Skill UI

A comprehensive guide to setting up a minimal working example on Databricks. This skill covers creating a development cluster, uploading and executing notebooks (Python SDK), running tasks as jobs, and setting up a Serverless SQL Warehouse. It is ideal for new users or initializing a basic data engineering project.

This comprehensive workflow guides developers through implementing full-stack product analytics using PostHog. It covers key areas including defining event taxonomies, capturing user actions (client-side), identifying and updating user properties, and performing B2B group analytics. It also demonstrates server-side event tracking using posthog-node, ensuring a complete, accurate, and traceable view of user behavior and product usage across web, mobile, and API sources.

This guide provides minimal working examples demonstrating the three core PostHog functionalities: event capture, user identification, and feature flag evaluation. It covers both server-side (Node.js, Python) and client-side (JavaScript) SDK usage, as well as direct API calls, making it perfect for initial integration setup, testing, and learning basic product analytics patterns.

BGPT Paper Search queries the remote MCP server to retrieve structured experimental data from full-text studies, returning methods, results, sample sizes, quality scores, and conclusions for efficient literature reviews and evidence synthesis.

Enables efficient querying of the bioRxiv preprint server with keywords, authors, dates, and categories, returning structured JSON metadata and optional PDF downloads for literature reviews or tracking research trends.

This comprehensive tool manages the complete lifecycle of ML experiments, providing robust deployment capabilities across diverse environments. It supports running training jobs on local GPUs, dedicated remote servers, Vast.ai, and serverless platforms like Modal. The workflow handles environment detection, code synchronization (rsync/git), dependency pinning, and integrates advanced logging tools (e.g., W&B), ensuring reliable, scalable, and reproducible ML training runs.

Utilize Modal, a serverless GPU cloud, for running complex machine learning workloads, including training, fine-tuning, and inference. This tool provides a zero-config environment, eliminating the need for SSH setup or Docker management. It features automatic scale-to-zero billing and local-first execution, making high-powered GPU compute accessible for debugging and small-scale experiments.

Securely inspect and automate microscopy data workflows against OMERO.server using omero-py, BlitzGateway, CLI, tables, annotations, ROIs, rendering, and documented APIs. Support scoped inventory, metadata export, and reviewed write operations.

This guide details how to configure and manage resource constraints in ClickHouse, addressing server-side limits, user quotas, and connection management. Use it when encountering "too many simultaneous queries," optimizing resource usage, or implementing robust rate limiting strategies in applications connecting to ClickHouse. Covers SQL quota setup and client-side pooling techniques.

This comprehensive guide provides advanced techniques to diagnose and significantly boost ClickHouse query performance. It covers analyzing slow queries using system tables, optimizing ORDER BY keys, implementing data skipping indexes (Bloom, Set, MinMax), utilizing projections for pre-aggregation, and fine-tuning server settings and materialized views. Essential for high-volume OLAP environments.

This comprehensive checklist guides users through critical operational readiness steps for a ClickHouse production deployment. It covers server tuning (config.xml), schema design best practices, robust backup strategies (S3), detailed monitoring setup (Prometheus/Grafana), security hardening, and application integration verification. Use this document before go-live, during major upgrades, or when auditing production systems to ensure maximum stability and performance.

This powerful tool compares identical tables across two SQL Server instances (e.g., Production vs. Staging). It efficiently detects missing rows, extra columns, schema drifts, and data mismatches. Leveraging Python and Apache Arrow for fast columnar data transfer, it provides a comprehensive reconciliation report, making it essential for data migration validation and ETL verification.