulk RNA-seq expression matrices with ssGSEA/GSVA, comparing case versus control groups, and generating downstream immune-score visualizations. NOT for single-cell RNA-seq, absolute cell proportion estimation, or clinical decision making.

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Use when generating Sankey or alluvial plots from sample annotation tables where rows are samples and selected columns are categorical stages such as risk group, response status, subtype, or cohort labels. NOT for: gene network flow analysis, continuous-value trajectories, or graph-structured pathway visualization.

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Use when performing correlation analysis between two variables including Pearson and Spearman correlation methods. Supports command-line parameter input, automatic data format detection, parameter validation, result directory creation, and CSV or TXT format result export.

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Use when evaluating diagnostic biomarker performance from case-control expression data with logistic regression and ROC curves, exporting coefficient and AUC tables together with a ROC PDF. NOT for: survival analysis, time-to-event outcomes, multiclass classification, calibration curves, decision-curve analysis, or nomogram construction.

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Use when performing PCA principal component dimensionality reduction on tabular numeric data. Supports command-line parameter input, automatic numeric feature selection, parameter validation, result directory creation, and CSV or TXT format result export.

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Use when constructing a prognosis nomogram from survival-related clinical predictors, exporting the nomogram bundle and C-index table, and optionally rendering the final nomogram PDF. NOT for: univariate/multivariable Cox feature screening, calibration curves, ROC analysis, decision-curve analysis, or non-survival outcomes.

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Use when assessing how well a survival model's predicted probabilities agree with observed outcomes by fitting a Cox model and generating bootstrap calibration curves at one or more prediction horizons from a clinical CSV file. NOT for: nomogram construction, univariate Cox screening, ROC analysis, or decision-curve analysis.

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Generates complete bidirectional multi-phenotype Mendelian randomization research designs from a user-provided exposure family and outcome family. Always use this skill whenever a user wants to design, plan, or build a genome-wide causal-inference study based on publicly available GWAS summary statistics, especially when the article logic includes multiple exposures, multiple outcomes or subtypes, bidirectional MR, IV filtering, IVW as the main estimator, weighted median / MR-Egger / MR-PRESSO sensitivity analyses, leave-one-out testing, heterogeneity / pleiotropy checks, and multiple-testing control with FDR. Covers five study patterns (single-family bidirectional MR, multi-phenotype screening MR, subtype-resolved MR, phenome-style bidirectional causal map, mechanism-prioritized MR follow-up) and always outputs four workload configs (Lite / Standard / Advanced / Publication+) with recommended primary plan, step-by-step workflow, figure plan, validation strategy, minimal executable version...

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Use this bioinformatics data analysis skill to construct a database-driven lncRNA-mRNA regulatory network from target lncRNA and/or gene lists by projecting shared miRNA evidence from local ceRNA reference tables. It does not infer networks from expression matrices.

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Detects methodological gaps across study design, analysis, validation, bias control, reproducibility, and implementation readiness within a biomedical research area. Use this skill when a user wants to identify what current studies are still methodologically missing, which weaknesses are most consequential, and what upgrade path would produce a stronger next-step study. Always separate design gaps, analysis gaps, validation gaps, and reproducibility gaps. Never treat technical complexity as methodological rigor.

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Verifies whether a scientific or biomedical claim is actually supported by the cited original papers rather than by citation drift, overstatement, selective citation, or correlation-to-causation inflation. Use this skill whenever a user wants to check whether a repeated statement, slide claim, manuscript sentence, review assertion, or “people often say” scientific conclusion is truly supported by the underlying primary literature. Always separate the claim itself, the cited paper(s), what the paper actually showed, what it did not show, and whether later retellings drifted beyond the original evidence. Never fabricate references, findings, study features, or citation chains.

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Use when you need a standardized R CLI workflow to build a protein-protein interaction network from a local gene list and an offline STRING cache, export node and edge tables, and render a reproducible PDF network plot. NOT for online API fetching, arbitrary graph databases, multi-omics integration, or non-STRING interaction sources.

日本語の概要は準備中です。原文の説明を表示しています。