Skill UI

A comprehensive guide for designing and implementing professional, data-driven dashboards across web, iOS, and Flutter. It details core principles like modular grid layouts, establishing data hierarchy (KPIs, charts), and maintaining minimalist aesthetics. Includes specific code examples using CSS Grid, SwiftUI, and Flutter widgets to ensure optimal user experience and data clarity.

A comprehensive design guide for creating ultra-compact, high-utility user interfaces. Ideal for professional tools, financial dashboards, or IDEs where information density is paramount. Principles include compact layouts, monospace typography, high contrast dark themes, and maximizing functional pixels while minimizing whitespace.

Expert data engineer specializing in scalable data pipelines, modern data warehouses, lakehouse architectures, and real-time streaming. Proficient in Apache Spark, dbt, Airflow, Kafka, and cloud-native platforms (AWS, Azure, GCP). Focuses on reliable, performant, and cost-effective data solutions with strong emphasis on data quality and governance.

Design scalable, reliable and cost-effective batch and streaming data pipelines. Covers ETL/ELT, Lambda, Kappa and Lakehouse patterns, Airflow/Prefect orchestration, dbt and Spark transformations, Delta Lake/Iceberg storage, data quality checks and cost optimization.

This skill enables implementing data quality validation using Great Expectations, dbt tests, and data contracts. It is used for building reliable data pipelines, setting up validation rules, and establishing data contracts between teams. It includes automating validation in CI/CD and monitoring data quality metrics to ensure data reliability in production environments.

Expert data scientist for advanced analytics, machine learning, and statistical modeling. Guides the full workflow from exploratory data analysis to production deployment, covering experimentation, forecasting, NLP, and business intelligence for actionable insights.

This skill guides the process of transforming complex, raw data into compelling, actionable narratives. It covers the core structure (Setup, Conflict, Resolution), narrative arcs, and the three pillars (Data, Narrative, Visuals) needed to create high-impact presentations for executives, investors, and non-technical stakeholders. It ensures insights drive clear decisions.

A protocol that provides LLM agents with persistent structural memory of a codebase. It maps the project as a directed graph, tracking not only dependencies (what imports what) but also the 'reasons' (why it connects). This allows agents to understand context, perform impact analysis, and navigate large repositories without loading the entire source code into the context window.

This comprehensive workflow guides the entire database lifecycle, from initial architecture design (covering both SQL and NoSQL) to advanced implementation, optimization, and maintenance. It covers crucial stages like schema design, implementing migrations, optimizing query performance, developing robust data pipelines (using tools like Airflow and dbt), and managing data quality and operations. Ideal for engineers building complex, production-grade data systems.

Implement comprehensive automated database backup strategies for PostgreSQL, MySQL, and MongoDB. Configure retention policies, schedule backups using cron or systemd, and upload to S3-compatible storage. Includes scripts for logical and physical backups, point-in-time recovery, and disaster recovery testing. Essential for DevOps and database administration roles to ensure data safety and business continuity.

This skill simulates a cloud cost optimization expert specializing in reducing infrastructure expenses across AWS, Azure, and GCP. It analyzes complex cloud spending patterns, identifies waste, overprovisioning, and potential savings opportunities. It provides actionable recommendations, implements cost controls, and proposes cost-effective, reliable architectures, ensuring cost efficiency without sacrificing performance or reliability.

This comprehensive guide covers best practices for building robust database systems. It teaches crucial concepts like schema normalization, effective indexing strategies, selecting the right database (e.g., SQLite vs PostgreSQL) and ORM, and optimizing queries to prevent common performance issues like N+1 problems. It emphasizes making context-aware architectural decisions.