Skill UI

Implements a robust data pipeline for handling large documents with the Grammarly API. This utility manages text chunking to comply with API character limits (max 100k), processes text in manageable segments, and aggregates multiple scoring metrics (like correctness, clarity, and tone) into a single, comprehensive report. Ideal for large-scale content analysis and data ingestion.

This bundle collects comprehensive diagnostic evidence for troubleshooting Grammarly API integration issues. It captures API connectivity status, text check responses, authentication details, and rate limit headers. Use this when encountering persistent errors, preparing detailed support tickets, or performing automated health checks on Grammarly services.

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 skill implements comprehensive observability (metrics, logging, and alerting) for Grammarly API integrations. It monitors critical performance indicators such as text check latency (p95), API error rates, suggestion acceptance rates, and token consumption. Use this setup when establishing robust monitoring systems, tracking real-time API performance degradation, or implementing proactive alerting to ensure consistent, high-quality user experiences in writing and editing flows.

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).

A comprehensive evaluation suite designed to benchmark the performance of Large Language Models (LLM) and Vision-Language Models (VLM) specifically for home security applications. It covers 143 diverse tests across 16 critical domains, including tool use, prompt injection resistance, contextual reasoning, alert triage, and detailed scene analysis, ensuring robustness for smart home AI systems.

This skill provides a comprehensive workflow for scheduling social media posts using the Hootsuite REST API. It manages the entire publishing lifecycle: users first upload media (images or videos) to obtain a media ID. Subsequently, the skill uses this ID, along with text content and target profiles, to schedule the post across various social networks. It supports both single and bulk scheduling operations, ensuring efficient content automation.

This skill analyzes an existing codebase using domain context and architectural decisions to identify architectural friction and "deepening opportunities." It proposes refactored improvements to increase module depth, enhancing testability, improving maintainer locality, and making the codebase more navigable for AI tools. It focuses on transforming shallow modules into robust, deeply leveraged components.

This skill serves as a comprehensive entry point for initiating new development work. It systematically analyzes project metadata (specs, roadmaps) to determine the optimal work decomposition path (new spec, existing extension, or simple fix). By conducting deep context loading and guiding structured dialogue, it refines vague ideas into clearly defined, actionable scope boundaries. Finally, it proposes multiple concrete technical approaches, complete with trade-offs and estimates, ensuring the resulting project scope is well-defined and ready for implementation.

This comprehensive tool analyzes a detailed specification by synthesizing data from multiple source files (JSON, Markdown, etc.). It tracks phase completion, identifies task progress, maps boundary contexts (upstream/downstream dependencies), and proactively highlights necessary revalidation points or blockers, providing a single, unified report of the spec's completeness and current development state.

A deep dive into handling typed content blocks (text, image, tool_use, thinking, document) in LangChain 1.0. This skill standardizes multi-modal message composition, tool-call iteration, and complex content handling across providers like Claude, GPT-4o, and Gemini, solving key API discrepancies.