Skill UI

A comprehensive meta-skill for validating, testing, and scoring the quality of technical skills within an ecosystem. It performs deep structural checks (SKILL.md compliance), executes Python scripts for syntax, runtime, and output validation, and generates multi-dimensional quality scores using letter grades and tier recommendations. Ideal for new skill authorship, auditing existing components, or integrating rigorous QA gates into CI/CD pipelines.

A comprehensive checklist designed for developers to ensure a language learning integration (Speak) is robust and ready for production deployment. It covers critical technical areas including API authentication, audio processing pipelines, rate limiting strategies, monitoring and alerting setup, and adherence to global data compliance standards (e.g., GDPR, FERPA). Use this when implementing or troubleshooting production-grade features.

A comprehensive skill to identify, analyze, prioritize, and track technical debt across large codebases. It provides three interconnected tools: a Debt Scanner, a Debt Prioritizer (using cost-of-delay frameworks), and a Debt Dashboard. This system helps engineering teams make data-driven decisions to balance new features with necessary maintenance and refactoring work, ensuring the long-term sustainability of software systems.

This skill generates comprehensive Technical Design Documents (TDDs), guiding users through structuring architectural decisions, implementation plans, and risk assessments. It follows industry best practices from standards like Google Design Docs, RFC patterns, and ADRs. It focuses on high-level contracts and architectural 'what' and 'why', rather than specific, low-level implementation code.

Automates guidance for technical diagram analysis, offering best practices for diagrams, charts, presentations, and validating code or visuals to meet visual documentation standards.

Automates the creation of technical troubleshooting guides by following documentation best practices, generating templates, and validating outputs for API docs, user guides, and architecture references whenever you mention troubleshooting patterns.

This skill acts as a specialized tutorial engineering specialist, transforming raw, complex technical concepts or codebases into structured, progressive, and engaging educational content. It applies pedagogical design principles, such as progressive disclosure, spaced repetition, and hands-on exercises, to create documentation, courses, and onboarding materials that teach users how to build, debug, and optimize, rather than just referencing information.

Automatically guides the creation of technical-doc tutorial outlines, covering API docs, user guides, architecture docs, and documentation automation with best practices, production-ready outputs, and validation when you mention “tutorial outline creator”.

This guide provides comprehensive solutions for diagnosing and fixing common technical errors and exceptions encountered when using the TwinMind platform. Whether you are facing API authentication failures, microphone permission issues, transcription timeouts, rate limit exceedances, or audio format incompatibility, this reference offers step-by-step solutions and code examples to ensure smooth operation and successful integration.

This tool collects comprehensive diagnostic data for TwinMind issues. It gathers crucial information including environment details (OS, Node.js version, memory), API health status, network connectivity, and configuration snapshots. It is essential for generating detailed bug reports or preparing for technical support requests, helping developers quickly pinpoint the root cause of complex problems.

Automates the creation of Vitepress configuration for technical documentation, offering guidance, patterns, and validation for API docs, user guides, and architecture docs when the "vitepress config creator" intent is detected.

This skill functions as a documentation architect, capable of generating highly structured wiki catalogues and comprehensive onboarding guides directly from a codebase. It scans the repository, detects architectural patterns, identifies technical layers (presentation, business logic, data access), and creates two distinct guides: a Principal-Level Guide for seniors and a Zero-to-Hero Path for newcomers, ensuring every detail is cited with specific file paths.