Skill UI

This framework detects NTLM relay attacks across Active Directory environments by correlating Windows Security Event 4624 (LogonType 3). It identifies key indicators such as IP-to-hostname mismatches, Responder/LLMNR poisoning artifacts, and non-enforced SMB/LDAP signing, providing comprehensive threat detection for T1557.001.

This skill is designed for performing local, on-premise evaluations and benchmarking of models hosted on the Hugging Face Hub. It facilitates running specialized evaluation frameworks like `inspect-ai` and `lighteval` against local hardware. Users can select optimal inference backends, including `vllm` for high throughput, or fall back to Hugging Face Transformers or `accelerate`, ensuring comprehensive smoke testing and task selection for LLMs. It focuses purely on the local execution process, separate from remote jobs or publishing workflows.

This framework implements robust input and output validation guardrails for LLM-powered applications. It prevents prompt injection, detects and redacts PII, filters toxic content, and validates model outputs against defined schemas. Use it when deploying AI agents that handle sensitive data or require strict adherence to corporate policies, ensuring a secure and trustworthy AI interaction layer.

This tool assesses whether coding agents adhere to defined skills, rules, or agent definitions. It automatically generates scenarios with varying prompt strictness, executes the agent, and generates a comprehensive report detailing the compliance rate of tool calls against predefined workflow specifications using advanced LLM classification.

Assesses AI/ML systems for prompt injection, jailbreak, model inversion, data poisoning, and agent tool abuse using MITRE ATLAS mapping, signature detection, and scoring metrics before deployment.

Provides an expert methodology to significantly reduce AI API spending (40-80%) without sacrificing user quality. The guide covers cost auditing, optimizing existing systems, and designing cost-efficient architectures using techniques like model routing, prompt caching, output length control, and prompt compression.

This skill provides comprehensive security and monitoring for Large Language Model (LLM) powered trading agents. It helps to audit trade decisions, ensure compliance with risk parameters, and mitigate potential risks associated with automated financial operations.

Manage BrowserWing’s intelligent browser automation platform: install Chrome, configure LLM providers, generate AI-explored scripts, and operate the script lifecycle through HTTP APIs for autonomous exploration, execution, and troubleshooting.

Enforces full, unabridged code and document generation by overriding LLM truncation safeguards, forbidding placeholder patterns, and managing token-limit splits so every requested deliverable is returned complete.

This skill provides comprehensive management capabilities for AI Integrations within the Arize platform. Users can create, list, retrieve, and update credentials for various Large Language Model (LLM) providers, including OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, and Vertex AI. These integrations are crucial for enabling Arize's advanced monitoring and evaluation features that require invoking external LLMs.

This skill guides users on setting up and running LLM-as-judge evaluations using Arize. It covers defining evaluators (including prompts, classification choices, and models) and executing tasks against real data spans or experiments. It supports key metrics like hallucination, faithfulness, and correctness, and facilitates continuous monitoring for comprehensive LLM performance analysis.

This skill provides a comprehensive framework for optimizing, improving, and debugging Large Language Model (LLM) prompts. It leverages production trace data, system evaluations, and human annotations to extract prompts from spans. Users can run a data-driven optimization loop, ensuring the AI responses meet desired quality standards. Ideal for prompt tuning, system prompt refinement, and improving overall AI output quality.