Skill UI

CHIPSEC is an open-source framework designed to assess the low-level security configuration of x86 platform firmware and hardware. It verifies critical security controls such as SPI flash write protection, BIOS locks, SMM/SMRR configuration, and Secure Boot variable integrity. Users can run automated tests, dump SPI flash memories for forensics, and detect potential firmware implants that persist below the operating system layer.

This skill provides a comprehensive, cross-platform workflow for auditing UEFI firmware security. It detects advanced threats like bootkits (e.g., BlackLotus, Bootkitty) and Secure Boot bypasses by verifying the integrity of the platform's trust chain. Key checks include confirming Secure Boot status, assessing the currency and application of DBX revocations, and examining the protection of critical EFI variables using specialized tools. Essential for hardware security assessments and forensic investigation.

This professional tool helps developers and researchers identify the optimal AI model for a specific task, such as coding, reasoning, or image recognition. It queries official HuggingFace leaderboards, filters models based on hardware constraints (e.g., available VRAM or RAM), and provides a detailed comparison table including benchmark scores, parameter size, and licensing information.

A comprehensive guide and skill for deploying large language models (LLMs) locally using the llama.cpp framework and GGUF quantization format. It covers searching the Hugging Face Hub, selecting optimal quantizations (e.g., Q4_K_M), running local servers, and achieving OpenAI-compatible API endpoints. Supports various hardware backends including CPU, Mac Metal, CUDA, and ROCm. Ideal for developers needing offline, private LLM inference.

This skill guides users through building, deploying, and maintaining machine learning applications on Hugging Face Spaces. It provides comprehensive knowledge on selecting the optimal SDK (Gradio, Docker, Static), choosing appropriate hardware resources (ZeroGPU, dedicated GPUs, CPU-basic), and following best practices to ensure ML applications are deployed efficiently and successfully on the platform.

A comprehensive guide for developers building AI demos and GPU compute tasks on Hugging Face Spaces using ZeroGPU hardware. It covers best practices for writing and reviewing code that utilizes the `@spaces.GPU` decorator, managing runtime duration and quota, and understanding the specific CUDA availability model and process isolation rules required for reliable ML deployment.