Skill UI

This skill performs comprehensive regulatory gap analysis across major compliance frameworks, including GDPR, CCPA, ADA, PCI-DSS, and CAN-SPAM. It analyzes live websites and codebases to produce a detailed compliance scorecard, identifies specific non-compliant gaps, and generates a prioritized remediation roadmap with effort estimates. Essential for businesses ensuring legal adherence across multiple global jurisdictions.

This advanced skill orchestrates a comprehensive, multi-agent review of legal contracts. It analyzes risk, compliance, missing protections, and plain-English readability simultaneously. By simulating a team of legal specialists, it aggregates findings into a single executive report, complete with a quantifiable Contract Safety Score (0-100). Ideal for founders, freelancers, or small businesses needing expert legal insights without retaining full-time counsel.

Audits experiment integrity before claiming results using cross-model review to detect fake ground truth, score normalization fraud, phantom results, and insufficient scope. Use when user says audit experiment, check experiment integrity, audit results, or after experiments complete before writing claims.

This skill provides integration with Grammarly's Writing Score API, enabling applications to automatically score documents. It evaluates writing quality across four dimensions: engagement, correctness, clarity, and tone. Use this when building content quality assurance features, implementing quality gates, or providing detailed, multi-dimensional feedback on written content.

Utilizes Grammarly's advanced APIs to conduct comprehensive content auditing. This skill detects the likelihood of AI-generated content and checks the provided text against vast databases for plagiarism. It provides a combined score assessing overall writing quality, clarity, and academic integrity, making it essential for professional, academic, and published content review.

This skill provides a comprehensive set of APIs for advanced writing analysis using Grammarly. It allows users to score text based on engagement, correctness, clarity, and tone. Furthermore, it includes functionality for AI detection and sophisticated plagiarism checking. It is ideal for developers needing to build content verification, grammar-checking, or academic integrity tools.

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.

This guide provides techniques to optimize Grammarly API interactions, focusing on reducing latency and improving throughput. Learn how to implement efficient caching strategies (like LRU cache) for repeated score checks and how to execute multiple API calls in parallel using Promise.all. Use this when experiencing slow response times or needing cost/performance improvements in Grammarly integrations.

A comprehensive guide and implementation pattern for handling rate limits, backoff, and idempotency when integrating with the Grammarly Text API. It provides a custom rate limiter and robust retry logic to manage API throughput, ensuring reliable processing of high volumes of documents and stable interaction with various endpoints (e.g., writing score, plagiarism checks).

Provides comprehensive guidance for upgrading and migrating between different versions of the Grammarly API. This skill covers version detection, critical schema changes (e.g., score breakdown), migration checklists, and implementing robust rollback strategies for writing analysis, AI detection, and plagiarism checking.

A guide on handling asynchronous operations and event notifications for the Grammarly API. Since Grammarly does not provide native push webhooks, this skill demonstrates techniques for implementing polling mechanisms (e.g., for plagiarism detection) and building a robust event bus to reliably handle streamed results, such as score completion or AI content detection alerts.

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.