Skill UI

Automates Box cloud storage operations through Rube MCP, covering uploads, downloads, content search, folder lifecycle, sharing, collaborations, metadata queries, and sign requests; always start with RUBE_SEARCH_TOOLS to sync the current schema.

Learn how to configure and implement robust backend endpoints to handle webhooks from Clerk. This guide covers processing user lifecycle events (creation, update, deletion) and organization events, ensuring seamless data synchronization to your external database. It emphasizes secure practices like Svix signature verification and idempotency protection.

A comprehensive workflow designed to orchestrate the entire lifecycle of large-scale features, from initial requirement gathering and architectural design through multi-service implementation, rigorous testing, and modern deployment strategies (e.g., canary, blue-green). Use this skill when coordinating complex, cross-functional feature delivery across frontend, backend, and data teams, ensuring quality and consistency at every phase.

This skill provides comprehensive management for the entire track lifecycle within a Conductor system. It allows users to perform critical operations such as archiving, restoring, deleting, renaming tracks, and cleaning up orphaned artifacts. Use it when managing track status across active, completed, or archived states to ensure data integrity and system organization.

This guide details production-ready patterns for integrating the Customer.io SDK (customerio-node). It covers best practices including building type-safe clients, implementing robust retry logic with exponential backoff for network stability, performing efficient event batching for high-volume data streams, and managing singleton lifecycles. Use these patterns to build scalable, reliable, and maintainable user behavior tracking systems.

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.

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.

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.

A comprehensive, end-to-end workflow bundle guiding developers through the entire software development lifecycle. It covers project scaffolding, frontend development (React/Next.js), robust backend architecture, database design, rigorous testing (TDD, E2E), code quality review, and modern CI/CD deployment practices for web and mobile applications.

Provides best practices for managing the entire document lifecycle within Documenso integrations. Covers secure PII handling, implementing automated data retention policies (e.g., deleting old drafts and archiving completed documents), and fulfilling GDPR data subject access and erasure requests. Essential for maintaining compliance and secure document storage.

A comprehensive, AI-assisted methodology for diagnosing complex software errors in production environments. This skill guides users through the entire debugging lifecycle, from initial triage and gathering observability data (Sentry, DataDog, Jaeger) to generating hypotheses, selecting appropriate debugging strategies (Time-Travel, Chaos Engineering), and performing AI-powered root cause analysis. It culminates in proposing validated fixes and prevention measures.

This skill provides comprehensive guidance on deploying Expo applications to production across multiple platforms, including iOS, Android, and Web, utilizing Expo Application Services (EAS). It covers the full lifecycle, from building and configuring profiles (eas.json) to submitting builds to App Store Connect, Google Play, and TestFlight. Furthermore, it details deploying web bundles, managing app versions, optimizing App Store metadata, and implementing automated CI/CD workflows.