Skill UI

This comprehensive workflow provides core CRUD (Create, Read, Update, Delete) operations for Notion integrations. It allows users to perform complex database queries using nested filters (AND/OR) across various property types (select, date, number, etc.). Furthermore, it facilitates the structured creation and updating of pages, making it essential for automating data management, content generation, and complex information retrieval within Notion.

A guide and tooling strategy to optimize programmatic interaction with the Notion API. It helps developers minimize costly rate-limit pressure, reduce engineering overhead, and maximize data throughput by identifying redundant calls, implementing efficient caching, and restructuring database queries. Essential for applications that rely heavily on Notion data synchronization and retrieval.

This guide provides production-ready patterns for integrating with the Notion API using official SDKs for TypeScript and Python. It covers advanced techniques including client initialization, executing complex database queries with filters and sorts, implementing cursor-based pagination for large datasets, constructing and manipulating rich text blocks, and ensuring robust, type-safe error handling.

A comprehensive tool for interacting with Notion via its API. It allows users to perform workspace-wide searches, run complex queries against structured databases using compound filters (AND/OR), retrieve specific pages, and deeply traverse and extract all nested block content. Supports pagination for handling large datasets, enabling full data visibility and structured data extraction.

This skill allows users to programmatically set up a comprehensive observability stack for Oracle Cloud Infrastructure (OCI) resources. It guides the creation of metric queries, alarm rules, custom metric publishing, and detailed log searches using the Python SDK. By automating monitoring and alerting, users gain full control over their infrastructure visibility, eliminating reliance on the OCI Console UI for critical operational checks.

This technique guides users through a rigorous two-phase research strategy. First, conduct a broad search to identify potential papers or authors. Crucially, the system instructs the user to use the extracted "author full name" as a highly specific anchor in subsequent queries. Combining the author's name with details like journal, institution, year, or biographical information ensures the most precise and reliable results, significantly narrowing down the search scope and achieving deep academic insights.

This mechanism is designed to optimize Qdrant's performance when handling queries with very large limits or when paginating results across multiple shards. Instead of forcing every shard to transfer the full requested limit (which is bandwidth-intensive), it uses Poisson distribution statistics to calculate smaller, optimized limits for each shard. This significantly reduces inter-shard data transfer and improves the efficiency of large-scale vector search, while maintaining a very high degree of accuracy.

A comprehensive guide for diagnosing and resolving slow search performance in Qdrant. This resource covers issues related to low QPS (throughput), high latency for single queries, and slow filtered searches. Solutions include tuning HNSW parameters, managing memory pressure, implementing quantization, optimizing indexing, and configuring cluster settings for maximum efficiency.

This skill allows for the generation and execution of complex, optimized SQL queries to derive deep business insights. It supports sophisticated data workflows, including multi-table joins, multi-dimensional analysis (e.g., trend analysis, attribution modeling, comparative analysis), and subsequent recommendations for appropriate data visualizations (e.g., line charts, waterfall charts). It goes beyond simple SELECT statements by planning the query structure, handling schema relationships, and ensuring adherence to best practices.

This skill provides a comprehensive guide for developers on performing fundamental blockchain queries using QuickNode's RPC infrastructure. It demonstrates how to retrieve the current block number, check ETH balances for specific addresses, read data from ERC-20 smart contracts, and fetch transaction receipts. It is essential for initial Web3 integration and blockchain development projects using JavaScript/TypeScript.

A comprehensive guide for diagnosing and resolving common Salesforce API, SOQL, and integration errors. This resource details specific error codes, analyzes root causes (e.g., insufficient permissions, malformed queries, API limits), and provides actionable solutions and best practices for developers and administrators.

This skill provides minimal working examples for connecting to Salesforce, executing SOQL queries, and performing full CRUD (Create, Read, Update, Delete) operations on standard sObjects. Examples are provided in both TypeScript and Python. It is ideal for initial Salesforce integration testing, learning core API patterns, or performing basic data manipulation in a Salesforce environment.