Skill UI

Converts text-based documentation into structured Markdown by following explicit instructions, documented options, or reference files, supporting header levels, patterns, platforms, and cleanup steps for polished technical documents.

This skill is designed for understanding complex database structures. It allows users to list all available tables, intelligently filter relevant tables based on natural language queries, retrieve detailed schemas (columns, types, primary/foreign keys), and map inter-table relationships. It culminates in generating the M-Schema format, which is essential for accurately preparing the context needed for advanced SQL query generation.

A comprehensive skill for creating scientific and technical documentation using Markdown and embedded Mermaid diagrams. This method establishes diagrams as text-based sources of truth, ensuring clean version control via Git diffs, native rendering across platforms (GitHub, Notion), and compatibility with AI tools, making it superior to static images.

This advanced skill optimizes LLM context usage by implementing strict rules for prompt caching, efficient log pruning, AST-based code inspection, and compact data structuring. It minimizes token consumption and maximizes information density, suitable for highly technical and iterative development workflows.

This comprehensive checklist guides the secure and robust deployment of AssemblyAI transcription services to a live production environment. It covers critical pre-deployment checks, health monitoring implementations, webhook-based processing setup, and gradual rollout strategies, ensuring high availability and reliability for mission-critical speech-to-text functions.

A guide and toolkit for optimizing Grammarly API costs in large-scale applications. It addresses common cost overruns associated with high text volume by implementing strategies such as result caching, chunking long documents, sample-based scoring, and setting usage budget alerts. Use this when integrating Grammarly into enterprise workflows or managing billing for high-volume content analysis.

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 production-ready patterns for integrating with the Notion API using official SDKs for TypeScript and Python. It covers advanced techniques including client initialization, executing complex database queries with filters and sorts, implementing cursor-based pagination for large datasets, constructing and manipulating rich text blocks, and ensuring robust, type-safe error handling.

This module provides a comprehensive set of abstractions, components, and hooks for building sophisticated rich text editors (based on Lexical). It includes essential UI components like Dividers and DropDowns, alongside powerful hooks for managing real-time selection state, applying typography actions, and handling complex content rendering. It is designed for developers building advanced administrative panels or Content Management Systems (CMS).

Provides comprehensive migration patterns for converting existing v5 Webiny code to the v6 architecture. It details key architectural shifts, including replacing ContextPlugins with Dependency Injection (DI) Services, updating event subscriptions to specialized EventHandler Features, and adopting the feature-based container registration model. Essential for developers and AI agents performing major framework upgrades.