Skill UI

This expert skill specializes in generating comprehensive, structured documentation for microservices and containerized architectures using the C4 Model. It covers system boundaries, container definitions, technology stacks, inter-container communication protocols, API specifications (OpenAPI/Swagger), and deployment infrastructure (e.g., Docker, Kubernetes). Ideal for documenting complex system deployments and mapping architectural relationships.

This skill provides comprehensive guidance on using LibreOffice Base for full-cycle database management. Users can create ODB databases, define schemas, manage tables, and establish relationships. It supports connecting to various external databases (MySQL, PostgreSQL, etc.), performing complex data operations, and automating professional reports and forms using SQL and Python scripting (UNO). Ideal for building data-driven applications.

Transforms raw datasets into charts, plots, and graphs by analyzing structure, picking suitable visualization types, and rendering them clearly so users can request visualizations or explore patterns and relationships.

This skill automates the creation of comprehensive documentation for relational databases (PostgreSQL/MySQL). By introspecting live schemas, it extracts detailed metadata including table structures, column descriptions, primary/foreign key relationships, indexes, views, and stored procedures. The output provides a data dictionary, entity-relationship map, and schema statistics, crucial for knowledge transfer and database lifecycle management.

This skill guides the definition of relationships and integration contracts between distinct bounded contexts within a complex system using Domain-Driven Design (DDD) patterns. It is crucial for preventing domain leakage, planning anti-corruption layers (ACLs), and clarifying ownership boundaries when multiple services interact, ensuring a robust and scalable system architecture.

Designs normalized database schemas by analyzing requirements, mapping relationships, generating ERD diagrams, and producing SQL CREATE statements so you can build well-structured relational databases and follow best practices.

Guides email marketing pros through building multi-step sequences—welcome, nurture, onboarding, re-engagement, and lifecycle flows—emphasizing clear goals, timing, subject/preview best practices, and copy structure to nurture relationships and drive conversions.

Generates Excalidraw diagrams from natural-language prompts, turning requests such as “make a flowchart” or “draw a system architecture” into ready-to-open .excalidraw JSON files for flows, relationships, mind maps, and architectures.

Automatically generate thorough database documentation using database-documentation-gen, covering ERD diagrams, relationships, columns, indexes, triggers, procedures, and interactive HTML reports whenever you need schema insight for onboarding, reviews, or governance.

Generates realistic database seed data and scripts for development or testing, leveraging Faker libraries to respect schema constraints, maintain relationships, and allow configurable volumes for quick population of demo or test environments.

Enables regression modeling via the regression-analysis-tool plugin by analyzing datasets, choosing linear or polynomial fits, validating models, and reporting metrics so users can predict outcomes or explore variable relationships.

Seaborn is a powerful Python library for creating publication-quality statistical graphics. It is designed for dataset-oriented plotting, making it ideal for exploratory data analysis, multivariate analysis, and visualizing complex relationships, distributions, and comparisons with minimal code. It builds upon Matplotlib, offering high-level interfaces for functions like scatterplots, heatmaps, and pair plots.