Skill UI

Design and execute comprehensive social engineering penetration tests, covering phishing, vishing, smishing, and physical pretexting to measure human security resilience and reveal training gaps.

Provides a workflow for detecting QR-code-based phishing that embeds malicious URLs in email images by enabling image OCR, extracting and scanning QR URLs, sandboxing destinations, and extending protection to mobile users plus awareness training.

This skill fetches the latest, live documentation and code examples for specific libraries and frameworks (e.g., Next.js, React, Prisma). Instead of relying on static or outdated training data, it utilizes the Context7 MCP to provide accurate, up-to-date answers for setup questions, API references, and code generation based on current best practices.

This skill automates the execution of red team phishing campaigns using the Python gophish library. It enables users to create sophisticated email templates with tracking pixels, configure SMTP profiles, import target lists from CSV, and launch full-scale simulations. The system tracks key metrics, including open rates, click rates, and submitted credentials, providing essential data for security awareness training and vulnerability assessments.

Interactive training for the GitHub Copilot CLI with guided lessons, quizzes, scenario challenges, and a reference covering slash commands, shortcuts, agents, MCP, and configuration; say “cliexpert” to begin.

PyTorch Lightning Toolkit streamlines PyTorch model training by organizing LightningModules, LightningDataModules, Trainer configs, callbacks, logging, and distributed strategies for scalable multi-GPU/TPU runs in research or production workflows.

This guide provides expert strategies for optimizing GPU inference performance on CoreWeave infrastructure. It covers GPU selection based on workload (LLM, image generation, training), advanced techniques like continuous batching (using vLLM), autoscaling setup (HPA), and benchmarking data. Use this to maximize GPU utilization, minimize latency, and optimize large-scale AI model deployment.

A comprehensive checklist for ensuring GPU workloads, such as inference services or model training pipelines, are fully prepared for production deployment on CoreWeave. It covers critical MLOps and DevOps aspects including autoscaling, resource limits, security policies, persistent storage validation, monitoring setup, and rollback procedures.

This guide provides comprehensive, step-by-step instructions for installing and securely configuring the MindTickle SDK and API credentials. It covers prerequisites, environment variable setup, and includes verification snippets for both Node.js/TypeScript and Python environments. Use this when initiating a new MindTickle system integration or developing an application that requires API access to sales, training, or performance data.

This tool provides a robust local development workflow (dev loop) for integrating with the MindTickle sales enablement and readiness API. It simulates critical data points—such as training modules, user progress, and coaching data—using mock services. This allows developers to build and test complex sales readiness dashboards and features rapidly and reliably, by toggling between a mock mode for fast iteration and a sandbox mode for real-world validation against the MindTickle platform.

This comprehensive curriculum provides a detailed roadmap for mastering exploit development and vulnerability research. It covers fundamental techniques like fuzzy testing using AFL++, and progresses through advanced topics such as in-process fuzzing with Google FuzzTest. It is ideal for structuring professional security training programs or onboarding new research engineers.

qzcli is a powerful command-line interface (CLI) tool designed for managing complex GPU compute jobs on the Qizhi platform. Modeled after industry standards like kubectl, it allows users to log in, discover available resources (nodes, workspaces), submit interactive or highly complex batch jobs (including matrix sweeps), and monitor job status efficiently. It is essential for handling high-performance computing (HPC) and distributed machine learning training workflows.