3,621 件 ・ 関連度順
概要と使いどころ
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.
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。
Explains why studies on the same biomedical topic reach different or opposing conclusions by auditing differences in population, endpoint definition, sample source, assay or platform, study design, statistical model, adjustment strategy, validation chain, and bias control. It separates true contradiction from apparent contradiction caused by framing or methods. Never fabricate references, PMIDs, DOIs, trial identifiers, dataset details, platform details, study features, or conflict explanations that are not supported by the input.
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 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...
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。
Use when analyzing bulk RNA-seq or microarray expression data to identify differentially expressed genes between two biological groups (case vs control), with volcano plots and heatmap visualization. NOT for:single-cell RNA-seq, methylation analysis, non-expression data.
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。
Use when estimating relative immune cell infiltration from a bulk expression matrix with a CIBERSORT-style nu-SVR deconvolution workflow based on an LM22 signature matrix, comparing one case group against one control group, and generating structured tables plus immune-fraction plots. NOT for single-cell RNA-seq, spatial data, clinical diagnosis, or workflows that require the original hosted CIBERSORT web service.
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。
Use when screening differentially expressed genes from a bulk expression matrix between two user-specified groups, producing DEG tables, a volcano plot, and a clustered heatmap. Triggers include DEG analysis, volcano plot, clustered heatmap, limma-based two-group comparison, and case-vs-control screening. NOT for single-cell RNA-seq, multi-group contrasts, count-model workflows such as DESeq2/edgeR, or non-expression omics data.
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。
Use when selecting predictive genes or other molecular features from bulk expression matrices for binary case-vs-control classification with elastic net logistic regression, including coefficient path and cross-validation plots. Trigger keywords: elastic net, glmnet, feature selection, binary classification, lambda.min, lambda.1se. NOT for: survival/Cox modeling, multiclass outcomes, single-cell data, or non-expression tables.
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。
Guides Stripe integration decisions across development and test environment planning (separate sandboxes vs the shared test mode sandbox), API selection (Checkout Sessions vs PaymentIntents), Connect platform setup (Accounts v2, controller properties), billing/subscriptions, tax and registrations (Stripe Tax, automatic_tax, product tax codes), Treasury financial accounts, integration options (Checkout, Payment Element), migrating from deprecated Stripe APIs, and security best practices (API key management, API key permissions, webhooks, OAuth). Use when planning, building, modifying, testing, or reviewing any Stripe integration, including choosing a development environment, accepting payments, building marketplaces, integrating Stripe, processing payments, setting up subscriptions, collecting sales tax, VAT, or GST, creating connected accounts, or implementing secure key handling.
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。
Use when running controlled perf experiments to validate hypotheses.
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。
Use when evaluating Paul Tudor Jones-style macro trend trades: major turning points, trend confirmation, risk control, stop discipline, liquidity, and not losing big.
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。
Use when evaluating markets through Li Bei's macro hedge lens: regime identification, trend + contrarian timing, position management by conviction, and risk control for drawdown.
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。
Use when evaluating Carl Icahn-style activist investments: undervalued assets, poor governance, capital allocation fixes, buybacks, asset sales, board pressure, and control catalysts.
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。
Remote-control tmux sessions for interactive CLIs by sending keystrokes and scraping pane output.
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。
Teaches how to reason from a transaction protocol’s rules to a working model. Use when analyzing a concurrency-control paper, spec, or algorithm to identify state, metadata, invariants, operation rules, examples, counterexamples, guarantees, false aborts, unsafe commits, or tradeoffs before inspecting concrete traces.
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。
Linearizing nonlinear dynamics around operating points for control design.
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。
Reference guide for translating Python syntax constructs to Scala equivalents. Use when converting Python code to Scala and need mappings for basic syntax elements like variable declarations, control flow, comprehensions, string formatting, and common operators.
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。
Design effective excitation signals (step tests) for system identification and parameter estimation in control systems.
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。
Implement safety interlocks and protective mechanisms to prevent equipment damage and ensure safe control system operation.
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。
Use this skill when calculating control system performance metrics such as rise time, overshoot percentage, steady-state error, or settling time for evaluating simulation results.
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。
Use this skill when simulating vehicle motion, calculating safe following distances, time-to-collision, speed/position updates, or implementing vehicle state machines for cruise control modes.
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。
Analyze videos with Google Gemini API (summaries, Q&A, transcription with timestamps + visual context, scene/timeline detection, video clipping, FPS control, multi-video comparison, and YouTube URL analysis).
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。
NVIDIA NeMo Guardrails configuration for conversational safety and control
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。
Structured prompt template creation with variables, formatting, and version control
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。
Session-scoped hook lifecycle management with enable/disable/status controls, execution profiling, and color-coded performance alerts.
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。