Skill UI

An internal utility designed for deep analysis of Python tracebacks. It performs Python-specific root-cause checks, provides guidance for minimal reproducible examples using pytest, and identifies precise targets for implementing defensive coding practices (like guard clauses or schema validation). Ideal for advanced debugging scenarios.

This skill enables the creation of a robust 'harness' system for coding agents within a repository. It addresses the need to prevent repeated AI-generated coding mistakes by implementing durable instructions, guardrails, regression tests, and failure memory. It treats the target repository as the source of truth, ensuring consistency and maintaining high reliability across all automated contributions.

This skill guides the design of statistically rigorous A/B tests for optimizing product features, UI/UX changes, onboarding flows, and pricing models. It generates a complete test plan, including formulating hypotheses, calculating required sample sizes, estimating test duration, defining primary and guardrail metrics, and providing a detailed guide on interpreting results (ship, iterate, or reject).

This skill assists users in defining, adapting, and designing bounded, structured feedback loops for AI agents. Whether you need to find a suitable existing workflow, fine-tune a published loop, or design a new process from scratch, this tool guides you through a safe, iterative methodology. It emphasizes setting clear stopping rules, guardrails, and verifiable, measurable steps to ensure the automation remains controllable and focused.

This solution provides a comprehensive framework for detecting compromised cloud credentials across AWS, Azure, and GCP. It analyzes anomalous API activity, impossible travel patterns, and indicators of credential stuffing by leveraging services like AWS GuardDuty, Azure Defender, and GCP SCC. Use this when investigating suspicious cloud API calls, responding to exposed credential alerts, or scoping a potential account takeover.

This guide teaches security teams how to proactively detect and respond to unauthorized cryptocurrency mining operations across major cloud platforms (AWS, Azure). It utilizes a multi-layered approach by analyzing compute cost anomalies, monitoring GuardDuty findings, inspecting network traffic to known mining pools, and analyzing runtime process execution in EC2, ECS, and EKS environments.

This skill provides a comprehensive solution for monitoring brand impersonation attacks across multiple digital vectors. It systematically scans domains, social media platforms, mobile app stores, and the dark web to detect phishing campaigns, typosquatting, and unauthorized brand usage. It is essential for security teams conducting proactive threat intelligence gathering and vulnerability assessments to safeguard brand integrity.

A comprehensive guide to achieving compliance with the HIPAA Security Rule (45 CFR Part 164). This skill outlines the mandatory steps to protect electronic Protected Health Information (ePHI), including conducting thorough risk analyses, deploying administrative, physical, and technical safeguards, managing Business Associate Agreements (BAAs), and preparing for breach notifications and OCR audits. Essential for Covered Entities and Business Associates.

Implements comprehensive runtime safety defenses for Large Language Model (LLM), RAG, and agent applications. This skill integrates industry-leading guardrail systems—including Llama Guard 3, NeMo Guardrails, and LLM Guard—to inspect and constrain all input and output data. It is essential for production environments requiring protection against adversarial attacks such as jailbreaks, prompt injection (OWASP LLM01), toxic content, and sensitive data leakage.

A comprehensive security skill designed to detect and mitigate indirect prompt injection attacks. It scans untrusted external content—including web pages, PDFs, and images—for hidden, obfuscated instructions before an LLM agent processes them. The pipeline combines content extraction (HTML, OCR), normalization, and dedicated ML models (e.g., LLM Guard, Prompt Guard 2) to ensure data safety.

A comprehensive skill for triaging npm packages and dependency lockfiles against supply-chain threats. It utilizes heuristic scanning (GuardDog), manual tarball inspection, and dynamic detonation in isolated environments to detect malicious behavior such as credential exfiltration, reverse shell execution, and self-propagation. Essential for secure code review and CI/CD pipeline gating.

NVIDIA garak is an open-source LLM vulnerability scanner designed for comprehensive red-teaming and security assessment. It systematically probes large language models (via OpenAI-compatible APIs, Hugging Face, etc.) using thousands of adversarial prompts. It covers critical vulnerabilities such as prompt injection, jailbreaking, data leakage, and toxicity. Use this tool to establish a repeatable security baseline before or after deployment, validate the effectiveness of safety guardrails, or generate quantitative evidence for AI risk assessments.