AI Agent Skills

Google quantum computing framework. Use when targeting Google Quantum AI hardware, designing noise-aware circuits, or running quantum characterization experiments. Best…

2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.

🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱

这是我自己做的一个类似滴滴打车的Android出行项目，主要针对滴滴等出行平台一直饱受质疑的“人车不符”问题，以及当前越发火热的或计划和出海战略，给出行项目增加了下面几个功能： 1. RFID识别验证功能：在司机证件或者车内识别硬件嵌入RFID识别芯片，乘客使用手机读取到芯片信息，并且通过网络发送到出行平台数据库进行验证（我用JNI加了…

Official AMD catalog of AI agent skills. Empower your AI agents with AMD's optimized SW stack.

Design, implement, optimize, and review SIMD code in .NET. USE FOR: vectorizing scalar loops with TensorPrimitives, Vector64/128/256/512, or platform hardware intrinsics…

Use for NVIDIA-related requests where an NVIDIA skill might help, even if the user did not ask for a skill. Trigger on NVIDIA products, hardware, software, SDKs

Reviews React Native TV apps for focus/D-pad navigation, 10-foot UI layout, TV playback/DRM integration, low-memory TV performance, and TV accessibility. Use when buildi…

Collective Knowledge (CK), Collective Mind (CM/CMX) and MLPerf automations: community-driven projects to facilitate collaborative and reproducible research and to learn…

Zero-copy PDF text extraction library written in Zig. High-performance, memory-mapped parsing with SIMD acceleration.

Libva is an implementation for VA-API (Video Acceleration API)

Enable Samsung Galaxy Book features (Quick Share, Multi Control, Samsung Notes, Galaxy Buds) on any Windows PC. Intelligent installer with 21 device profiles, package ma…

DePIN for Vintage Hardware — Proof-of-Antiquity blockchain where old machines outmine new ones. AI-powered hardware fingerprinting, 15+ CPU architectures, Solana bridge…

AeroJAX: A differentiable, structure-preserving framework for real-time flow simulation, control, and inverse design. Architected for neural operator integration and lat…

PMetal: high-performance Apple Silicon framework for local LLM inference, LoRA/QLoRA fine-tuning, serving, quantization, and MLX/Metal acceleration.

Distributed tensors and Machine Learning framework with GPU and MPI acceleration in Python