Skill UI

A comprehensive guide to setting up a fast, isolated local development loop for data engineering projects using Databricks Connect v2, Asset Bundles, and standard IDEs (like VS Code). This workflow allows developers to run PySpark code locally for debugging and unit testing, while managing resource deployment and ensuring consistency across development, staging, and production environments.

Expert in modern CI/CD pipelines, GitOps workflows, and platform engineering. Specializes in designing and implementing secure, zero-downtime deployment strategies for enterprise-scale applications. Proficient with Kubernetes, ArgoCD, Terraform, and advanced security compliance practices.

A comprehensive, AI-assisted methodology for diagnosing complex software errors in production environments. This skill guides users through the entire debugging lifecycle, from initial triage and gathering observability data (Sentry, DataDog, Jaeger) to generating hypotheses, selecting appropriate debugging strategies (Time-Travel, Chaos Engineering), and performing AI-powered root cause analysis. It culminates in proposing validated fixes and prevention measures.

This skill provides a comprehensive pattern for building robust, scalable, and fault-tolerant asynchronous video generation pipelines using Kling AI. It implements advanced software engineering patterns, including Redis queues (Producer/Consumer), State Machines, and event-driven callbacks. It is designed for managing complex, multi-step AI workflows—such as generate, extend, and download—ensuring reliable background task processing.

This guide documents common mistakes, gotchas, and anti-patterns encountered during Kling AI API integration. It covers critical best practices regarding data types, token management, polling, URL persistence, and prompt engineering to ensure stable and reliable AI video generation workflows. Essential reading for robust development and troubleshooting.

This elite workflow simulates the comprehensive process of a Senior Software Engineer and Product Designer. It enforces a rigorous development pipeline: starting with planning, solid architecture, and TDD implementation, through clean code practices, and technical auditing. Crucially, it includes mandatory, pixel-perfect UX visual validation to ensure the output is flawless, consistent, and production-grade.

This skill covers the full lifecycle of deploying machine learning models into production environments. It focuses on establishing robust MLOps pipelines, including model deployment workflows (e.g., Docker, Canary releases), feature store management (Feast), and comprehensive drift monitoring. Furthermore, it details advanced integration patterns for Large Language Models (LLMs) and building Retrieval-Augmented Generation (RAG) systems, emphasizing operational stability, performance validation, and cost optimization.

This guide outlines common anti-patterns and critical mistakes when using the Windsurf AI IDE. It covers best practices for prompt engineering, managing workspace scope (especially in monorepos), distinguishing between Supercomplete and Cascade usage, and ensuring safe, maintainable AI-assisted coding workflows. Essential for new developers and team auditors.

This comprehensive guide outlines a rigorous, human-in-the-loop engineering workflow for leveraging advanced AI agents to complete complex implementation tasks. It mandates defining clear completion criteria, decomposing work into manageable units, and strategically routing models (Haiku, Sonnet, Opus) based on task complexity. The workflow emphasizes continuous evaluation, regression testing, security review of invariants and edge cases, and strict cost discipline to ensure robust and reliable software development using AI assistance.

This model provides an engineering operational framework for teams that leverage AI agents to generate large volumes of code. It shifts the focus from simple syntax to process quality, robust architecture design, rigorous testing, and systematic risk control, ensuring the development of highly stable and reliable systems in AI-assisted environments.

Curated collection of 37 advanced engineering skills for multi-agent architecture, RAG, MCP servers, CI/CD, databases, observability, security audits, and release workflows. Helps select the right skill for complex platform and reliability tasks.

Helps founders, RevOps, and GTM engineers turn manual revenue processes into reliable, observable automation by mapping tech stacks, designing workflow architectures, and orchestrating AI agents across platforms like n8n, Make, and Zapier.