Skill UI

A command-line interface (CLI) for accessing and interacting with Model Context Protocol (MCP) servers. It acts as a unified gateway, allowing users to discover, inspect, and execute tools and APIs from external sources like filesystems, databases, APIs, and specialized services. Essential for programmatic workflow automation and system integration.

This is an expert guide for writing, debugging, and validating complex expressions within n8n workflows. It covers core variables such as $json, $node, $now, and $env, detailing how to correctly access nested data from current nodes, previous nodes, and webhook payloads. Learn best practices for handling data structures, managing variable scopes, and avoiding common syntax errors to ensure reliable workflow automation.

A structured planning system that uses markdown files (task_plan.md, progress.md) to manage complex, multi-step projects, research tasks, or workflows requiring extensive steps. It provides robust session recovery and includes file tampering detection, treating all plan contents as immutable data. Ideal for highly structured project management.

A powerful toolset for automating core PostHog product analytics tasks via Rube MCP. It enables developers and analysts to capture user events, list and filter event data, manage feature flags (creation, retrieval, listing), and inspect user profiles. Ideal for integrating advanced product tracking and A/B testing workflows directly into development pipelines.

This skill safely randomizes the order of repetitive JSON objects within an array without corrupting the data or breaking the JSON syntax. It acts as a data engineer, rigorously validating the input schema (checking for property consistency and lack of nesting) before performing the shuffle. It ensures data integrity is maintained while reordering records, making it ideal for testing, simulation, or data preparation workflows.

A comprehensive tool that analyzes existing codebases to generate detailed, definitive architectural blueprints. It automatically detects technology stacks, identifies architectural patterns (e.g., Microservices, Clean Architecture), and documents implementation details, including dependency flow, cross-cutting concerns (security, logging), and data models. This ensures architectural consistency, aids in onboarding new team members, and guides future development efforts.

This skill provides comprehensive commands to manage the entire AI model evaluation lifecycle using Arize. Users can list, retrieve metadata, and export results from experiments. It is ideal for comparing multiple model runs, benchmarking performance against a dataset, and analyzing model accuracy using structured evaluation workflows (CRUD operations).

This skill analyzes a recorded video of a manual, repetitive process. It extracts visual frames and transcribes audio narration to reconstruct the step-by-step workflow, identifying applications used, data flow, and pain points. Based on a detailed understanding of the process, the assistant proposes targeted, working automation scripts using tools already available on the user's machine.

Provides comprehensive patterns and techniques for securing AI agent systems. Learn how to enforce governance policies, including tool allow/block lists, content filtering, rate limiting, and human approval workflows. Essential for building robust, compliant, and trustworthy agents in multi-agent or production environments that interact with external APIs and sensitive data.

This skill provides comprehensive tools to download, export, and deeply inspect existing Arize traces and spans. It is essential for understanding the full execution flow of complex LLM applications, debugging runtime errors, and performing root-cause analysis. Users can retrieve detailed data filtered by Trace ID, Span ID, or Session ID, enabling thorough analysis of conversational or multi-step agent behaviors.

This advanced workflow automates the process of analyzing the operational health of specific Azure resources. It ingests and analyzes raw logs, telemetry data, and performance metrics from diverse Azure services (e.g., VMs, Web Apps, Databases). The system performs comprehensive root cause analysis, classifies issues (Critical, High, Medium), and generates actionable, step-by-step remediation plans, significantly reducing manual troubleshooting time.

This skill analyzes Azure resource groups in depth, identifying all resources, their configurations, and complex interdependencies. It maps relationships—including network connections, data flows, and managed identities—to generate comprehensive, detailed Mermaid architecture diagrams. Use this when you need a clear, structured visualization and full documentation of an Azure environment's topology.