Skill UI

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.

Guides using Rube MCP’s Composio toolkit to discover current Datagma tools, verify connections, and execute workflows while adhering to live schemas, session reuse, and required memory parameters.

This guide provides essential best practices for building reliable, fault-tolerant applications in Go using DBOS durable workflows. It covers architectural patterns for integrating DBOS into existing Go code, defining complex workflows, managing steps, and utilizing queues for robust concurrency control. Essential reading for developers setting up, configuring, or testing mission-critical asynchronous processes.

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.

A comprehensive guide for building reliable, fault-tolerant applications using DBOS durable workflows within a TypeScript environment. It covers best practices for initialization, structuring complex workflows and steps, managing concurrency via queues, and integrating DBOS into existing TypeScript codebases.

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.

This skill guides the architectural definition of complex business domains using Domain-Driven Design (DDD). It helps in defining core subdomains, separating services by bounded contexts, and establishing a unified ubiquitous language. It is essential for decomposing monoliths, structuring microservices, and aligning team ownership with clear domain boundaries.

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.

Guides diagnosing Buttercup CRS Kubernetes issues across the crs namespace by triaging pod states, analyzing logs, checking resource pressure, monitoring Redis, and inspecting queues for cascading failures in services such as redis, build-bot, scheduler, and UI.

This specialized skill acts as an expert debugger, designed for deep root cause analysis of software errors, test failures, and unexpected behavior. It guides the user through systematic debugging processes, including capturing stack traces, isolating failure points, implementing minimal fixes, and providing comprehensive reports detailing the root cause, evidence, specific code patches, and prevention recommendations. Ideal for complex bug resolution and quality assurance.

This skill provides a structured methodology for tackling complex bugs, performance bottlenecks, and production incidents. It guides users through reproducing issues, forming hypotheses, performing controlled experiments, and narrowing down the scope using proven strategies and tools. Ideal for deep dive troubleshooting and root cause analysis in complex software systems.