Skill UI

This guide provides a deep dive into migrating from the deprecated Grammarly Text Editor SDK to the current, server-side REST APIs. It details the architectural shift from client-side embedding to modern API calls, covering how to implement Writing Score (v2), AI Detection (v1), and Plagiarism Detection (v1) using secure token authentication.

A comprehensive guide detailing three scalable architecture patterns for integrating applications with HubSpot CRM. It helps developers choose the optimal design based on project scale, ranging from simple embedded clients for MVPs, to robust Service Layers with caching and async queues, up to dedicated, enterprise-grade API Gateways for multiple services. Essential for planning complex, high-volume integrations.

Master the complexities of building robust Retrieval Augmented Generation (RAG) pipelines using LangChain. This guide addresses common pitfalls in vector search, such as score semantics differences (L2 vs. Cosine), embedding dimension mismatches, and chunking issues for code/Markdown. Learn best practices for selecting between FAISS, Pinecone, Chroma, and PGVector, normalizing scores, and implementing hybrid keyword+vector search for maximum retrieval quality.

This comprehensive workflow provides full Create, Read, Update, and Delete (CRUD) capabilities for managing Miro boards and all embedded items. Users can programmatically manage entire boards, sticky notes, shapes, cards, frames, and tags using the official REST API v2. Ideal for integrating Miro into automated workflows, automating retrospective reports, or building complex collaboration tools that require structured board content manipulation.

Programmatically manages advanced elements on Miro boards using the REST API v2. Functions include creating connectors, uploading images (from URL or Base64), embedding external content (like videos/URLs), managing custom App Cards, and adding documents. Ideal for building complex, automated, and visually rich workflows, architecture diagrams, and collaborative dashboards.

A comprehensive guide detailing advanced offensive fuzzing methodologies. It covers the full lifecycle from target identification (parsers, network protocols, kernels) to tool selection (AFL++, libFuzzer, Honggfuzz, syzkaller), building harnesses, curating seed corpora, executing fuzz campaigns, and finally, performing crash triage and vulnerability analysis. Essential for security researchers and embedded systems developers.

This guide provides a complete, end-to-end workflow for implementing Know Your Customer (KYC) identity verification using the Persona API. It details both backend implementation (creating inquiries, handling completion callbacks, resuming flows) and frontend integration (embedding the verification UI into web applications). Ideal for secure user onboarding and identity validation processes.

This guide provides a comprehensive walkthrough of the identity verification lifecycle using the Persona REST API. It covers the full process: creating a new inquiry, generating hosted or embedded verification URLs, polling the inquiry status until completion, and finally retrieving detailed KYC results. This is essential for developers building secure, automated user onboarding, and identity verification flows.

Provides comprehensive strategies for migrating embedding models within Qdrant. This guide covers zero-downtime alias swapping, side-by-side A/B testing, and transitioning from dense to hybrid search. It is essential for production environments requiring continuous availability when updating models, dimensions, or switching providers.

A comprehensive guide to diagnosing and enhancing search relevance within Qdrant. This skill addresses reported issues such as low precision, low recall, or irrelevant matches. It guides users through determining if the problem lies in the embedding model, Qdrant configuration, or the query strategy. Techniques covered include optimizing chunk splitting, tuning HNSW parameters, implementing hybrid search, and utilizing reranking for superior result quality.

Provides a systematic guide for diagnosing and improving poor search quality in vector databases like Qdrant. This guide covers troubleshooting issues such as low recall, when approximate search degrades, selecting optimal embedding models, and correctly tuning HNSW parameters, quantization, and filtering strategies. Essential for optimizing RAG and semantic search pipelines.

This guide helps developers choose the optimal architecture pattern for building on Shopify. It compares four validated variants: Embedded Remix App (for admin tools), Headless Storefront (for custom e-commerce experiences using Hydrogen), Backend Integration (for system syncing like ERP), and Theme App Extension (for storefront widgets). Use this resource when starting a new Shopify project to determine the correct development path and technology stack.