Skill UI

OWASP Threat Dragon is an open-source tool designed for security professionals and developers. It guides users through creating detailed data flow diagrams (DFD) to map system architecture. The framework enables the systematic identification of vulnerabilities by applying established methodologies like STRIDE and LINDDUN, culminating in comprehensive, compliance-ready threat model reports.

Harbor is an open-source container registry designed for robust security and compliance. This guide details the comprehensive workflow for securing your container ecosystem by implementing advanced features such as vulnerability scanning (Trivy), image signing (Notary/Cosign), Role-Based Access Control (RBAC), and content trust policies. Learn how to enforce image provenance and maintain strict access control in Kubernetes environments.

This skill is designed for assessing the security of Android inter-process communication (IPC) by systematically testing intents, broadcast receivers, content providers, and exported components. It helps identify critical vulnerabilities such as intent injection, unauthorized data leakage, and component hijacking, which are crucial during mobile penetration testing and security audits.

A comprehensive guide for advanced penetration testing designed to identify flaws in application business rules that automated scanners miss. Focus areas include price manipulation, workflow bypass, privilege escalation, and race conditions. Essential for securing e-commerce platforms, financial systems, and multi-step critical processes.

This tool systematically designs and plans ablation studies crucial for advanced research paper submissions. It simulates the role of a rigorous ML reviewer, guiding the process of isolating the specific contributions of each novel component, testing hyperparameter sensitivities, and addressing potential reviewer questions. The workflow ensures that every experiment has a clear hypothesis, maximizing the scientific impact of the research results.

A comprehensive knowledge-base-first skill designed for deep literature reviews in advanced communication domains. Use it for topics like 4G/5G/6G, wireless networking, satellite communications, Wi-Fi, routing protocols, and cross-layer optimization. It systematically searches structured academic databases (IEEE, ACM, Zotero, etc.) to provide related work, survey papers, and landscape summaries.

This guide outlines the architecture and implementation steps for building a robust, production-ready design system. It covers core elements like design tokens, reusable components, and layout patterns. Ideal for teams needing to maintain design consistency, ensure accessibility, and synchronize design (Figma) with implementation (Storybook).

A multi-layered defense system designed to detect sophisticated prompt injection attacks targeting LLM-powered applications. It combines regex pattern matching for known signatures, heuristic scoring for structural anomalies, and advanced DeBERTa-based classification. Ideal for building robust input validation layers, securing RAG pipelines, and performing proactive AI security scanning.

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.

The Safety Guard skill is designed to prevent destructive actions when working in production systems or when autonomous agents are executing tasks. It offers three protection modes (Careful, Freeze, and Guard) to monitor dangerous commands, lock specific directories, and ensure code changes are confined to intended areas, enhancing system stability and security.

The Santa Method is a multi-agent adversarial verification framework designed to improve output quality by requiring two independent reviewers (Agent B and Agent C) to pass the content before publication. It eliminates single-agent bias and systematically improves outputs through iterative refinement loops until convergence or manual escalation.

Provides an expert methodology to significantly reduce AI API spending (40-80%) without sacrificing user quality. The guide covers cost auditing, optimizing existing systems, and designing cost-efficient architectures using techniques like model routing, prompt caching, output length control, and prompt compression.