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kaiser-skills
kaiser-skills には CodingKaiser から収集した 11 個の skills があり、リポジトリ単位の職業カバレッジとサイト内 skill 詳細ページを表示します。
このリポジトリの skills
Write a marimo notebook in a Python file in the right format.
R plotting conventions for Seurat/scRNA-seq: UMAP square plots (aspect.ratio=1, raster=FALSE), polychrome cluster colors, DimPlot/FeaturePlot settings, saving 300 DPI PNG, volcano plots with EnhancedVolcano, statistical comparisons with rstatix/ggpubr.
R Markdown formatting patterns: chunk display strategies (include=FALSE + inline R vs results='asis'), table rendering with knitr::kable, header hierarchy with tabsets, downloadable file embedding, and what to avoid (cat/print in visible chunks).
Sequencing read requirements and bcl2fastq base masks for 10x Genomics libraries: chemistry-specific BasesMaskCR values, ATAC I2 barcode requirement (capital I not lowercase y), Visium FFPE vs frozen R2 length difference.
Python interface to the B-Fabric laboratory information management system. Use when working with B-Fabric API for CRUD operations on samples, datasets, workunits, resources, or building B-Fabric applications and scripts.
Fast, reproducible scientific Python environments with pixi - conda and PyPI unified
Compact Plotly visualization patterns. Express for quick plots, Graph Objects for control.
CLI application patterns for Python. Triggers on: cli, command line, typer, click, argparse, terminal, rich, console, terminal ui.
Comprehensive Python programming guidelines based on Google's Python Style Guide. Use when you needs to write Python code, review Python code for style issues, refactor Python code, or provide Python programming guidance. Covers language rules (imports, exceptions, type annotations), style rules (naming conventions, formatting, docstrings), and best practices for clean, maintainable Python code.
Modern R development practices emphasizing tidyverse patterns (dplyr 1.1 and later, native pipe, join_by, .by grouping), rlang metaprogramming, performance optimization, and package development. Use when you needs to write R code, create R packages, optimize R performance, or provide R programming guidance.
Specialized knowledge of Bash and Zsh scripting, shell automation, command-line tools, and scripting best practices. Use when the user needs to write, debug, or optimize shell scripts, work with command-line tools, automate tasks with bash/zsh, or asks for shell script help.