Skill UI

This comprehensive workflow provides the blueprint for deeply integrating a SaaS application with Customer.io. It defines a robust event taxonomy and implements service methods to track critical user lifecycle events (e.g., signup, feature usage, upgrades, cancellations). These tracked events act as triggers, powering automated campaigns, welcome sequences, and re-engagement flows, ensuring continuous user nurturing and improving conversion rates.

Automates Customgpt tasks through Rube MCP by discovering tool schemas with RUBE_SEARCH_TOOLS, managing toolkit connections, and executing workflows via RUBE_MULTI_EXECUTE_TOOL.

Guide for automating Customjs tasks through Composio’s toolkit using the Rube MCP interface, covering connection checks, tool discovery, execution calls, and schema compliance while emphasizing repeated tool searches.

Automates Cutt Ly operations by orchestrating Composio toolkits through the Rube MCP interface, ensuring you always discover current schemas, manage connections, and execute compliant workflows for bulk or single tasks.

Defines how to automate Dadata Ru operations through Composio’s Rube MCP, covering prerequisite connection checks, schema-aware tool discovery, execution patterns, and common pitfalls so agents can run reliable workflows with updated tool interfaces.

Automates guidance for data catalog updater tasks, covering ETL, transformation, orchestration, and streaming workflows to keep pipeline metadata accurate.

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.

A comprehensive, expert-level skill for designing, analyzing, and optimizing robust database schemas. It covers normalization, index tuning, choosing between SQL/NoSQL, and implementing zero-downtime data migrations. Provides systematic workflows for schema analysis, performance modeling, and advanced SQL pattern generation.

Automates database-focused test work by offering best-practice guidance, fixtures, and configurations for unit, integration, and mocking scenarios in test automation workflows.

This comprehensive guide outlines the end-to-end process of building production-grade ETL data pipelines using Databricks and Delta Lake. It implements the robust Medallion Architecture (Bronze, Silver, Gold) for scalable data management. Techniques covered include Auto Loader for incremental streaming ingestion, MERGE INTO for upsert semantics, data cleansing, and advanced aggregation into analytics-ready layers. Ideal for establishing reliable data pipelines in a data lakehouse environment.

A comprehensive guide detailing the setup process for Databricks CLI v2 and Python SDK. It covers essential steps for connecting to various Databricks workspaces, including configuring Personal Access Tokens (PAT), interactive OAuth U2M flows, and secure OAuth M2M service principals for CI/CD pipelines. Essential for developers integrating Databricks into their projects.

A comprehensive guide to setting up a fast, isolated local development loop for data engineering projects using Databricks Connect v2, Asset Bundles, and standard IDEs (like VS Code). This workflow allows developers to run PySpark code locally for debugging and unit testing, while managing resource deployment and ensuring consistency across development, staging, and production environments.