Skill UI

An automated verification system designed for critical healthcare application deployments (EMR/EHR). This harness enforces stringent patient safety standards by running multiple mandatory test gates, including Clinical Decision Support System (CDSS) accuracy, Protected Health Information (PHI) leakage prevention, and data integrity checks. A single failure in critical gates automatically blocks deployment, ensuring regulatory compliance and optimal patient safety in the CI/CD pipeline.

A comprehensive guide to building HIPAA, GDPR, and other global compliance systems for healthcare applications. This pattern outlines best practices for protecting Protected Health Information (PHI) and Personally Identifiable Information (PII) by implementing robust access controls, Row-Level Security (RLS), and mandatory audit trails. Essential for any feature handling patient or clinical data.

This skill is designed for performing local, on-premise evaluations and benchmarking of models hosted on the Hugging Face Hub. It facilitates running specialized evaluation frameworks like `inspect-ai` and `lighteval` against local hardware. Users can select optimal inference backends, including `vllm` for high throughput, or fall back to Hugging Face Transformers or `accelerate`, ensuring comprehensive smoke testing and task selection for LLMs. It focuses purely on the local execution process, separate from remote jobs or publishing workflows.

Gradio is a powerful Python library designed for building and sharing machine learning demo applications and user interfaces (UIs). It allows developers to quickly create interactive web prototypes, from simple interfaces to complex chat interfaces, streamlining the process of demonstrating ML models without needing extensive front-end coding knowledge. It supports advanced features like custom layouts and streaming outputs.

A comprehensive tool and API wrapper for accessing, parsing, and analyzing research papers published on Hugging Face and arXiv. Users can input various URLs or IDs (e.g., arXiv ID) to retrieve structured metadata, full markdown content, and automatically link associated resources such as models, datasets, and application spaces. Ideal for researchers and developers needing structured access to cutting-edge AI knowledge.

Trackio is a comprehensive library for logging, visualizing, and managing ML experiment metrics and alerts. It allows developers to use a Python API to log metrics and set diagnostic alerts (INFO/WARN/ERROR) during training. Furthermore, a dedicated CLI enables metric retrieval and dashboard viewing, syncing data to Hugging Face Spaces for persistent, remote monitoring, making it ideal for autonomous ML agent workflows.

Fine-tune or train object detection, classification, and SAM/SAM2 segmentation models on Hugging Face Jobs, leveraging managed GPUs, automatic dataset checks, and Hub persistence without local GPU setup.

This skill detects stealthy lateral movement by monitoring the abuse of DCOM objects (MMC20.Application, ShellBrowserWindow, ShellWindows). It correlates Sysmon telemetry (Process Creation, Network Connections), WMI analysis, and RPC traffic to identify unauthorized remote command execution, aiding threat hunters and red teams.

A comprehensive guide to hardening Windows Active Directory environments using Group Policy Objects (GPO). This skill configures multiple layers of defense, including AppLocker, Controlled Folder Access, and Attack Surface Reduction (ASR) rules, to prevent ransomware execution paths, block unauthorized file modifications, and restrict lateral movement across the domain.

This comprehensive guide implements External Attack Surface Management (EASM). It integrates leading industry tools like Shodan, Censys, subfinder, httpx, and nuclei for deep reconnaissance. Users can systematically discover assets, enumerate subdomains, fingerprint services, scan for vulnerabilities, and calculate weighted exposure scores based on professional OWASP methodology. Ideal for continuous security monitoring and deep penetration testing.

This guide details establishing confidential computing environments using AWS Nitro Enclaves for hardware-level isolation. It covers building secure enclaves for sensitive data (PII, PHI), configuring cryptographic attestation workflows, and integrating KMS policies using PCR-based conditions. Learn to harden workloads by moving secret decryption into isolated, verifiable hardware boundaries.

This guide outlines the implementation of Remote Browser Isolation (RBI) as a core component of a Zero Trust architecture. It details policies for URL risk classification, Data Loss Prevention (DLP) controls, and Content Disarm and Reconstruction (CDR). This is essential for hardening web access, protecting against zero-day exploits, phishing, and preventing data exfiltration from untrusted web content.