Skill UI

Access the BRENDA SOAP API to retrieve enzyme kinetic parameters, reaction equations, organism-specific data, and environmental preferences, enabling biochemical research, metabolic pathway reconstruction, and enzyme engineering workflows.

This skill provides a structured guide to querying Azure Monitor activity and sign-in logs using KQL. It is crucial for proactive threat hunting and security incident investigation. Users can detect suspicious administrative actions, including unauthorized role assignments, impossible travel, privilege escalation, and resource modifications within Azure environments. Ideal for SOC analysts and cloud security engineers.

Utilize Bright Data's Scraping Browser with Playwright or Puppeteer to reliably scrape structured data from JavaScript-rendered pages, Single Page Applications (SPAs), and anti-bot-protected sites. This tool manages proxy routing, CAPTCHA solving, and fingerprint management, ensuring robust data collection from complex web environments.

This comprehensive guide teaches users how to set up a ClickHouse database environment. It covers creating a MergeTree table, inserting structured event data using TypeScript, and executing essential analytical queries (e.g., counting, grouping, unique user metrics). Ideal for beginners learning OLAP databases, testing connections, or mastering core data warehousing concepts.

This guide provides comprehensive steps for setting up and authenticating the official ClickHouse client for both Node.js and Python. It covers connecting to ClickHouse Cloud or a self-hosted instance, managing environment variables, and verifying the connection using sample code snippets. Ideal for new ClickHouse projects or integrating data analytics pipelines into existing applications.

A comprehensive guide to setting up and securing API access for Clari. Learn how to generate API tokens, configure environment variables, and test connectivity to export critical revenue intelligence and forecast data to data warehouses like Snowflake or BigQuery. Includes steps for both standard Clari API and Copilot API integration.

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 guide provides robust solutions for handling Hex API rate limits, implementing backoff, and ensuring idempotent API calls. It includes a rate limiter class and a retry mechanism with exponential backoff. Use this when building reliable data pipelines, running large-scale batch analytics, or integrating Hex into CI/CD environments where API call throttling is expected.

A comprehensive skill for managing the entire lifecycle of Volcano Jobs within the KubeSphere environment. It supports the scheduling and execution of complex batch workloads, including deep learning tasks using PyTorch and TensorFlow, MPI, and general resource-intensive jobs. Provides detailed API usage and troubleshooting guidance for job scheduling and resource conflicts.

A structured guide for setting up a robust local development environment for Palantir Foundry data pipelines. This workflow enables users to run Python transforms and unit tests using PySpark against sample data, and mock the Foundry API client, ensuring fast, isolated iteration cycles before deploying code to the official Foundry environment. Ideal for data engineers and developers building complex ETL logic.

Collects comprehensive diagnostic evidence from Snowflake's ACCOUNT_USAGE views, QUERY_HISTORY, and environment details. This tool is essential for troubleshooting persistent performance issues, connection failures, or preparing detailed support cases for Snowflake support, ensuring all necessary logs, query metrics, and resource usage data are collected in one package.

This guide provides a comprehensive methodology for load testing and capacity planning within Snowflake data warehouses. It covers optimizing query performance by adjusting warehouse sizes (Scale UP), simulating high concurrency using multi-threading (Scale OUT), and configuring advanced scaling policies (Standard, Economy, Maximized). Users can benchmark critical queries using SQL and simulate real-world user traffic using Python, ensuring robust and cost-efficient production environments.