「aria」の検索結果

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

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Detect copy number variations from whole genome sequencing data and generate publication-quality genome-wide CNV plots. Supports CNV calling, segmentation, and visualization for cancer genomics and rare disease 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。

Detect and correct for horizontal pleiotropy in Mendelian randomization analyses using MR-PRESSO for outlier removal, MR-Egger regression for directional pleiotropy, and Steiger filtering for variant directionality. Use when validating MR results, detecting pleiotropic instrum...

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 standardized figure legends for scientific figures and charts; use when preparing publication-ready legends that summarize design, variables, sample size, and key statistical notes.

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。

Designs retrospective or prospective clinical cohort study protocols for biomedical and clinical research. Always use this skill when the user needs a cohort-based study plan rather than a general study idea, evidence summary, or mechanistic experiment design. Focus on cohort appropriateness, enrollment logic, baseline time-zero definition, follow-up structure, endpoint definition, variable collection, confounding control, and a coherent primary statistical analysis line. Do not invent data availability, follow-up completeness, outcome ascertainment quality, sample size adequacy, or causal interpretability.

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 Mendelian randomization study designs from a user-provided exposure and outcome direction. Always use this skill whenever a user wants to design, plan, or build a Mendelian randomization study — even if phrased as "help me write a paper on X", "design an MR study for Y", or "I want to test whether A causally affects B using GWAS". Covers core two-sample MR design, optional bidirectional follow-up, optional multivariable MR, IV selection logic, ancestry alignment, harmonization, IVW as the default primary estimator, weighted median / MR-Egger / MR-PRESSO / leave-one-out sensitivity analyses, Steiger directionality, heterogeneity / pleiotropy checks, and explicit claim-boundary control. Always outputs four workload configs (Lite / Standard / Advanced / Publication+) with a recommended primary plan, stepwise workflow, method rationale, validation ladder, figure plan, minimal executable version, and strictly verified literature guidance with no fabricated references.

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 two-sample Mendelian randomization research designs from a user-provided outcome, exposure or exposure family, and robustness direction. Use when a study centers on summary-statistics causal inference with instrument selection, harmonization, IVW-primary estimation, complementary estimators, sensitivity analyses, optional multivariable upgrades, and conservative evidence interpretation. 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 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.

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 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。

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。

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.

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。

Fail closed on published draft XQUIC code pull requests before ready-for-review state. Use when asked to pre-review a code PR or move it out of draft; verify current base-to-head code, protocol conformance, validation coverage, adversarial bad cases, hot-path performance, and memory safety in a five-part local report.

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 John Templeton-style global contrarian value: buy at points of maximum pessimism, search worldwide, and demand a large discount to long-term value.

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Use when evaluating Prem Watsa / Fairfax-style value investing: insurance float, contrarian allocation, special situations, and conservative downside-focused capital allocation.

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。

Emulated Vercel REST API for local development and testing. Use when the user needs to interact with Vercel API endpoints locally, test Vercel integrations, emulate projects/deployments/domains, set up Vercel OAuth flows, manage environment variables, create API keys, configure protection bypass, emulate Vercel Blob storage, or test without hitting the real Vercel API. Triggers include "Vercel API", "emulate Vercel", "mock Vercel", "test Vercel OAuth", "Vercel integration", "Vercel Blob", "local Vercel", or any task requiring a local Vercel 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。

Apply when concurrent actors might write to the same file, branch, key, or state object. Eliminate the sharing first; serialize structurally only when one shared writer is a real invariant.

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。

Apply when designing types, reviewing a function signature, or writing code in any statically-typed language. Make illegal states unrepresentable, brand semantic primitives, parse external data at boundaries, refuse to lie to the compiler, exhaust variants, derive from authoritative schemas.

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 for "interrogate", "adversarial review", "multi-model review", "challenge this", "stress test this code", "find blind spots", or "tear this apart". Multiple LLM reviewers challenge changes from independent angles.

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。

Create banners using AI image generation. Discuss format/style, generate variations, iterate with user feedback, crop to target ratio. Use when user wants to create a banner, header, hero image, cover image, GitHub banner, Twitter header, or readme banner.

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。

Create logos using AI image generation. Discuss style/ratio, generate variations, iterate with user feedback, crop, remove background, and export as SVG. Use when user wants to create a logo, icon, favicon, brand mark, mascot, emblem, or design a logo.

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。

Detect pauses and silence in audio using local dynamic thresholds. Use when you need to find natural pauses in lectures, board-writing silences, or breaks between sections. Uses local context comparison to avoid false positives from volume variation.

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。