Skill UI

This skill automates the setup and configuration of Ollama, allowing users to deploy Large Language Models (LLMs) locally. It performs system assessment (OS, GPU, RAM), selects optimal models based on hardware availability, and handles installation across macOS, Linux, and Docker. Use this when you require free, self-hosted AI alternatives, need offline inference capabilities, or wish to eliminate reliance on costly hosted API services.

This comprehensive runbook provides rapid, structured procedures for incident response on Vast.ai cloud GPU instances. It covers common failure scenarios like spot preemption, training job crashes, and GPU hardware issues. Users can follow the steps for triage, mitigation, recovery, and postmortem analysis to minimize downtime and ensure computational continuity.

This comprehensive guide outlines security best practices for utilizing Vast.ai's cloud environment. It covers secure API key management, hardening SSH access to GPU instances, implementing proper data cleanup before instance destruction, and adhering to robust network security protocols. Use this when setting up, auditing, or deploying sensitive AI/ML workloads on shared cloud hardware.

Cirq is Google Quantum AI’s Python framework for building, simulating, and deploying noise-aware quantum circuits on both simulators and real hardware, complete with experiment templates, transformation utilities, and hardware-specific toolkits.

Qiskit is the world's leading open-source framework for building, optimizing, and executing quantum circuits. It provides comprehensive capabilities for quantum error mitigation, running algorithms on diverse hardware backends (including IBM Quantum and simulators), and supporting applications in chemistry and machine learning. Essential for researchers entering the quantum computing field.

This skill monitors the performance of Claude Code and the local operating system. It diagnoses slowdowns, measures CPU/RAM/Disk usage, checks API latency, and generates detailed system health reports. It helps determine if the bottleneck is local hardware or the remote API connection, providing actionable corrective suggestions to improve the development workflow.

This skill guides the configuration and usage of Hardware Security Modules (HSMs) for robust cryptographic key management. Utilizing the industry-standard PKCS#11 interface, it covers key generation, signing, encryption, and key management directly on hardened physical or soft devices. It is essential for applications requiring the highest level of key protection, ensuring that private keys never leave the secure hardware boundary.

This skill provides comprehensive functionality for integrating and interacting with Hardware Security Modules (HSMs) via the industry-standard PKCS#11 interface using python-pkcs11. It supports key generation, signing, encryption, and verification operations across various platforms, including SoftHSM2, AWS CloudHSM, and YubiHSM2. It is essential for implementing robust, tamper-resistant key management systems and validating compliance against standards like FIPS 140-2/3.

This tool provides comprehensive security assessments for the entire IoT ecosystem, including physical hardware, embedded firmware, network communications, and cloud APIs. Testers can perform deep dives using hardware debugging (UART/JTAG), firmware extraction and reverse engineering, analyzing protocols (MQTT, CoAP, BLE), and simulating cloud-to-device attacks to identify vulnerabilities across all layers of the smart device stack.

A comprehensive guide for building a FIDO2/WebAuthn relying party server using python-fido2. This skill covers the entire lifecycle of passwordless authentication, including secure credential registration (enrollment of hardware keys like YubiKey and resident passkeys) and robust authentication ceremonies. It is essential for migrating traditional password-based systems to phishing-resistant Multi-Factor Authentication (MFA).

This skill provides advanced architectural guidelines for generating premium, award-level web experiences. It guides code generation beyond basic HTML/CSS, focusing on creating immersive digital environments. Key areas include sophisticated motion design (scroll-driven narratives, micro-interactions), establishing strong visual identities (e.g., Brutalism, Glassmorphism), and enforcing strict performance best practices like hardware acceleration and accessibility compliance.

Accelerate scientific Python code on NVIDIA hardware using CUDA and RAPIDS libraries. Optimizes NumPy, pandas, scikit-learn, and ML workloads for GPU execution. Includes profiling, memory management, and kernel optimization to ensure correctness and performance gains.