Skill UI

This skill provides comprehensive scripts and workflows to automatically identify and remove unused AWS resources across multiple services (EBS, S3, EC2, EIPs). By automating the cleanup of orphaned volumes, old snapshots, and stale data, users can eliminate cloud waste, significantly optimizing their AWS expenditure and ensuring a cost-efficient infrastructure.

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.

Changelog Creator automatically surfaces best practices for DevOps basics by guiding requests that mention changelog creator, recommending version-control tips, container checks, and CI/CD fundamentals while also validating outputs and generating ready-to-use code/configurations.

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.

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.

Collects Docker build, compose, and runtime tips including multi-stage builds, isolated dependencies, compose healthchecks, .dockerignore, image analysis, and security anti-patterns to streamline container pipelines.

Provides advanced patterns to significantly boost the performance and scalability of Documenso integrations. Key techniques covered include using templates to drastically reduce API round-trips, implementing metadata caching (e.g., using Redis) to minimize database lookups, and utilizing batch processing with concurrency control (p-queue) or asynchronous job queues (Bull) for handling large volumes of documents efficiently.

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.