Skill UI

Automate Clockify time tracking via Claude Code, letting you log entries, query history, manage workspaces, and handle running timers through natural language commands for streamlined billing, reporting, and team audits.

Automates guidance, code, and configuration for Google Cloud Function scenarios, helping engineers follow best practices for compute, storage, BigQuery, Vertex AI, and other GCP services.

Automates guidance for configuring Google Cloud Logging sinks, offering best practices, production-ready configurations, and validation steps across compute, storage, BigQuery, Vertex AI, and related services.

Guides GCP cloud monitoring alert tasks by detailing steps, configs, production-ready code, and validation whenever alert-related requests arise for compute, storage, BigQuery, Vertex AI, or native services.

Automates Cloud Run service configuration within GCP, providing step-by-step guidance, best practices, ready-to-deploy configs, and validation for compute, storage, BigQuery, and Vertex AI workflows.

Guides GCP users through automating Cloud Scheduler job creation with best practices, ready-to-run code, and validation, ideal for implementing cron-driven workloads across compute, storage, BigQuery, Vertex AI, and Cloud Run.

Automated Cloud SQL setup guidance that activates within GCP Skills and delivers step-by-step configuration, production-ready code, and validation against best practices for compute, storage, BigQuery, Vertex AI, and other GCP services.

Automates Contentful GraphQL workflows via the Composio Rube MCP toolkit by first querying RUBE_SEARCH_TOOLS for current schemas, verifying contentful_graphql connections, and executing schema-compliant tool calls with RUBE_MULTI_EXECUTE_TOOL while handling sessions, pagination, and memory.

This skill provides a comprehensive guide to implementing the Command Query Responsibility Segregation (CQRS) pattern. It is crucial for separating read and write concerns, allowing independent scaling of read models, optimizing complex query performance, and building robust, event-sourced architectures when read and write data models differ.

Automates common CTE query builder tasks for data analytics, offering best-practice guidance, production-ready SQL snippets, and validation against standard requirements when you need help with BI, visualization, or statistical analysis.

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.

Provides a practical checklist and principles for designing database schemas, indexing strategies, query optimization, and migration planning for both SQL and NoSQL systems, plus guidance on spotting and fixing N+1 issues and denormalization trade-offs.