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 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.

This skill provides comprehensive management for annotation schemas (configs) and human review workflows (queues) within the Arize platform. It supports defining categorical, continuous, and freeform label types. Users can programmatically apply these annotations to project spans and datasets using the Python SDK, facilitating structured human feedback loops and improving model monitoring.

A comprehensive skill for building, operating, and automating complex, reproducible genomics workloads on the DNAnexus platform. It provides deep integration with the `dx` CLI, `dxpy` Python SDK, and workflow languages like Nextflow and WDL. Use it for data transfer, job monitoring, project automation, and ensuring secure, efficient execution of large-scale biological computations.

This skill provides a comprehensive framework for building, registering, debugging, and operating complex bioinformatics workflows on the Latch platform. It supports multiple industry-standard pipelines (Nextflow, Snakemake) and utilizes the Python SDK, CLI, and Latch Data/Registry. Use this when developing, deploying, or managing robust, data-intensive computational pipelines in a controlled environment.

This guide provides production-ready patterns for integrating with Bright Data proxy services. It covers critical aspects like setting up a proxy client, implementing robust retry logic for transient errors (e.g., 502, 503), managing sticky sessions for consistent IPs, and parsing complex HTML responses. Essential for building reliable, high-scale web scrapers using TypeScript and Python.

This skill guides users through building, deploying, and maintaining machine learning applications on Hugging Face Spaces. It provides comprehensive knowledge on selecting the optimal SDK (Gradio, Docker, Static), choosing appropriate hardware resources (ZeroGPU, dedicated GPUs, CPU-basic), and following best practices to ensure ML applications are deployed efficiently and successfully on the platform.