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概要と使いどころ
Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation
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File and directory management tool for creating, reading, writing, deleting, moving, copying, and searching files. Use this skill when the user needs to perform file operations, list directories, search by pattern or content, or get file metadata like size and modification time.
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。
Extract subtitles and transcripts from Bilibili and YouTube videos. Use when the user wants to get subtitles from B站 (Bilibili) or YouTube, extract Chinese/Japanese video transcripts, watch member-only Bilibili content, or perform Q&A on video content. Supports dual-platform subtitle extraction with yt-dlp.
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 whenever the user wants to create, read, edit, or manipulate Word documents (.docx files). Triggers include: any mention of "Word doc", "word document", ".docx", or requests to produce professional documents with formatting like tables of contents, headings, page numbers, or letterheads. Also use when extracting or reorganizing content from .docx files, inserting or replacing images in documents, performing find-and-replace in Word files, working with tracked changes or comments, or converting content into a polished Word document. If the user asks for a "report", "memo", "letter", "template", or similar deliverable as a Word or .docx file, use this skill. Do NOT use for PDFs, spreadsheets, Google Docs, or general coding tasks unrelated to document generation.
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。
React、Next.js、状态管理（State Management）、性能优化（Performance Optimization）及 UI 最佳实践的前端开发模式。
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。
ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads.
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。
Frontend development patterns for React, Next.js, state management, performance optimization, and UI best practices.
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。
Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generate...
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。
Process CRISPR screening data to identify essential genes and hit candidates. Performs quality control, statistical analysis (RRA), and hit calling for pooled CRISPR screens including viability screens and drug resistance/sensitivity studies.
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。
Perform quantitative meta-analysis with effect size calculation, forest plots, funnel plots, and heterogeneity assessment. Use when: user asks to combine results from multiple studies, calculate pooled effect sizes, assess publication bias, or create forest/funnel plots. NOT f...
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。
A high-performance Rust toolkit (with Python bindings and a CLI) for genomic interval analysis; use it when you need fast overlap queries, coverage track generation, genomic tokenization for ML, reference sequence verification, or fragment processing.
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 Meta-analysis funnel plots and perform publication bias testing. Takes CSV file with Meta-analysis data as input, outputs funnel plot PNG, Egger test and Begg test 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。
Performs epidemiological analyses including disease modeling (SIR/SEIR), outbreak investigation, risk factor identification, incidence/prevalence estimation, and causal inference from observational data; trigger when users discuss disease spread, public health data, or populat...
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。
Perform statistical tests, hypothesis testing, correlation analysis, and multiple testing corrections using scipy and statsmodels. Works with ANY LLM provider (GPT, Gemini, Claude, etc.).
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 Biopython to read/write/convert biological sequence files (FASTA/GenBank/FASTQ, etc.) and perform basic sequence operations; use when you need reliable sequence I/O, lightweight sequence manipulation, or scalable processing of large sequence datasets.
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 Bio.Phylo to read/write phylogenetic trees and perform visualization and statistics; use when tree parsing/conversion, pruning/rerooting, distance calculation, or plotting is required.
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。
A multi-stage workflow for writing long-form medical reviews; used when the user needs to build an outline based on PubMed literature, write chapter by chapter, perform supplementary searches, and format citations; input is the review topic and project directory, output is a c...
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。
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.
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。
A medical-research-native literature reading skill for users with clinical, bioinformatics, translational, and basic experimental backgrounds. Use this skill whenever a user wants to read, analyze, critique, or interpret a medical or scientific paper — whether they provide a PDF, abstract, DOI, PMID, or just a title. Triggers include requests like \"analyze this paper\", \"critique this study\", \"is this a strong paper?\", \"give me similar studies\", \"prepare me for journal club\", \"help me understand this bioinformatics paper\", \"what are the weaknesses here?\", or \"turn this into a mind map\". Also activate for any downstream deliverables such as journal club kits, comparison tables, PI decision briefs, replication starters, or follow-up experiment designs. Do NOT treat as a generic summarizer — this skill performs structured evidence-type classification, track-specific critical appraisal, interpretation-boundary judgment, and research-grade follow-up generation.
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。
Maps whether a biomedical research topic, subtopic, or study angle is truly saturated, superficially crowded, strategically occupied, or still open for differentiated entry. Use this skill when a user wants to know whether a hot medical research direction is already overworked, whether meaningful whitespace remains, whether major groups have already occupied the obvious claims, and whether the timing window is still open. Always distinguish popularity from true saturation, and distinguish cosmetic novelty from meaningful differentiating entry.
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 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.
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 XGBoost models on tabular data and returning feature importance ranking outputs. Supports binary classification and regression with automatic task detection, train-test split, performance tables, feature importance ranking tables, and PNG importance plots.
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。