Skill UI

This comprehensive guide details the architecture of memory for intelligent agents. It covers the distinction between short-term context, long-term vector storage, and advanced cognitive models (CoALA framework: semantic, episodic, procedural). Learn how to implement robust memory retrieval mechanisms, select appropriate vector databases (Pinecone, Qdrant, etc.), and utilize embedding models to build truly context-aware, persistent AI applications.

Helps analyze current CPU, memory, storage, and network utilization to forecast future infrastructure needs, identify bottlenecks, and recommend scaling tactics with cost context for proactive capacity management.

Helps analyze current CPU, memory, storage, and network utilization to forecast future infrastructure needs, identify bottlenecks, and recommend scaling tactics with cost context for proactive capacity management.

Analyzes query workloads with the database-index-advisor plugin to spot missing or unused indexes and recommends creations or removals to boost performance and reclaim storage.

Analyzes query workloads with the database-index-advisor plugin to spot missing or unused indexes and recommends creations or removals to boost performance and reclaim storage.

Automates Api Gateway configuration guidance within AWS skills, delivering best-practice steps, production-ready code, and validation for compute, storage, networking, and serverless scenarios.

A comprehensive guide covering security best practices for integrating with the Apollo.io API. This includes secure key storage (using secret managers/env vars), implementing PII redaction for logging, adhering to the principle of least privilege (using standard vs. master keys), and performing key rotation procedures. Essential for building secure and compliant integrations.

Automates database archival workflows by identifying historical records in PostgreSQL or MySQL, applying retention policies, and migrating data to archive tables or cold storage (S3/Azure Blob/GCS) with compression and compliance tracking.

Automates database archival workflows by identifying historical records in PostgreSQL or MySQL, applying retention policies, and migrating data to archive tables or cold storage (S3/Azure Blob/GCS) with compression and compliance tracking.

The official Java SDK for Azure Cosmos DB, providing comprehensive client capabilities for NoSQL operations. It supports global distribution, multi-model data structures, and reactive patterns (async/await). Ideal for developing high-throughput, scalable enterprise applications in Java that require reliable, low-latency data storage.

This library provides a comprehensive Java client for interacting with Azure Table Storage and Cosmos DB Table API. It supports all standard CRUD operations (Create, Read, Update, Delete), advanced querying using OData filters, and batch transactions. Ideal for building scalable, high-performance applications requiring structured NoSQL data persistence on Azure Cloud.

A comprehensive Python SDK for interacting with Azure Table Storage and Cosmos DB Table API. This library enables developers to perform full NoSQL key-value operations, including creating, reading, updating, and deleting (CRUD) entities, executing complex queries using filtering, and managing batch transactions. It supports both synchronous and asynchronous operations, making it ideal for building resilient, high-throughput cloud applications.