Skill UI

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.

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.

Bilig WorkPaper provides a code-first runtime environment for complex spreadsheet business logic. It allows agents to interact with structured data using formulas and JSON workbooks via deterministic API calls (MCP/TypeScript). This is ideal for replacing unreliable UI automation in use cases like quote calculators, budget modeling, and data validation, ensuring calculation integrity through explicit read-back proof.

A robust API-backed skill for fetching YouTube transcripts, searching video content, browsing channels, and extracting playlists. It functions reliably from any cloud server environment, bypassing limitations found in local tools like yt-dlp or reliance on Google API keys. Ideal for building research corpora, competitive intelligence monitoring, and systematic content analysis.

A comprehensive guide to the Tabular Editor 2 command-line interface (CLI). It provides essential syntax and usage examples for automating data model management. Key functions include deploying models to XMLA endpoints, executing inline C# scripts, running Best Practice Analyzer (BPA), validating schemas, and integrating model processes into CI/CD pipelines in Windows environments.