Skill UI

Detects and exploits ransomware kill switch mechanisms via mutex, domain, registry, and file checks, enabling proactive vaccination and DNS monitoring to stop execution before encryption.

A comprehensive guide covering best practices for video creation using Remotion within React. This resource provides domain-specific rules and detailed examples for handling complex multimedia elements, including 3D rendering (Three.js), advanced animations, audio synchronization, data charting, captioning, and scene transitions. Learn how to structure professional, high-quality videos entirely through React components.

Extracts business domain knowledge from a codebase, optionally reusing an existing knowledge graph, and generates an interactive horizontal flow graph for dashboard visualization, with lightweight scanning and validation steps.

Defines layered Flutter architecture practices covering UI/data/domain segregation, unidirectional data flow, dependency injection, repositories/services, and testable MVVM patterns to keep apps maintainable at scale.

Comprehensive reference for red teamers and penetration testers on executing Active Directory attacks, covering reconnaissance, credential harvesting, Kerberos abuses, lateral movement, and domain persistence techniques against Windows domains.

Synchronizes mature lessons from a domain-specific memory file into the matching instruction file inside VS Code, letting you choose global or workspace scope so you can preserve knowledge, cut redundancy, and finalize updates in a controlled merge process.

A comprehensive automation toolkit designed to manage the entire Vercel deployment lifecycle and associated infrastructure. Users can programmatically monitor, trigger, and manage deployments, configure custom domains and DNS records, manage environment variables across multiple targets, and oversee project settings and project listings. Essential for implementing robust CI/CD pipelines and managing cloud infrastructure.

This technique is used when multiple failure points (e.g., different test files or subsystems) are discovered, and these failures are independent of each other. Instead of debugging them sequentially, you dispatch separate, focused AI agents to work on each problem domain concurrently. This maximizes efficiency by allowing parallel investigation, dramatically reducing the time spent on large-scale debugging tasks.