3,001 件 ・ 関連度順
概要と使いどころ
Control deep thinking mode. Default enabled. For very simple tasks (simple reminders, greetings, quick queries), can temporarily disable to speed up response. Auto-restores to enabled after completion.
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。
Launch browser or check its status. Returns current state (is_open, url, title, tab_count). If already running, returns status without restarting. Auto-handles everything - no need to call browser_status first.
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 any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.
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。
Django架构模式，使用DRF设计REST API，ORM最佳实践，缓存，信号，中间件，以及生产级Django应用程序。
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。
Django 测试策略，包括 pytest-django、TDD 方法、factory_boy、模拟、覆盖率以及测试 Django REST Framework API。
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。
Spring Boot架构模式、REST API设计、分层服务、数据访问、缓存、异步处理和日志记录。用于Java Spring Boot后端工作。
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。
Django testing strategies with pytest-django, TDD methodology, factory_boy, mocking, coverage, and testing Django REST Framework APIs.
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。
Spring Boot 架构模式、REST API 设计、分层服务、数据访问、缓存、异步处理与日志。适用于 Java Spring Boot 后端开发。
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。
REST API设计模式，包括资源命名、状态码、分页、过滤、错误响应、版本控制和生产API的速率限制。
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。
生产级 API 的 REST API 设计模式，包括资源命名、状态码、分页、过滤、错误响应、版本控制和速率限制。
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。
Django 架构模式、使用 DRF 的 REST API 设计、ORM 最佳实践、缓存、信号（Signals）、中间件（Middleware）以及生产级 Django 应用。
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。
Spring Boot 架构模式、REST API 设计、分层服务、数据访问、缓存、异步处理和日志记录。适用于 Java Spring Boot 后端开发。
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。
使用 pytest-django 进行 Django 测试的策略、TDD 方法论、factory_boy、Mock 模拟、测试覆盖率以及测试 Django REST Framework API。
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。
Generate leave-one-out sensitivity analysis plots for meta-analysis. Input is a CSV file containing meta-analysis data; outputs are a sensitivity forest plot (PNG) and a sensitivity data table (CSV) showing pooled effect estimates after excluding each study in turn.
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。
Advanced Biopython modules for motifs, population genetics, sequence utilities, restriction analysis, clustering, and GenomeDiagram visualization; use when you need extended bioinformatics analysis beyond basic sequence I/O and alignment.
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 NHANES-style cross-sectional epidemiology + retrospective clinical validation research designs from a user-provided disease and biomarker direction. Always use this skill whenever a user wants to design, plan, or build a population-level biomarker association study using NHANES or similar survey datasets, especially when the article logic includes disease definition, biomarker formula derivation, multivariable logistic regression, restricted cubic spline analysis, subgroup stability testing, and a secondary hospital-based retrospective validation cohort. Covers five study patterns (cross-sectional association, dose-response / RCS, subgroup-stability, NHANES + retrospective validation, preliminary screening-performance) 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...
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 conventional non-oncology diagnostic machine-learning research designs from a user-provided disease context, optional mechanism theme, and validation direction. Use when a study centers on disease-vs-control transcriptome comparison, optional mechanism-gene restriction, feature shrinkage, diagnostic model construction, ROC / calibration / DCA evaluation, interpretation layers, 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.
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 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.
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。
Ranks papers by evidence family, methodological quality tier, validation depth, and claim discipline; assigns anchor, context-setting, mechanistic support, or caution citation roles; prevents prestige-based or design-label-based ranking errors.
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 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.
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。
Converts biomedical table content into clear manuscript or presentation narrative by prioritizing meaningful patterns, contrasts, and interpretation boundaries rather than restating every number.
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。
Builds prioritized manuscript revision plans for major or minor revisions by separating comments that require experiments, analyses, clarification, restructuring, or wording changes.
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 filtering genes with high missingness and then imputing missing values in a bulk expression matrix with group-aware KNN through DMwR2, where donor samples are restricted by one annotation column before imputation. For strata with 10 or fewer samples, the script falls back to row-wise direct filling with mean or median. NOT for: single-cell data, multi-column stratification, non-tabular inputs, network access, or interactive workflows.
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。
Land a change everywhere the same fact is stated — enumerating the full surface inventory (landing copy, docs, machine-readable summaries, changelog badges repeated across every page, sitemap, README, descriptions embedded in code, and untyped frontend consumers of typed responses), generating a surface instead of restating it, drift tests where you cannot generate, never hand-maintaining a copy of a surface another codebase owns, shipping a paired PR when you change a format a different codebase decodes, and keeping overloaded product words apart. Use when adding or renaming a user-facing feature, editing product/marketing copy or docs, renaming a serialized response field, changing a serialization or share-link format, wiring one repo's output into another's checks, or reviewing a PR that touched only one place a fact appears.
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。