Skill UI

Actionable guidance for analyzing plans, indexing, connection pooling, partitioning, and detecting N+1 queries to keep PostgreSQL and MySQL workloads fast and reliable.

A specialized skill for optimizing database performance and scalability across PostgreSQL and MySQL. It guides users through analyzing slow queries, interpreting execution plans (EXPLAIN ANALYZE), designing optimal indexing strategies, tuning configuration parameters, and resolving bottlenecks like lock contention. Essential for DevOps engineers, database administrators, and backend developers.

Automates database schema design guidance for backend projects, activating whenever you ask about schema patterns, best practices, or implementation in node.js, Python, Go, microservices, caching, or messaging contexts.

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.

This skill provides a comprehensive guide on implementing robust data governance patterns within Delta Lake, leveraging Unity Catalog. It covers essential compliance topics such as data classification tagging, handling GDPR 'right-to-deletion' requests for PII, and establishing automated data retention policies. Use this when designing secure, compliant, and auditable data pipelines in a data lake environment.

This skill guides the setup and management of Databricks resources across development, staging, and production environments. It covers best practices for using isolated workspaces, managing environment-specific secrets, implementing Asset Bundles, and integrating with CI/CD pipelines to ensure consistent and reliable data deployments.

This skill provides comprehensive observability for the Databricks platform. It enables users to monitor job run histories, track cluster utilization and associated costs (DBU), detect slow SQL queries, and audit access logs. Use it for setting up robust monitoring dashboards, ensuring pipeline health, and optimizing resource consumption across the entire data stack.

A comprehensive guide and implementation pattern for managing rate limits, throttling, and ensuring idempotency when interacting with the Databricks API. This skill demonstrates techniques like exponential backoff with jitter, token-bucket rate limiting, and using idempotency tokens to make bulk and concurrent API calls reliable and efficient, minimizing HTTP 429 errors.

A comprehensive guide to production-ready patterns for the Databricks Python SDK. This skill covers essential best practices including singleton client initialization, structured error handling with typed exceptions, context-managed cluster lifecycle control (ensuring cleanup), and implementing type-safe job definitions. It is essential for building robust, maintainable, and idiomatic integrations with the Databricks platform.

Automates Datadog monitoring workflows via Composio’s Rube MCP, letting you query metrics, search logs, manage monitors and dashboards, and create events or downtimes after connecting to the MCP toolkit and tools list.

A comprehensive guide detailing best practices for building reliable, fault-tolerant Python applications utilizing DBOS durable workflows. This resource is essential when integrating DBOS into existing Python codebases, defining complex workflows and steps, managing concurrency via queues, or implementing robust workflow communication patterns.

This skill guides the application of Domain-Driven Design (DDD) tactical patterns, including defining aggregates, modeling value objects, managing repositories, and utilizing domain events. It helps developers translate complex domain rules into clean, robust, and behavior-rich code structures, ensuring invariants are explicitly enforced.