「express」の検索結果

SSTI playbook. Use when template expressions, server-side rendering, preview features, or templating engines may evaluate attacker-controlled content.

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。

Establish durable brand and voice context for cross-skill consumption. Generates BRAND.md (audience, positioning, do/don't editorial rules, taboo phrases, competitor differentiation) and VOICE.md (existing persona JSON re-expressed as readable prose), both written to the project root. When present, the blog orchestrator auto-loads these files for write, rewrite, brief, outline, calendar, and strategy workflows. Pairs with blog-persona, which manages the structured persona JSON. Use when user says "blog brand", "create brand context", "brand voice doc", "BRAND.md", "VOICE.md", "establish editorial brand", "brand guidelines for blog".

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Produces a cost or effort estimate someone can defend — decomposing the work, choosing between analogous, parametric and bottom-up methods, documenting the basis and its assumptions, expressing confidence as a range, and sizing contingency and management reserve separately. Use this to build an estimate, challenge one you have been handed, work out why estimates keep coming in low, or decide how much reserve a portfolio actually needs.

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DiDi ride-hailing service skill. Supports taxi booking, price estimation, route planning (driving/transit/walking/cycling), order management, driver location tracking, and scheduled rides. Use when user expresses any transportation need including ride-hailing, route queries, or commuting.

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后端架构模式、API 设计、数据库优化以及适用于 Node.js、Express 和 Next.js API 路由的服务端最佳实践。

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Backend architecture patterns, API design, database optimization, and server-side best practices for Node.js, Express, and Next.js API routes.

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。

后端架构模式、API设计、数据库优化以及适用于Node.js、Express和Next.js API路由的服务器端最佳实践。

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。

后端架构模式、API 设计、数据库优化以及 Node.js、Express 和 Next.js API 路由的服务端最佳实践。

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。

后端架构模式、API 设计、数据库优化以及针对 Node.js、Express 和 Next.js API 路由的服务端最佳实践。

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Professional beautification tool for gene expression heatmaps, automatically adds clustering trees, color annotation tracks, and intelligently optimizes label layout.

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Decompose genetic effects into direct and indirect paths through mediating variables using the mediation R package. Tests whether gene expression, methylation, or other molecular phenotypes mediate the effect of genetic variants on disease. Use when testing whether a molecular...

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Generates complete conventional oncology bulk-transcriptome biomarker and hub-gene research designs from a user-provided cancer type and study direction. Always use this skill whenever a user wants to design, plan, or build a tumor bioinformatics study centered on differential expression, prognostic filtering or risk modeling, PPI-based hub-gene prioritization, diagnostic/prognostic evaluation, clinical association, immune infiltration context, methylation context, and optional tissue or cell validation. Covers five study patterns (signature-first prognostic workflow, hub-gene-first biomarker workflow, hybrid signature-to-hub workflow, immune-context biomarker workflow, translational validation workflow) 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, publication upgrade path...

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Design evidence-discovery and validation workflows for drug repurposing studies by integrating disease mechanisms, drug-target logic, expression reversal, real-world evidence, and validation routes into a closed-loop study blueprint.

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。

Generates complete tumor immune-infiltration-guided bulk-transcriptome diagnostic biomarker and machine-learning research designs from a user-provided cancer type and study direction. Always use this skill whenever a user wants to design, plan, or build a tumor bioinformatics study centered on differential expression, immune infiltration estimation, immune-linked module discovery, consensus feature selection, diagnostic modeling, nomogram construction, clinical association, and optional prognostic extension or validation. Covers five study patterns (immune-cell-first diagnostic workflow, immune-module-to-biomarker workflow, consensus-ML biomarker workflow, diagnostic-plus-prognostic hybrid workflow, translational validation workflow) 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, publication upgrade path, reference literature pack, and self-critical risk review.

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Generates complete process-related diagnostic biomarker bioinformatics research designs from a user-provided disease context, gene-family or pathway theme, and validation direction. Use when a study centers on process-related genes, DEG and WGCNA integration, machine-learning feature selection, nomogram-based diagnostic modeling, immune infiltration, regulatory-network analysis, and optional external or experimental validation. Covers five study patterns (process-DEG discovery, co-expression-module integration, machine-learning biomarker selection, diagnostic model/nomogram workflow, immune-regulatory interpretation and validation) and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.

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Generates complete dual-disease shared-transcriptome biomarker and hub-gene research designs from a user-provided disease pair and shared-biology direction. Always use this skill whenever a user wants to design, plan, or build a non-oncology two-disease transcriptome study centered on per-disease differential expression, shared-signal intersection or concordance, PPI-based hub-gene prioritization, diagnostic evaluation across both diseases, immune infiltration context, pathway interpretation, and optional orthogonal validation. Covers five study patterns (shared-DEG-first workflow, hub-gene-first shared-biomarker workflow, hybrid shared-biomarker compression workflow, immune-context shared-biomarker workflow, orthogonal validation workflow) 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, publication upgrade path...

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Generates complete conventional single-gene oncology research designs from a user-provided cancer context, target gene, and validation direction. Use when a study centers on a fixed candidate gene and needs expression, prognosis, clinicopathologic association, functional interpretation, immune context, genomic or epigenetic context, optional drug-response hypotheses, and orthogonal validation. Covers five study patterns and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.

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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 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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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 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 you need a standardized R CLI workflow to train a two-class random forest model from an expression-like feature matrix, rank variable importance, and generate reproducible error and importance plots. NOT for regression tasks, multi-class classification, missing-value imputation, preprocessing, or remote data fetching.

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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 building a ceRNA regulatory network from a key gene list by combining bundled miRNA-mRNA and miRNA-lncRNA database files, with flat-file CSV exports and PDF visualization in a single output directory. NOT for: differential expression, single-cell analysis, enrichment analysis, or workflows without a key gene list.

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。