Skill UI

SimPO (Simple Preference Optimization) is a state-of-the-art, reference-free method designed for aligning Large Language Models (LLMs) using human preference data. It is an efficient alternative to DPO and PPO, notably outperforming DPO without requiring a separate reference model. It is ideal for practitioners who need faster, simpler, and more resource-efficient fine-tuning for preference alignment.

Generates minimal macOS Seatbelt sandbox profiles with allowlist-based resource rules so apps can be isolated by permissions, useful for restricting file, network, and IPC access when defending against high-risk execution or supply chain threats.

Implements zero trust device posture assessment by ingesting endpoint health signals from CrowdStrike ZTA, Microsoft Intune, and Jamf to enforce compliance via conditional access policies before granting access to resources.

This skill generates highly precise deep links to various resources within the Arize platform, including traces, spans, sessions, datasets, and evaluators. It requires collecting specific resource IDs (e.g., project_id, dataset_id) and managing time ranges (startA/endA) to ensure the user is taken to the exact view, optimizing the user experience for ML practitioners and data scientists.

Analyzes and generates a self-contained, explorable HTML report detailing usage metrics for Claude Code sessions. It tracks token consumption (input/output), subagent usage, skill calls, and cache efficiency across specified time ranges. This tool helps developers visualize resource waste and pinpoint areas for workflow optimization, ensuring cost-effective and efficient AI development cycles.