Skill UI

This skill details how to set up automated CI/CD pipelines for Databricks data pipelines. It leverages Declarative Automation Bundles (DABs) and GitHub Actions to manage the entire deployment lifecycle. Key functionalities include running unit tests on pull requests, validating and deploying to staging environments, and promoting validated code to production upon merge, ensuring reliable and automated data infrastructure.

Use Declarative Automation Bundles (DABs) to manage and deploy Databricks assets, including jobs, DLT pipelines, and ML models, using an Infrastructure-as-Code approach. This skill allows defining resources and deployment targets (dev, staging, prod) in a single YAML file, enabling robust CI/CD practices and controlled promotion of changes across environments.

DSPy lets you declaratively program complex language model systems, assemble modular RAG/agent pipelines, and optimize prompts and modules automatically for reliable LM-driven apps.

A comprehensive guide detailing how to implement declarative, Git-based continuous delivery for Kubernetes. It covers setup using industry-leading tools like ArgoCD and Flux CD, adhering to OpenGitOps principles for automated, reliable, and traceable application deployments and infrastructure management.

Provides deep expertise in modern Kotlin patterns, covering structured concurrency via Coroutines and Flow, Multiplatform architecture (KMP), declarative UI using Compose, and server implementation with Ktor. Ideal for building robust, type-safe, and highly maintainable applications across various platforms.

Pyvene is a declarative framework designed for performing causal interventions on PyTorch neural networks. It allows researchers to conduct advanced experiments such as activation patching, causal tracing (ROME-style), and interchange intervention training (IIT). Use this library when you need to test causal hypotheses about model behavior, deeply interpret model components, or ensure reproducibility in advanced AI research.

A comprehensive guide covering modern best practices for building high-performance, declarative user interfaces in SwiftUI. Topics include using the advanced @Observable framework for state management, achieving type-safe navigation with NavigationStack, effective view composition, and optimizing rendering performance for iOS and macOS platforms.

Expert skill for building modern, production-ready Chrome Extensions using Manifest V3 standards. Covers advanced topics including service worker management, content script injection, secure cross-context communication (message passing), and implementing best practices like `declarativeNetRequest` and secure storage APIs.

Master common data transformations using functional programming patterns in TypeScript. This guide covers essential techniques for arrays, objects, and complex data structures, including mapping, filtering, reducing, grouping, and safely accessing nested values. It teaches how to replace verbose, imperative loops with concise, declarative functional code, improving readability and maintainability.

This guide provides a quick reference for 'pipe' and 'flow' utilities in fp-ts. The `pipe` function is used to chain multiple operations to transform a specific value, while `flow` allows you to create reusable, composed functions (pipelines). It is essential for implementing clean, declarative data manipulation and functional composition in TypeScript.

AWT provides AI coding tools with the ability to perform sophisticated End-to-End (E2E) web testing using a real browser. It accepts declarative YAML scenarios and executes them via Playwright. Key features include visual matching (OpenCV + OCR) instead of relying on fragile DOM selectors, automatic platform detection (React, Vue, etc.), and structured failure diagnosis for improved AI coding workflows.

A comprehensive, generics-first utility library for Go, providing over 500 type-safe, functional helpers (like Map, Filter, Reduce, GroupBy) for slices, maps, and channels. Designed to replace manual, verbose loops with declarative code. It supports immutable operations by default and offers progressive complexity variants (lo, lop, lom, loi) to handle different performance needs, from standard transforms to CPU-bound parallelism and lazy iteration. Ideal for writing clean, maintainable, and performant Go code.