Skill UI

This tool conducts comprehensive, sector-specific threat landscape assessments by analyzing designated threat actor patterns (e.g., APT groups, cybercriminals), common attack vectors, and industry-specific vulnerabilities. It is designed to provide actionable intelligence for risk management, security investment prioritization, and executive reporting across critical sectors like finance, healthcare, and energy.

Processes STIX 2.1 threat intelligence bundles delivered via TAXII 2.1 servers. It normalizes various threat intelligence objects (indicators, actors) into platform-native schemas, enabling automated ingestion, validation, and routing to SIEM, TIP, and EDR systems. Ideal for integrating new ISAC feeds or building bi-directional intelligence pipelines.

Develops comprehensive profiles for Advanced Persistent Threat (APT) groups, criminal organizations, and hacktivist collectivities. This tool aggregates TTP documentation, campaign data, and attribution indicators from major intelligence sources (MITRE ATT&CK, Mandiant, CrowdStrike) to help organizations update threat models, prioritize defensive controls, and prepare executive briefings on sector-specific risks.

A comprehensive guide to writing robust and maintainable tests in Rust. It covers various advanced testing patterns, including Unit, Integration, Async, and Property-Based testing. Learn how to apply the TDD (Test-Driven Development) cycle (RED-GREEN-REFACTOR) to enhance code reliability and ensure high code coverage.

This skill provides a comprehensive guide to implementing secure, defense-in-depth remote access architectures for Operational Technology (OT) and Industrial Control Systems (ICS). It covers best practices such as using jump servers, implementing Multi-Factor Authentication (MFA), session recording, and adhering to standards like IEC 62443 and NERC CIP-005 for vendor and operator access.

This skill provides advanced techniques and code examples for monitoring, mapping, and pivoting across adversary-controlled infrastructure. It leverages Passive DNS, Certificate Transparency logs, Shodan/Censys scanning, and network fingerprinting (JARM/JA3S) to discover C2 servers, phishing domains, and exploitable assets over time, which is crucial for advanced security operations and threat intelligence gathering.

This tool implements the Stakeholder-Specific Vulnerability Categorization (SSVC) framework, providing a structured decision-tree methodology for advanced vulnerability prioritization. It moves beyond traditional CVSS scoring by incorporating critical factors such as active exploitation status (CISA KEV), technical impact, automatability, and mission prevalence. It generates actionable remediation priorities (Track, Attend, Act) to guide security teams in managing risk and improving security program maturity.

The /hub:spawn command launches N subagents that work on the same task simultaneously. Each agent operates within an isolated git worktree, ensuring true parallelism and resource separation. Users can optionally specify templates (e.g., `optimizer`, `refactorer`) to guide agents toward specific strategies for improving code quality, performance, or test coverage. Ideal for complex software engineering tasks requiring diverse, competing approaches.

An autonomous factory for generating production-ready, cross-platform AI agent skills. It accepts raw, unstructured human input—such as transcripts, PDFs, screenshots, vague ideas, or code snippets—and reverse-engineers the complete workflow logic. It bridges the gap between human intent and functional agent specifications, allowing users to automate complex processes without writing code.

This skill generates user-friendly, public-facing release notes (changelogs) for App Stores. It analyzes the raw Git history since the last tag, intelligently filtering out internal commits (like refactors, CI updates, or build scripts) and grouping visible changes into concise, benefit-focused bullet points suitable for a "What's New" section.

This skill acts as an anti-over-engineering guardrail, instructing AI coding agents to maintain strict scope control and minimalism. It guides the AI to only modify the files and lines explicitly requested by the user, avoiding unnecessary abstractions, unrequested refactors, or scope creep. Use it when you prefer the simplest, most direct solution and want to prevent the agent from 'grinding' the code base.

This skill provides a systematic pipeline for safely executing large-scale code refactors across multiple files or subsystems. It first performs parallel, dependency-aware analysis to map the entire scope, synthesizing a single, actionable plan. The process then breaks the work into independent 'work packets,' which are executed sequentially or in parallel by specialized worker agents, ensuring strict dependency management and behavior parity throughout the entire process.