Skill UI

This SDK enables developers to build robust document analysis applications using Azure AI Document Intelligence. It provides powerful capabilities to extract structured data from diverse document types, including receipts, invoices, ID cards, and general forms. Developers can analyze documents from local files or URLs to extract key-value pairs, tables, and specific fields automatically.

This SDK enables developers to build robust document analysis applications using Azure AI Document Intelligence. It provides powerful capabilities to extract structured data from diverse document types, including receipts, invoices, ID cards, and general forms. Developers can analyze documents from local files or URLs to extract key-value pairs, tables, and specific fields automatically.

This resource provides a minimal working example for implementing a complete digital document signing workflow using the Documenso SDK. It comprehensively guides users through creating a document, uploading a PDF file, adding designated recipients, placing specific signature fields, and finally triggering the signing process via code examples (TypeScript, Python) or REST API calls. Use this when integrating document signing into new applications or testing core document management capabilities.

FAISS (Facebook AI Similarity Search) is a library designed for efficient similarity search and clustering of dense vectors. It supports indexing and searching on billions of vectors at scale, featuring GPU acceleration and multiple index types (Flat, IVF, HNSW, PQ) to balance speed and accuracy. It is essential for building high-performance, large-scale applications requiring fast k-NN retrieval or pure vector similarity matching, especially in fields like RAG and recommendation systems.

This comprehensive guide outlines a multi-step process for safely upgrading the Linear SDK across major versions. It details how to check for breaking changes (like renamed fields or changed return types), implement compatibility layers using patterns like the Bridge pattern in TypeScript, run thorough unit and integration tests, and establish a robust deployment and rollback plan, ensuring zero downtime migration.

Guides using Rube MCP and the Composio Moosend toolkit to orchestrate Moosend tasks with emphasis on discovering current tool schemas, confirming connections, obeying field types, passing memory, and reusing sessions before executing workflows.

This skill demonstrates the multi-model pattern for creating robust and clean API contracts using Pydantic. It guides the creation of separate models for creation requests (Create), updates (Update), responses (Response), and database documents (InDB), ensuring type safety and clear data validation for Python backends. It also covers best practices like handling camelCase aliases and optional fields.

This comprehensive tool validates Claude Code plugins against official Anthropic specifications and internal enterprise standards. It performs two main types of checks: structural validation (checking `plugin.json` fields, file references, and permissions) and content validation (conducting deep grading using `SKILL.md`). Use this when building, auditing, or preparing plugins for marketplace submission to ensure compliance and quality.

Generates implementation-ready JSON-LD schema markup for 10 types (e.g., FAQPage, Product, Article). It validates all fields against Google rich result eligibility rules, flags missing data with fix instructions, and provides clean, ready-to-paste script blocks to optimize search engine visibility and achieve rich results.

A comprehensive guide to advanced design patterns in Go, focusing on writing highly testable and clear code. It covers principles like keeping interfaces small and composable, defining contracts where they are consumed, preferring concrete return types, utilizing generics over 'any', and mastering the use of struct embedding and field tags for serialization.

This skill demonstrates how to use Playwright MCP to programmatically automate the process of inputting data into a web form. It covers various input types, including text fields, date selection, time setting, and file uploads. The process simulates realistic user interaction for data entry or form submission testing, emphasizing the ability to fill data without final submission for review purposes.

Bulk migrate repo labels and Project V2 metadata into GitHub org issue fields, converting label-based tags into typed fields, optionally removing old labels, and syncing project field values for cleaner structured issue tracking.