Skill UI

An AI assistant for generating and analyzing Chinese Bazi (Four Pillars) and Ziwei Dou Shu charts using precise algorithms, supporting individual analysis, cross-validation, and structured report generation.

kube-bench is an open-source tool that validates the compliance and security posture of Kubernetes clusters. It rigorously checks control-plane components, worker nodes, and cluster policies against the comprehensive CIS Kubernetes Benchmark. It reports findings (PASS/FAIL/WARN) and includes specific remediation guidance, making it essential for compliance auditing and hardening efforts.

This comprehensive tool manages all aspects of a brand's identity, including voice, visual standards, messaging frameworks, and asset management. It helps teams maintain brand integrity by synchronizing guidelines (e.g., markdown) into actionable digital tokens (JSON/CSS) and validating assets, ensuring all content and designs adhere to established rules and standards.

This guide details the architecture and deployment of resilient Command and Control (C2) redirectors. By using tools like Nginx and Apache, operators establish an intermediary layer that filters beacon traffic based on malleable C2 profiles. This setup protects the true team server from detection, takedown, and analysis (OPSEC), enabling advanced techniques like domain fronting and traffic shaping for low attribution.

Plaso (log2timeline) is an open-source engine used to generate forensic super-timelines. This skill guides the process of normalizing hundreds of diverse artifact types—including MACB times, registry keys, EVTX logs, and browser history—into a single, cohesive chronological view. The workflow covers extraction, filtering, and importing the timeline into Timesketch for collaborative analysis, which is crucial for incident response, correlating complex activity chains, and detecting anti-forensic techniques like timestomping.

This skill details how to forcefully coerce a target machine account (e.g., a Domain Controller) to authenticate to an attacker-controlled relay. It combines tools like Coercer and PetitPotam, leveraging various RPC protocols (MS-EFSR, MS-RPRN) to achieve NTLM relay. This technique ultimately enables domain compromise by forcing authentication into services like AD CS Web Enrollment (ESC8).

Automate security regression testing for Large Language Model (LLM) applications by integrating Promptfoo and DeepTeam into CI/CD pipelines. This skill enforces a critical security gate, automatically testing endpoints against OWASP LLM Top 10, OWASP Agentic threats, and common jailbreaks/prompt injections. It ensures that security patches do not regress over time due to prompt or model changes, providing continuous risk monitoring.

This skill outlines the implementation of advanced runtime defenses for production Large Language Model (LLM) applications. It covers using multiple industry-leading guardrail systems—including Llama Guard, NeMo Guardrails, and LLM Guard—to detect and block adversarial attacks such as prompt injection, jailbreaks, and toxic content. It provides a comprehensive defense-in-depth strategy for ensuring model safety and output integrity in real-world deployments.

This skill guides the deployment and strategic use of honeytokens and canarytokens (decoy credentials, files, URLs, etc.). These high-fidelity decoys are designed to detect unauthorized access, insider threats, or adversary reconnaissance by triggering alerts upon any interaction, providing early warning deep within the kill chain.

Falco is a powerful CNCF project for real-time runtime security monitoring. This guide teaches authors how to write and deploy custom detection rules using Falco's robust YAML rule engine and modern eBPF driver. It is designed to detect critical threats such as container escapes, namespace manipulation, privileged mounts, and anomalous syscalls in Kubernetes and Docker environments, significantly hardening the overall container security posture.

This skill provides comprehensive tools to detect data poisoning and model backdooring attacks throughout the machine learning lifecycle. It checks data provenance, detects label-flip anomalies (using Cleanlab), and analyzes model activations and spectra (using ART) to identify malicious triggers or compromised weights, ensuring ML supply chain integrity.

A comprehensive tool and methodology for identifying and preventing dependency confusion attacks across major package ecosystems (npm, PyPI, Maven). It detects instances where internal, private package names are leaked and subsequently published publicly by attackers. The skill provides both detection (enumerating claimable internal names) and prevention strategies (enforcing registry pinning and scope restrictions) to secure the software supply chain.