Skill UI

Cartography is a CNCF project designed to perform comprehensive cloud asset inventory across AWS, GCP, and Azure. It queries cloud APIs to aggregate infrastructure resources, map complex relationships (like IAM permissions and network paths), and store them in a Neo4j graph database. This allows security teams to visualize potential attack paths, conduct deep security audits, and identify cross-cloud misconfigurations.

This tool provides comprehensive asset discovery and inventory management for Operational Technology (OT) and Industrial Control Systems (ICS) environments using the Claroty xDome platform. It utilizes passive monitoring and active querying to achieve full visibility into critical assets across Purdue Model levels, including PLCs, RTUs, and HMIs. Ideal for compliance assessments (e.g., IEC 62443) and onboarding brownfield sites.

This skill details how to conduct comprehensive Operational Technology (OT) vulnerability assessments using platforms like Claroty xDome. It covers passive asset discovery, correlating vulnerabilities against CISA/ICS-CERT advisories, calculating risk scores, and prioritizing remediation based on operational impact and compliance standards (e.g., IEC 62443, NERC CIP). The provided workflow uses Python to manage asset inventory and vulnerability matching for highly regulated industrial environments.

Provides comprehensive, enterprise-level monitoring for all Power Automate flows across an entire tenant. Using a cached store, it tracks failure rates, run health trends, asset inventory (makers, apps, connections), and compliance reports without hitting API rate limits. Ideal for governance teams needing an aggregated view of platform health and root-cause analysis.

Automate the complex process of migrating design systems, design tokens, and styles between multiple Figma files, or from external sources into Figma. This tool covers the full pipeline: inventorying assets, extracting raw style data (colors, typography, effects), transforming them into structured design tokens (e.g., JSON format), and finally writing them back into Figma variables using the Variables API. Ideal for maintaining consistency across large design libraries.