Skill UI

This skill enables the creation and configuration of specialized Cascade agents tailored for specific domains or tasks. Users can define system prompts, integrate domain knowledge, and set precise activation triggers. It facilitates building highly customized, domain-specific AI agents and automated workflows within the Windsurf ecosystem.

Provides OpenClaw agents with local persistent memory via MemOS, managing installation, configuration, upgrades, and a memory viewer for secure offline workflows.

This skill provides comprehensive CLI commands for managing human feedback labeling in Arize. Users can create, list, and delete annotation configurations (schemas for labels like categorical, continuous, or freeform) and set up annotation queues. This is essential for defining human review workflows and applying labels to project spans, dataset examples, and experiment outputs.

This skill generates accurate deep links to various resources within the Arize UI, including traces, spans, sessions, datasets, evaluators, and annotation configs. It is essential for creating shareable, clickable URLs when a user needs to view specific data points or configurations, especially when IDs are gathered from exported logs or context.

A comprehensive security auditing framework designed to systematically detect sensitive information disclosure from Large Language Models (LLMs). It uses multi-phase, escalating dialogue probes to test for leaks such as system prompts, API keys, PII, and internal configurations. Essential for red teaming and rigorous model security assessment.

A powerful tool for executing complex, multi-step, and multi-agent workflows defined in YAML format directly within the AI session. It acts as an orchestration engine, simulating various roles (agents) to complete tasks sequentially or in parallel, without requiring external API keys or configuration. Ideal for complex business process automation and multi-role content generation.

This tool reads your local GitHub Copilot CLI conversation history using the specialized `vardoger` CLI. It analyzes your behavioral patterns, coding conventions, and usage style to generate a personalized instructions block. This block is written directly to your local Copilot configuration file, allowing the assistant to adapt its guidance and suggestions specifically to your unique habits and preferences. The process runs entirely offline, ensuring maximum privacy.