Skill UI

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.

KubeSphere Core acts as the central operating system for cloud-native application management built on Kubernetes. It provides essential functions, including API aggregation, multi-cluster routing, and multi-tenant separation. It manages core resources and simplifies complex cloud-native deployment and operation tasks.

A powerful skill covering geospatial science, including GIS, remote sensing, spatial statistics, and machine learning for Earth observation. It supports processing of various data types (raster, vector, point cloud) from sources like Sentinel and Landsat, and integrates modern cloud-native workflows (STAC, COG). Ideal for environmental monitoring, urban planning, and advanced spatial modeling tasks.

LaminDB is an open-source, lineage-native lakehouse designed for managing complex biological datasets and computational workflows. It makes diverse scientific data (like scRNA-seq, AnnData) queryable, traceable, and compliant with FAIR principles. Core capabilities include robust data lineage tracking, schema validation, ontology-backed annotation (Bionty), and unified querying across multiple cloud/local storage systems. It is essential for bioinformatics research and building reproducible data infrastructure.

polars-bio is a high-performance Python library built on Polars for genomic interval operations and bioinformatics file I/O. It provides a familiar DataFrame-centric API for interval arithmetic (overlap, nearest, merge, complement, etc.) and supports reading/writing major bioinformatics formats like BED, VCF, BAM, and GFF. Key features include streaming, cloud-native I/O, and SQL query support, making it a fast alternative for large-scale genomic data processing.

Zarr is a robust Python library designed for efficiently storing and managing large N-dimensional arrays. It provides features like chunking, compression, and parallel I/O, making it perfectly suited for cloud-native scientific computing pipelines. It ensures seamless integration with major scientific stacks including NumPy, Dask, and Xarray, while supporting remote storage backends like S3 and GCS.

A deep-dive guide for re-architecting complex enterprise systems within the Adobe ecosystem. This skill covers three major migration patterns: consolidating multiple legacy Adobe APIs into the unified Firefly Services SDK, implementing 'strangler-fig' patterns to replace competitor APIs (e.g., PDFTron, Cloudinary) with native Adobe services, and migrating authentication methods (JWT to OAuth). It provides practical code examples and architectural advice for full platform replatforming.

This reference architecture provides a comprehensive blueprint for deploying Machine Learning models on CoreWeave's GPU cloud infrastructure. It details the structure for multi-model serving (e.g., vLLM, TGI), scalable Kubernetes deployments, shared persistent storage (PVC), and autoscaling mechanisms using KServe/Knative. Use this when designing robust MLOps pipelines, planning high-performance multi-model inference services, or establishing standardized best practices for GPU cloud deployment.

This skill provides a comprehensive solution for deploying containerized applications to Oracle Cloud Infrastructure (OCI). It supports two primary deployment paths: OKE (Oracle Kubernetes Engine) for full Kubernetes orchestration, and Container Instances for a simpler, serverless alternative. Use this skill for automating the entire lifecycle, including pushing images to OCIR, provisioning OKE clusters via Python SDK, and executing Kubernetes deployments using kubectl.

This skill addresses the challenge of managing Dev/Staging/Prod separation in OCI without native account separation. It uses Python to centralize configuration profiles, compartment IDs, and implements safety guardrails (like blocking destructive actions in production) to prevent accidental cross-environment deployments, ensuring repeatable and secure cloud workflows.

This comprehensive guide assists in deploying the Persona identity verification service to various cloud platforms. It covers essential development practices, including Dockerfile generation, secrets management, and implementing robust health checks. Use this skill when integrating KYC or identity verification processes into a cloud-native application.

Rowan is a cloud-native workflow platform providing a unified Python API for complex molecular modeling and medicinal chemistry tasks. It handles property prediction (pKa, descriptors), conformational analysis, molecular docking, and molecular dynamics. Users can run scalable, multi-step drug discovery pipelines and batch screening campaigns without managing local high-performance computing infrastructure.