Skill UI

A comprehensive guide providing best practices for Git workflows, covering adaptation strategies for major Chinese platforms (Gitee, Coding.net, etc.), branch naming conventions, standardized commit messaging (Chinese Conventional Commits), and CI/CD integration across various domestic environments. Helps streamline development processes for Chinese teams.

This skill guides the management of CI/CD pipelines within KubeSphere DevOps. It utilizes Kubernetes Custom Resources (CRs) to define, trigger, and monitor complex build workflows, seamlessly integrating with Jenkins concepts in a cloud-native manner. Use it for automating build, test, and deployment processes for applications running on Kubernetes.

Astropy is a core Python library for astronomy and astrophysics workflows. It provides essential functionality for astronomical research and data analysis, including coordinate transformations, unit and quantity calculations, FITS file operations, cosmological calculations, precise time handling, tabular data manipulation, and astronomical image processing. Use it for implementing or debugging astronomical data analysis code.

Dask is a Python library for parallel and distributed computing that scales pandas, NumPy, and custom workflows beyond memory limits. It supports DataFrames, Arrays, and Bags for processing large datasets across single machines or clusters. Ideal for scientific computing, ETL tasks, and parallelizing operations when data exceeds available RAM or requires distributed execution.

Pythonic wrapper around RDKit for drug discovery workflows: SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, and parallel batch processing. Returns native RDKit Mol objects.

Build or audit reproducible research workflows with NeuroKit2 for physiological time-series: preprocessing, event/interval analysis, multimodal alignment, variability and complexity on ECG, RSP and EDA signals.

Build local, research-only computational pathology workflows with PathML: load and tile whole-slide images, run preprocessing and QC pipelines, manage h5path data, quantify multiplex images, construct spatial graphs, and plan bounded model inference.

A Python library for reading, querying, filtering, and writing genomic file formats including SAM/BAM/CRAM, VCF/BCF, FASTA/FASTQ, and tabix data. Supports pileup, coverage analysis, indexing, and CRAM reference operations with HTSlib integration. Ideal for bioinformatics workflows requiring low-level streaming access to genomic data.

This specialized skill automates the process of converting visual designs (from Stitch) into high-quality, modular React and Vite components. It adheres to modern frontend best practices, enforcing logic isolation via custom hooks, data decoupling using mock data files, and ensuring type safety with TypeScript interfaces. The workflow includes automated asset fetching, style mapping (Tailwind), and rigorous validation cycles to produce production-ready, maintainable code.

Comprehensive guidance for machine learning in Python using scikit-learn. Covers classification, regression, clustering, preprocessing, model evaluation, and pipelines for building production-ready ML workflows.

Use UMAP-learn for fast, scalable nonlinear dimensionality reduction, 2D/3D visualization embeddings, clustering preprocessing, supervised and semi-supervised UMAP, DensMAP, AlignedUMAP, and Parametric UMAP workflows, with guidance on key parameter tuning.

This tool automates the complete specification generation process across four core phases: Initialization, Requirements, Design, and Tasks. It supports both interactive (step-by-step approval) and automatic execution modes. After generating the full spec, it performs a final sanity review to ensure coherence and completeness, providing a ready-to-implement blueprint.