Skill UI

A powerful operational companion for database developers. This skill assists with the daily lifecycle of database interactions, including translating natural language requirements into optimized SQL queries, generating database migrations, performing comprehensive schema introspection across multiple dialects (PostgreSQL, MySQL, SQLite, SQL Server), and identifying performance bottlenecks through detailed query optimization and index recommendations. It also supports ORM patterns like Prisma and SQLAlchemy.

Adds x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base via agentwallet-sdk and X Layer via OKX Payments for safe API purchases and agent-to-agent payments.

This skill detects AI-generated deepfake audio used in voice phishing (vishing) attacks. It functions by extracting critical spectral features (including MFCCs, spectral centroid, and zero-crossing rate) and passing them through advanced machine learning models (like Random Forest and Gradient Boosting). The system supports batch forensic analysis, provides confidence scores, and generates detailed reports to verify the authenticity of recorded voice calls, making it essential for cybersecurity incident response and fraud investigation.

This library provides comprehensive patterns for building robust Clinical Decision Support Systems (CDSS) integrated into Electronic Medical Records (EMR). It ensures patient safety by implementing critical checks such as drug interaction screening, weight/age/renal-adjusted dose validation, and standardized clinical scoring (e.g., NEWS2). The engine uses pure functions, guaranteeing high testability and reliability for critical medical insights.

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.

This skill is designed for running comprehensive evaluations of Hugging Face Hub models on local hardware. It supports multiple evaluation frameworks, including `inspect-ai` and `lighteval`, and provides flexibility in backend selection (e.g., `vllm`, Transformers, or `accelerate`) to optimize for performance and compatibility. Use this for local GPU benchmarking, smoke testing, and backend selection, but note that it does not handle remote job orchestration or result publishing.

Trackio is an essential library for logging, visualizing, and analyzing machine learning training experiments. It provides multi-interface support: a Python API for real-time metric logging and alert firing during training; a Command Line Interface (CLI) for retrieving historical metrics and alerts; and synchronization capabilities to Hugging Face Spaces for persistent dashboards. This tool is crucial for debugging, diagnosing training issues (like loss spikes), and automating ML workflow iteration.

A comprehensive guide for implementing phishing-resistant multi-factor authentication using FIDO2/WebAuthn protocols. Learn to build a complete relying party server using Python, supporting both roaming hardware keys (e.g., YubiKey) and platform biometrics. Covers registration ceremonies, credential storage, and migrating to passwordless authentication.

This skill automates the entire SOC 2 Type II audit lifecycle. It facilitates gap assessment against AICPA Trust Services Criteria (CC1-CC9) and collects continuous evidence from major cloud providers (AWS, Azure, GCP) and identity systems (Okta, Jira). It supports validating control effectiveness, remediation tracking, and overall compliance monitoring for Security, Availability, Processing Integrity, Confidentiality, and Privacy.

A comprehensive workflow for preparing and maintaining grounded, venue-compliant rebuttals to external academic reviews. It handles strict text-only character limits, manages responses for multiple reviewers (e.g., OpenReview style), supports follow-up rounds, and enforces safety gates against fabrication or overpromising. Ideal for the post-submission phase of research publishing.

This skill acts as a critical gatekeeper after a set of experiments are completed. It systematically evaluates raw experimental metrics and outcomes against predefined research claims. The goal is to determine whether the results adequately support a claim, contradict it, or if critical evidence is missing. It guides the research team towards the next decisive action: pivoting the hypothesis, supplementing the data, or confirming the findings. Use this before writing final reports.

Transformers.js allows developers to run state-of-the-art machine learning models directly within JavaScript/TypeScript environments (browser, Node.js). It supports a comprehensive range of tasks including NLP (text generation, summarization, translation), Computer Vision (image classification, object detection), and Audio processing. This capability enables building fully client-side, multimodal AI applications without requiring a dedicated Python backend or server infrastructure.