Skill UI

A powerful security tool for querying Certificate Transparency (CT) logs via crt.sh and pycrtsh. It detects potential security threats such as phishing domains, unauthorized certificate issuance, and shadow IT infrastructure. The tool specifically monitors new certificates for typosquatting and brand impersonation using advanced distance metrics, enabling proactive security threat detection.

BloodHound is a powerful, graph-based reconnaissance tool specifically designed for Active Directory environments. It utilizes graph theory to map deep, hidden relationships and potential attack paths—such as privilege escalation chains—from compromised low-value accounts to high-value targets like Domain Admins. This tool is essential for authorized red team exercises and penetration testing engagements.

This tool implements network segmentation based on the Purdue Enterprise Reference Architecture (PERA). It helps secure Industrial Control Systems (ICS) by mapping assets and data flows to specific Purdue levels (0-5), defining necessary firewall rules, and establishing DMZs. It ensures strict traffic control between IT and OT domains, adhering to standards like IEC 62443 and facilitating secure network retrofitting.

An interactive tool designed to discover, select, and assemble specialized teams of AI agents. It supports structured agent collections across different domains (e.g., Engineering, Marketing). Users select multiple roles for a specific task, and the skill launches them in parallel, synthesizing their independent findings into a unified report, including consensus and conflict analysis.

A comprehensive guide covering best practices for creating videos using Remotion and React. Learn how to incorporate advanced features such as 3D rendering (Three.js), complex animations, audio synchronization, data charting, subtitles, and sophisticated scene transitions. This skill covers 29 domain-specific rules for professional video pipelines.

A comprehensive guide to structuring Flutter applications using a feature-first, layered architecture. It defines clear separation of concerns across UI, Logic (Domain), and Data layers, ensuring maintainability and testability. This approach guides developers on organizing code by feature modules, making it ideal for building complex, scalable, and long-lived applications while remaining agnostic to specific state management solutions.

This guide provides detailed best practices for writing and maintaining comprehensive End-to-End (E2E) test cases for the 'lark-cli' tool. It covers the entire workflow, from exploring live help documentation and identifying leaf commands, to structuring state-proving tests (e.g., read-after-write assertions) and generating the required `coverage.md` artifact. Use this when adding or significantly updating E2E coverage for any specific functional domain.

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, multi-phase quality engineering audit tool designed to test codebases beyond standard unit tests. It first explores the codebase's domain and architecture to derive behavioral requirements, then executes a rigorous sequence including spec-traced functional tests, three-pass code reviews, and a multi-model specification audit (Council of Three). It is specifically engineered to find the critical 35% of real-world defects that structural code review alone might miss. Works with any programming language.

This skill guides the parallel assignment of specialized agents when debugging multiple, independent failure domains. Instead of using one agent for all issues, isolate each problem (e.g., a specific test file or subsystem) to its own dedicated agent. This allows agents to work concurrently without sharing context or interfering with each other, significantly accelerating the debugging process and maintaining context purity.

This technique guides users on decomposing multi-constraint questions, especially in the domain of football trivia or historical research. Instead of searching all criteria simultaneously, it instructs users to rank constraints by distinctiveness (e.g., specific match results first, then historical milestones). By performing incremental, cascading searches, the system dramatically narrows down the result set, leading to more accurate information retrieval.

A guide for refining search queries when researching Indian or South Asian actors. It emphasizes the necessity of including domain keywords such as "Bollywood," "Hindi film," or specific Indian award names (e.g., "Filmfare Award") to filter out irrelevant Western search results and drastically improve search precision.