Skill UI

A comprehensive workflow for implementing advanced search and retrieval features within Evernote. It allows users to build complex queries using search grammar, filter notes by tags, notebooks, dates, and completion status, and paginates through large result sets. The service also enriches results by mapping GUIDs to human-readable names for notebooks and tags.

Guidance lets you wrap LLM calls with grammars, regex, and selection constraints to ensure valid JSON/XML/code, enforce formats, heal tokens, and orchestrate multi-step workflows via Pythonic context managers for production-ready automation.

Outlines guarantees valid JSON/XML/code outputs by applying CFG/FSM-constrained token filtering, Pydantic schemas, and grammar-level sampling while supporting local Transformers, llama.cpp, and vLLM backends for fast inference.

SGLang is a high-performance serving framework for LLMs and VLMs that uses RadixAttention prefix caching to deliver structured outputs (JSON/regex/grammar) and agentic workflows with function calls, providing up to 5× faster inference than vLLM across multi-GPU production deployments.

This workflow provides an AI-powered platform for immersive conversation practice. It uses advanced language models (like GPT-4o) and real-time speech APIs to simulate real-life scenarios (e.g., ordering food, navigating). Users receive immediate, detailed feedback on pronunciation (phoneme level), grammar corrections, and vocabulary notes, making it an ideal core module for building language fluency and simulating professional tutoring experiences.

This comprehensive guide demonstrates how to build a robust AI language tutoring session using the Speak SDK. It covers initiating conversations, submitting student responses (text or audio), performing deep pronunciation assessment at the phoneme level, and generating detailed session summaries with grammar and vocabulary metrics. Ideal for developing language education platforms and testing API integration.

This tool utilizes Microsoft RESTler to perform stateful REST API fuzzing. It compiles OpenAPI/Swagger specifications into a usable grammar, allowing users to test complex API interaction sequences. It is crucial for discovering state-related bugs, injection vulnerabilities, authentication bypasses, and resource leaks that standard testing might miss. Ideal for continuous security validation in CI/CD pipelines.

Configure comprehensive CI/CD pipelines for integrating Grammarly's text analysis into your development workflow. This setup uses GitHub Actions to run unit and integration tests on pull requests, validating API connectivity, enforcing content quality gates (e.g., minimum grammar scores), and ensuring documentation quality before merging to the main branch.

Skills this deployment guide covers building, configuring, and deploying a containerized Grammarly writing assistant service. It provides detailed instructions for production setup, including Docker configuration, environment variable management (for OAuth credentials), health checks, and zero-downtime rolling updates when integrating Grammarly's Text API for grammar, clarity, and tone checking.

This guide provides a comprehensive walkthrough for authenticating and initializing the Grammarly SDK/CLI. Learn how to use OAuth 2.0 Bearer tokens to securely connect to Grammarly's services (including Writing Score, AI Detection, and Plagiarism Detection). Use this skill when setting up a new integration, handling API key configuration, or managing token refreshing in a development project.

This guide details setting up a robust local development environment for Grammarly API integrations. It covers establishing the project structure, implementing mocked API responses, and utilizing vitest for comprehensive unit and integration testing. Ideal for developers who require a fast iteration cycle or need to configure reliable API test workflows.

This comprehensive checklist guides developers through the necessary steps to deploy a robust and reliable Grammarly API integration in a production environment. It covers critical aspects such as secure credential storage, rate limit handling, implementing text chunking, designing error resilience patterns (circuit breakers, retries), and establishing comprehensive monitoring and alerting systems to ensure high content quality and system uptime.