Skill UI

Provides comprehensive observability for the TwinMind AI meeting system. This skill allows users to monitor critical performance metrics, including transcription quality (using Ear-3 model), meeting coverage, action item extraction accuracy, and memory vault health. It is essential for implementing robust, enterprise-grade AI workflows and managing the operational status of advanced meeting AI applications.

This skill optimizes the performance of TwinMind's meeting AI operations. It fine-tunes transcription accuracy and speed using the advanced Ear-3 model, managing audio quality, and implementing caching strategies. Ideal for developers or system administrators needing to integrate, troubleshoot, or optimize high-volume meeting transcription workflows.

This comprehensive guide outlines the production-grade reference architecture for advanced AI meeting systems using TwinMind. It details the end-to-end workflow, integrating high-accuracy transcription (Ear-3), AI summarization (GPT-4, Claude, Gemini), memory vault management, and automatic action item and calendar integration. Use this resource when implementing or managing a robust, automated AI meeting solution.

An expert specialist for rigorous visual validation, focusing on UI testing, design system compliance, and WCAG accessibility verification. This skill systematically analyzes visual evidence for cross-platform consistency, pixel-perfect accuracy, and adherence to modern design standards, utilizing principles from visual regression testing and comprehensive QA methodologies.

This skill answers technical questions about a codebase by grounding all responses strictly within the provided source files. It is ideal for users who need to understand how a specific function works, locate definitions, or gain an overview of a component's implementation. All answers include inline citations to ensure accuracy and traceability.

Defines strict guidelines for creating and maintaining Excel financial models, covering zero formula errors, industry color coding, number formatting, documented assumptions, and mandatory use of Excel formulas to ensure accuracy and auditability before recalculation.

A specialized tool for generating long-form content, including guides, blogs, and tutorials, that maintains a unique, human-like brand voice. It ensures structural integrity and factual accuracy, preventing the generic, boilerplate feel often associated with raw LLM output. Ideal for content teams needing sophisticated, reliable narrative development.

A two-layer memory architecture designed for managing corporate board meeting decisions. It separates raw, unfiltered transcripts (Layer 1) from founder-approved, canonical decisions and action items (Layer 2). This system ensures historical accuracy, tracks overdue tasks via commands like /cs:review, and actively detects topic contradictions and conflicts to maintain governance integrity.

This skill establishes a centralized, canonical 'Source of Truth' for all foundational brand, product, and market information. It captures critical elements like target audience personas, brand voice guidelines, competitive landscapes, and core value propositions. By maintaining this single context document, all subsequent marketing tasks (copywriting, SEO, content strategy) can ensure absolute consistency and accuracy, eliminating the need for users to repeat foundational details.

This skill provides sophisticated project estimation by adapting to human-only, hybrid, or agent-first development modes. It uses research-backed PERT statistics and confidence bands (P50, P75, P90) to forecast development timelines for tasks, backlogs, and capacity planning. It continuously calibrates estimates based on actual completion data, ensuring accuracy in AI-assisted and hybrid development environments.

LEX is a structured truth engine designed to eliminate legal hallucinations by grounding AI agents in verified government references and legislation across 29+ jurisdictions (including US, EU, and CA). It provides deterministic context for comparing legal requirements, drafting compliant business, and employment documents, ensuring high jurisdictional accuracy.

This skill provides a systematic framework for analyzing cyber campaign attribution. It involves collecting, weighting, and correlating diverse indicators—such as infrastructure overlaps, TTP consistency, malware code similarities, and operational patterns—to determine the responsible threat actor. Utilizing structured methodologies like Analysis of Competing Hypotheses (ACH) and the Diamond Model, users can build confidence-weighted assessments to enhance threat intelligence accuracy.