Skill UI

A comprehensive guide to designing scalable, cost-effective, and compliant architectures on Microsoft Azure. This skill covers the entire lifecycle, from gathering detailed business requirements (SLA, RPO/RTO) and selecting appropriate service patterns (AKS, Serverless, Data Factory), to generating Infrastructure as Code (IaC) using Bicep. It also includes steps for cost optimization, reserved instance planning, and setting up robust CI/CD pipelines via Azure DevOps or GitHub Actions.

Provides Playwright-driven browser automation for scraping structured data, driving multi-step workflows, managing sessions and anti-detection tactics, and capturing screenshots/PDFs in production pipelines.

This comprehensive workflow guides the design, optimization, and deployment of scalable, cost-effective Google Cloud Platform (GCP) architectures for both startups and large enterprises. It covers the full lifecycle, from gathering detailed requirements (compliance, SLA) and recommending optimal patterns (Serverless, GKE, Data Pipelines) to estimating costs, generating Infrastructure as Code (IaC) using Terraform, setting up CI/CD pipelines, and completing a thorough security review.

A comprehensive guide to advanced development practices on the Snowflake platform. Covers best practices for SQL (MERGE, semi-structured data, colon prefix rule), modern data pipeline construction (Dynamic Tables, Streams/Tasks, Snowpipe), and deep integration with Cortex AI functions and Snowpark Python. Essential knowledge for data engineers building robust, scalable, and intelligent data applications.

An automated verification system designed for critical healthcare application deployments (EMR/EHR). This harness enforces stringent patient safety standards by running multiple mandatory test gates, including Clinical Decision Support System (CDSS) accuracy, Protected Health Information (PHI) leakage prevention, and data integrity checks. A single failure in critical gates automatically blocks deployment, ensuring regulatory compliance and optimal patient safety in the CI/CD pipeline.

`jq` is the standard and powerful CLI tool for querying, filtering, and reshaping JSON data. This skill provides expert-level practical patterns for working with deeply nested structures, handling complex transformations (like flattening, renaming, and grouping), and integrating JSON logic into shell pipelines. Essential for parsing API outputs (AWS, GitHub), log files, and building robust Bash scripts.

A comprehensive guide covering best practices for creating videos using Remotion and React. Learn how to incorporate advanced features such as 3D rendering (Three.js), complex animations, audio synchronization, data charting, subtitles, and sophisticated scene transitions. This skill covers 29 domain-specific rules for professional video pipelines.

A Lodash-inspired, generics-first utility library providing 500+ type-safe functions for common data transformations in Go. It eliminates boilerplate code associated with manual loops, offering declarative methods like Map, Filter, Reduce, and GroupBy. The library supports core immutable operations (lo), concurrent variants (lop), mutable mutations (lom), and lazy iterators (loi), allowing developers to choose the optimal approach based on performance profiling and memory constraints. Ideal for implementing robust, type-safe data pipelines.

A comprehensive Go implementation of ReactiveX for building declarative, type-safe pipelines for asynchronous data streams. It manages complex event sources, handling automatic backpressure, error propagation, and context integration. Ideal for real-time data processing, WebSockets, and event-driven architectures where manual goroutine and channel wiring is complex.

This skill provides comprehensive composable handlers for Golang structured logging using the samber/slog ecosystem. It enables developers to build robust, multi-stage log pipelines by integrating sampling for throughput control, formatters for PII scrubbing, complex routers, and multiple backend sinks (e.g., Sentry, Loki, Datadog). It is essential for building high-observability Go applications.

This toolkit provides comprehensive automated control over CircleCI CI/CD pipelines via Rube MCP. Users can trigger new builds, monitor workflow and job statuses, retrieve detailed job information, download build artifacts, and extract detailed test metadata. It streamlines complex DevOps operations by managing the entire CI/CD lifecycle.

This skill guides the end-to-end process of building automated evaluation pipelines for Python applications powered by Large Language Models (LLMs). It teaches how to define evaluation criteria, instrument the application, build golden datasets, and run real-world evaluations using tools like pixie, ensuring the app's logic (not just the LLM) is rigorously tested. Ideal for QA, benchmarking, and improving LLM-based services.