Skill UI

This skill provides access to a comprehensive, high-quality icon library designed for modern UI/UX systems. It ensures superior visual consistency across digital products by offering multiple premium styles (Linear, Bold, Two-tone). Users can find existing icons or leverage the integrated AI generator to create unique, perfectly styled custom icons, elevating the design professionalism beyond standard generic sets.

Provides actionable guidance and review checklists for modern clean UI/UX, emphasizing CRAP principles, task-first flows, information architecture, system feedback, consistency, and refined iconography.

This powerful orchestrator coordinates content across Instagram, Telegram, and WhatsApp. It automatically adapts central content into platform-specific formats (captions, templates, hashtags), ensuring brand consistency. Features include unified analytics, synchronized scheduling, and centralized campaign management, maximizing reach and operational efficiency across all major channels.

This skill provides a comprehensive framework for designing and reviewing native iOS applications. It covers adherence to Apple's Human Interface Guidelines (HIG), ensuring consistency across layout, typography, safe areas, and system components (e.g., Dynamic Island). Use this when building or reviewing SwiftUI/UIKit code to guarantee the app feels intuitive, highly polished, and genuinely native to the Apple ecosystem.

A comprehensive guide for building modern, accessible, and scalable user interfaces using the shadcn/ui component library. This document outlines core principles, best practices for component composition, styling rules (including Tailwind CSS usage), and structured approaches for forms and layouts. Essential for developers building complex design systems and ensuring code consistency.

Guides Sankhya dashboard developers on HTML/JSP/Java patterns, SQL sanitization, BI parameterization, and UI/UX consistency so dashboards load reliably with lazy-loading states and structured queries.

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.

A lightweight CLI tool for systematically comparing various coding AI agents (such as Claude Code, Aider, and Codex) head-to-head. It measures crucial metrics—pass rate, API cost, execution time, and consistency—on reproducible tasks defined via YAML manifests. Use this to objectively select the best agent for your codebase or to validate model updates before production deployment.

This tool serves as a persistent memory and state manager for complex coding projects within the Claude environment. It allows users to initialize new projects with structured metadata, track detailed session progress (including summaries, decisions, and next steps), and save the entire context reliably. By ensuring project continuity across disconnected sessions, it drastically improves the flow and consistency of the development workflow.

This technique audits user interaction flows (like button clicks) to detect subtle bugs that static analysis often misses. It specifically tracks the full sequence of state changes, identifying instances where later actions unintentionally cancel, contradict, or leave the UI in an inconsistent state due to state store side effects or race conditions. Ideal for debugging complex state management architectures (Redux, Zustand).

This guide outlines the architecture and implementation steps for building a robust, production-ready design system. It covers core elements like design tokens, reusable components, and layout patterns. Ideal for teams needing to maintain design consistency, ensure accessibility, and synchronize design (Figma) with implementation (Storybook).

This expert tool provides deep, systematic auditing of datasets to assess completeness, consistency, accuracy, and structural validity. It performs full data profiling, detects anomalies (outliers), analyzes missing data mechanisms (MCAR/MAR/MNAR), and calculates a Data Quality Score (DQS). Use it when validating data for model training, debugging pipelines, or establishing ongoing monitoring checks.