Skill UI

A comprehensive skill for leveraging massive pre-trained transformer models across diverse AI domains, including Natural Language Processing (NLP), Computer Vision (CV), and Audio. Use this skill for advanced tasks such as text generation, classification, question answering, summarization, object detection, and model fine-tuning on proprietary datasets. It provides streamlined pipelines and granular control for deployment.

Transforms natural-language prompts into Excalidraw JSON diagrams, supporting flowcharts, relationships, mind maps, architecture, DFD, swimlane, class, sequence, and ER views; ideal when users ask to visualize processes or system structures and need editable Excalidraw files.

This powerful tool helps translate natural language requirements into robust, normalized relational database schemas. It can generate comprehensive schema designs, complete with relationships, constraints, and advanced architectural concerns like multi-tenancy, soft deletes, and audit trails. Furthermore, it automates the generation of database migrations (supporting Prisma, TypeORM, Alembic, etc.), corresponding type definitions (TypeScript/Python), seed data, and Entity-Relationship Diagrams (ERD) using Mermaid.

Build, run, and visualize multi-step AI generation workflows via muapi.ai. Translate natural language intents into runnable node graphs, discover or edit existing pipelines, and execute them interactively or through the CLI with automated monitoring.

A comprehensive solution for building advanced, intelligent voice skills for Amazon Alexa. This system transforms standard Alexa devices into ultra-smart assistants by integrating the power of Claude (LLM) as the core brain. It utilizes the full AWS ecosystem, including Lambda, DynamoDB, Polly, and Lex, to enable complex functionalities like smart home control, persistent memory, and natural language understanding.

This methodology converts verbose, natural language system instructions into an atomic, structured-label format (e.g., trigger:, action:). By structuring rules, it bypasses the limitations of natural language processing, ensuring LLMs allocate full attention to every constraint, thereby maximizing compliance, improving reliability, and reducing token usage.

The Stock Analyzer Skill provides comprehensive technical analysis for stocks and ETFs. It utilizes industry-standard indicators like RSI, MACD, and Bollinger Bands to generate actionable trading signals. Users can perform advanced technical comparisons, monitor portfolio strength, and identify chart patterns through natural language queries, eliminating the need for manual indicator calculation.

A powerful operational companion for database developers. This skill assists with the daily lifecycle of database interactions, including translating natural language requirements into optimized SQL queries, generating database migrations, performing comprehensive schema introspection across multiple dialects (PostgreSQL, MySQL, SQLite, SQL Server), and identifying performance bottlenecks through detailed query optimization and index recommendations. It also supports ORM patterns like Prisma and SQLAlchemy.

Compress natural language memory docs (CLAUDE.md, todos, prefs) into caveman-speak to shrink token usage before Claude calls; CLI runs python3 -m scripts, keeps human-readable backups, validates output, and retries targeted fixes, triggered via /caveman-compress.

This utility compresses natural language memory files (like notes, todos, or preferences) into a concise 'caveman' format. It achieves significant token reduction for large text inputs by systematically removing filler words, articles, and redundant phrasing, while rigorously preserving all technical content, code blocks, URLs, and structural integrity. Ideal for optimizing knowledge bases and saving API/LLM input costs.

This comprehensive guide provides a detailed framework for authoring effective and structured SKILL.md files for AI coding agents. It covers best practices for structuring content, including frontmatter fields, defining natural language triggers, establishing core principles, and incorporating advanced elements like decision trees and verification checklists, ensuring agents receive clear, actionable, and consistent technical guidance.

Design, analyze, and refine Azure infrastructure through natural language prompts, generate interactive architecture diagrams locally with bundled scripts, and produce deployable Bicep templates to automate infrastructure-as-code workflows.