Skill UI

A comprehensive runbook for rapid incident response to Snowflake outages, query failures, and pipeline breaks. It guides users through systematic triage, checking account usage, managing resource monitors, and performing rollbacks using time travel, ensuring minimal data loss and quick recovery after critical data infrastructure incidents.

This guide provides comprehensive steps to install and configure Snowflake SDKs for Node.js and Python. It covers various authentication methods, including password, secure Key Pair authentication (ideal for CI/CD), and enterprise-grade SSO/OAuth integration. Use this when setting up data pipelines or applications that require reliable and secure connectivity to the Snowflake data warehouse.

This comprehensive checklist guides the deployment of Snowflake data pipelines and integrations into a production environment. It covers critical areas such as authentication best practices, warehouse configuration, data pipeline readiness, access control, monitoring setup, and disaster recovery planning. Use it when preparing for a go-live or validating the stability of your production Snowflake setup.

A comprehensive guide to implementing production-grade reliability patterns for Snowflake environments. Covers advanced techniques like Time Travel for point-in-time recovery, cross-region database replication, account failover groups for disaster recovery, and application-level resilience coding (e.g., connection failover logic). Essential for building fault-tolerant, highly available data pipelines.

Master event-driven patterns within Snowflake using native alerts, email integrations, external functions, and cloud notification services (S3/GCS). This skill is essential for building robust, reactive pipelines that trigger actions (like sending alerts, calling external APIs, or ingesting data) in response to data changes or specific system events. Use cases include data quality checks, real-time monitoring, and automated notifications.

This skill provides Continuous Integration (CI) testing for applications built with WebContainer and StackBlitz SDK. It utilizes Playwright browser tests to ensure code reliability and functional correctness across various environments. Ideal for developers integrating WebContainers into CI pipelines or working within the StackBlitz development ecosystem.

This guide details the deploy integration process for automating screen capture and video editing using TechSmith's Snagit COM API and Camtasia Producer CLI. It is designed for developers needing to orchestrate complex content pipelines, such as programmatically capturing specific screen regions, handling various output formats (clipboard, file, printer), and automating batch rendering within a Windows environment using PowerShell or Python.

This skill demonstrates how to automate core content creation operations using industry-standard tools. It utilizes the Snagit COM API to perform batch screenshot and video captures, and leverages command-line execution (CamtasiaProducer) to batch-render and export video projects (e.g., .tscproj to MP4). Ideal for building robust documentation pipelines, automating content workflows, or integrating TechSmith tools into larger scripting systems.

This guide provides comprehensive technical guidance for performance tuning and automating workflows using TechSmith Snagit COM API and Camtasia. It details key considerations for robust scripting, including using PowerShell and Python interop, defining various input/output types (like desktop, window, clipboard, or file), and solving common errors (such as COM registration failures or permission issues). Use this skill when building complex, automated pipelines for screen capture and video editing.

This guide provides production-ready patterns for automating TechSmith tools using PowerShell and COM API. It features a Snagit COM API factory pattern for consistent screen capture and a batch rendering function for Camtasia. Use these patterns to build robust, automated content pipelines for screen recording and professional video production.

A comprehensive reference architecture for building scalable, cost-effective, and robust AI services. This system manages the full AI lifecycle, including intelligent model routing (for quality/cost trade-offs), response caching (using Redis), asynchronous batch processing, and fine-tuning pipeline management via Together AI's OpenAI-compatible API. Ideal for enterprise applications requiring high availability across multiple open-source models.

This guide covers essential security best practices for utilizing Together AI's OpenAI-compatible API for inference, fine-tuning, and model deployment. It provides code examples and architectural advice on secure API key management, robust webhook signature verification, input validation using schemas (like Zod), and implementing data redaction pipelines to protect sensitive information in logs and datasets. Essential for building secure AI applications.