Skill UI

A comprehensive guide to optimizing operating costs when utilizing Together AI's OpenAI-compatible API. This resource details strategies for cost reduction across various AI tasks, including inference, fine-tuning, and model deployment. Learn how to select appropriate model sizes, implement caching, leverage batch inference, and utilize Turbo variants to maximize efficiency and minimize spending.

A comprehensive collection of high-quality UI abstractions and components designed for building professional administrative dashboards and user interfaces. It provides essential building blocks such as layout managers, data tables, complex forms, dialogs, and various interactive elements like buttons, pickers, and accordions, ensuring consistency and efficiency in large-scale web applications.

A state-of-the-art solution for real-time object detection using the advanced YOLO 2026 model. This skill detects 80+ COCO object classes (people, vehicles, animals) and outputs bounding boxes with high precision. It features automatic hardware acceleration, optimizing the model for various platforms (CUDA, MPS, OpenVINO, ROCm) to ensure maximum speed and efficiency, whether running on CPU, GPU, or Neural Engine.

This skill provides real-time object detection capabilities by leveraging the Google Coral Edge TPU accelerator. It operates natively within Windows Subsystem for Linux (WSL), allowing high-speed, low-latency inference on live camera frames. By utilizing hardware acceleration, it achieves impressive performance (e.g., ~4ms inference at 320x320), making it ideal for edge computing projects requiring rapid visual analysis and resource efficiency.

A comprehensive guide on how to write, organize, and optimize skills for AI agents using a Test-Driven Development (TDD) approach, focusing on Claude Search Optimization (CSO) and token efficiency.

This skill guides you through writing, debugging, and optimizing Adobe Illustrator automation scripts using ExtendScript (JavaScript/JSX). Learn to manipulate the entire Illustrator DOM—including documents, layers, paths, text frames, and artboards—to automate complex design workflows, perform data-driven batch processing, and export assets to various formats, significantly enhancing creative efficiency.

A systematic auditor designed to evaluate the resilience and quality of complex AI workflows. It scans across five critical dimensions—Prompt Quality, Context Efficiency, Tool Health, Architecture Fitness, and Safety—providing a scored report and prioritized, actionable remediation steps. Use this tool to find hidden flaws and ensure robustness before deploying any AI agent or system.

This skill provides robust automation capabilities for interacting with network devices via SSH using the industry-standard Netmiko library. It is designed to simplify complex network operations, such as configuration backups, status checks, and bulk management across heterogeneous devices, significantly improving operational efficiency.

This tool automatically converts design screenshots or visual exports into structured, functional Vue 3 Composition API components. It supports popular UI libraries like Vant, Element Plus, and Ant Design Vue. By batch converting screenshots, it streamlines the development process from raw design assets to reusable, production-ready code, significantly boosting frontend development efficiency.

A comprehensive guide to planning, executing, and optimizing paid media campaigns across major platforms like Google Ads, Meta Ads, and LinkedIn. Covers full-funnel targeting (TOFU-BOFU), critical KPIs (ROAS, CPA), budget allocation rules, and best practices to maximize customer acquisition efficiency.

This skill provides a comprehensive audit of GitHub Actions workflows. It inspects CI configurations to identify bottlenecks, waste sources, and areas for optimization, helping users reduce runtime, lower costs, and boost overall CI/CD efficiency. It covers caching, concurrency, and trigger reduction.

This skill provides a comprehensive framework for auditing and improving the efficiency of GitHub Codespaces environments. It helps users identify waste sources like oversized devcontainers, inefficient machine types, and poorly scoped prebuilds. By following a structured workflow, users can reduce monthly cloud spending, drastically cut startup times, and maintain optimal development throughput.