Skill UI

Provides advanced patterns for high-volume Notion API interactions. Learn how to manage rate limits (3 req/sec) using worker queues, implement parallel requests with p-queue, and efficiently paginate massive databases (100K+ records) using incremental sync and chunked processing. Essential for building robust, scalable Notion automation tools.

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.

A comprehensive guide to advanced SQL injection (SQLi) methodologies for offensive security assessments and bug bounty hunting. Covers all major injection types, including error-based, boolean/time-based blind, out-of-band, and second-order injection. Provides specific payloads and techniques for MySQL, MSSQL, PostgreSQL, Oracle, and NoSQL databases, along with methods for bypassing WAFs, exploiting GraphQL, WebSocket, and modern API filters. Includes full automation workflows using tools like sqlmap.

Provides a comprehensive, production-ready standard 3-tier reference architecture for Oracle Cloud Infrastructure (OCI). The design covers essential components like VCN, public/private subnets, Internet Gateway, NAT Gateway, Service Gateway, and Autonomous Database. It includes deployment steps using Terraform and OCI SDK Python code, making it ideal for designing new cloud environments or migrating existing patterns from other major cloud providers.

This comprehensive guide details the end-to-end process of migrating data and connecting to Oracle Cloud Infrastructure's Autonomous Database (ADB). It walks through provisioning the ADB using the Python SDK, securely downloading the necessary mTLS wallet, establishing a connection using `python-oracledb`, and executing compatibility-aware data migration via Data Pump.

This skill manages comprehensive identity verification using the Persona API. It covers government ID checks (passport, driver's license), selfie liveness detection, and database lookups (SSN, watchlist). Use this when implementing Know Your Customer (KYC) workflows, reviewing detailed verification results, or building custom approval logic based on multi-source identity data.

This tool systematically searches global patent databases (Google Patents, Espacenet) and academic literature (Google Scholar, arXiv) to determine the novelty and inventive step of an invention. It assesses potential prior art, analyzes overlap risk, and generates a structured preliminary Freedom-to-Operate (FTO) report.

This comprehensive guide details advanced strategies for managing time-series data and data retention in vector databases (like Qdrant). It is designed for scenarios where only recent data is relevant (e.g., social media feeds, news articles). Three core methods are presented: Shard Rotation (recommended for natural time boundaries), Collection Rotation (for per-period configuration needs), and Filter-and-Delete (for simple cleanup). The guide also covers hot/cold tiering to optimize performance.

This guide provides comprehensive guidelines for performing vertical scaling on Qdrant instances. Vertical scaling involves upgrading the computational resources (CPU, RAM, or disk) of existing nodes rather than adding new nodes. It is the recommended first step when single nodes experience resource exhaustion (e.g., RAM usage > 80%, CPU saturation). The guide covers sizing guidelines, best practices for cloud deployment, and clearly defines the thresholds when horizontal scaling becomes necessary.

This guide details how to use Shopify Metafields and Metaobjects via the GraphQL Admin API. Metafields allow attaching key-value custom data to existing resources (like products or orders), while Metaobjects enable defining entirely new, structured content types. Together, they provide a powerful way for developers to model complex custom data, often eliminating the need for external databases to extend e-commerce functionality.

This comprehensive guide demonstrates how to establish a robust Continuous Integration/Continuous Deployment (CI/CD) pipeline for Snowflake data warehousing. It integrates SchemaChange for managing version-controlled database migrations, GitHub Actions for automated testing and deployment triggering, and Terraform for provisioning and managing underlying Snowflake infrastructure (databases, warehouses, roles). Use this when setting up reliable, repeatable, and automated deployment workflows.