Skill UI

A comprehensive Python wrapper for the U.S. Treasury Fiscal Data REST API. This tool allows users to query diverse federal financial datasets, including national debt, interest rates, foreign exchange rates, and government revenue/spending statistics. It requires no API key and is ideal for financial modeling, economic research, and data analytics projects.

Vaex is a high-performance Python library designed for processing and visualizing enormous tabular datasets that exceed available RAM (gigabytes to terabytes). It excels at out-of-core DataFrame operations, lazy evaluation, fast statistical aggregation, and machine learning integration on massive datasets. Use it for analyzing large CSV, Parquet, or HDF5 files efficiently on a single machine, without memory constraints.

This guide provides comprehensive instructions for setting up monitoring and observability for Qdrant deployments. It helps users track critical performance metrics, check system health, and diagnose complex production issues such as memory growth or slow query requests. Use cases include setting up Prometheus, Grafana, and implementing robust health checks.

This guide details setting up a robust, local development loop for Adobe integrations (Firefly, PDF, Photoshop). It covers using the `aio` CLI for App Builder, implementing hot reload, and establishing unit/integration testing with mocking to ensure rapid, reliable iteration cycles for Adobe APIs.

A comprehensive guide to setting up a full-cycle local Web3 development workflow. This skill utilizes Alchemy as the RPC provider, Hardhat for local unit testing, and integrates Mainnet forking and Sepolia testnet deployment. It covers initializing the project, configuring smart contract interactions, and implementing advanced testing patterns.

A comprehensive guide to setting up robust CI/CD pipelines for Algolia indexing. This includes running unit and integration tests against live indices, validating index settings against defined configurations, and implementing automated reindexing upon deployment (e.g., triggering on version tags). It ensures the stability and data consistency of your search service.

This comprehensive tutorial guides developers through setting up and utilizing Algolia for advanced search functionality. It covers fundamental operations such as indexing records using `saveObjects`, performing targeted searches, and configuring index settings (like custom ranking and faceting). The guide also demonstrates advanced use cases, including multi-index federation and browsing all records, making it ideal for developers building or optimizing a search backend.

A comprehensive guide for integrating the Algolia JavaScript v5 client into Node.js/TypeScript projects. It covers proper installation, secure management of Application IDs and API keys (Admin vs. Search-Only), and best practices for client initialization, ensuring robust and safe search functionality implementation.

This guide provides a comprehensive, reproducible workflow for setting up an Algolia local development environment. It covers creating separate development indices, seeding mock data, and implementing client mocking for unit tests. This process ensures fast, safe iteration cycles, allowing developers to build and test search features without ever touching production data.

A comprehensive tool for managing annotation datasets, supporting full CRUD operations, label management, and advanced export functionality. It specifically handles COCO format export and facilitates uploading datasets to platforms like Kaggle, streamlining the data pipeline for AI model development.

This guide provides a comprehensive setup for a fast and reliable local development loop for applications powered by the Anthropic Claude API. It covers environment management, implementing hot reloading, tracking API usage costs, and utilizing mock responses to ensure a smooth, rapid iteration cycle for AI developers.

A comprehensive guide for configuring and managing Claude API access across disparate environments (Development, Staging, Production). This skill details how to implement isolated API keys, model selection strategies, and crucial spend controls using Anthropic Workspaces, ensuring secure and reliable deployment pipelines for AI applications.