Monitoring AI Agent Skills

A system monitoring tool that exposes system metrics via the Model Context Protocol (MCP). This tool allows LLMs to retrieve real-time system information through an MCP-compatible interface. This tool provides the following monitoring capabilities: - CPU Information: Usage percentage, core count, and detailed CPU info - Memory Information: Virtual and swap memory usage - Disk Information: Disk usa

The implementation of Model Context Protocol (MCP) server for VictoriaMetrics. This provides access to your VictoriaMetrics instance and seamless integration with VictoriaMetrics APIs and documentation. It can give you a comprehensive interface for monitoring, observability, and debugging tasks related to your VictoriaMetrics instances, enable advanced automation and interaction capabilities for e

This skill should be used when users want to train or fine-tune language models using TRL (Transformer Reinforcement Learning) on Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, and model persistence.

ntfy-me-mcp provides AI assistants with the ability to send real-time notifications to your devices through the ntfy service (either public or selfhosted with token support). Get notified when your AI completes tasks, encounters errors, or reaches important milestones - all without constant monitoring. The server includes intelligent features like automatic URL detection for creating view actions

Purpose: System configuration and health monitoring Category: System Version: 1.0.0 The System plugin provides essential commands for configuring, monitoring, and maintaining Claude Code installations. These are system-level operations that configure or monitor Claude Code itself. Purpose: Unified view of work, system, and memory state Provides comprehensive status overview including: - Active wor

11 skills · 31 commands · 1 hook

Build anomaly detection applications with Azure AI Anomaly Detector SDK for Java. Use when implementing univariate/multivariate anomaly detection, time-series analysis, or AI-powered monitoring.

Automate Sentry tasks via Rube MCP (Composio): manage issues/events, configure alerts, track releases, monitor projects and teams. Always search tools first for current schemas.

This is the official MCP (Model Context Protocol) server for LG ThinQ devices. This server provides integrated control capabilities including status monitoring, device control, and profile information for various LG ThinQ devices, built on the LG ThinQ API and Python Open SDK. MCP connection method is stdio. - Prerequisites - Quick Start - Detailed Usage - Tool Reference - Device List Query Retrie

Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarks Use when: agent testing, agent evaluation, benchmark agents, agent reliability, test agent.

Automate Supabase database queries, table management, project administration, storage, edge functions, and SQL execution via Rube MCP (Composio). Always search tools first for current schemas.

You are an SLO (Service Level Objective) expert specializing in implementing reliability standards and error budget-based practices. Design SLO frameworks, define SLIs, and build monitoring that balances reliability with delivery velocity.

A remote Model Context Protocol (MCP) server that connects AI assistants to your crash reporting and real user monitoring data in Raygun through natural language conversations. 📚 Tool Reference • 🚀 Quick Start • 🔑 Get API Token - 🐛 Error Management - Investigate, resolve, and track application errors and crashes with full stack traces and context - 🚀 Deployment Tracking - Monitor releases and

A Model Context Protocol (MCP) server for shipment tracking api, package monitoring, and logistics management using the TrackMage API. Supports tracking across 1600+ carriers worldwide. - Carrier Support: Track packages across 1600+ carriers worldwide (full list) - Resources: Workspaces, shipments, orders, carriers, tracking statuses - Tools: Create shipments/orders, get shipment checkpoints, carr

The server uses the MCP protocol to share access to a local Home Assistant instance with an LLM application. A powerful bridge between your Home Assistant instance and Language Learning Models (LLMs), enabling natural language control and monitoring of your smart home devices through the Model Context Protocol (MCP). This server provides a comprehensive API for managing your entire Home Assistant

An enterprise-grade MCP (Model Context Protocol) server for Jenkins CI/CD integration with advanced features including multi-tier caching, pipeline monitoring, artifact management, and batch operations. - Job Management: Trigger, list, search, and monitor Jenkins jobs with folder support - Build Status: Real-time build status tracking and console log streaming - Pipeline Support: Stage-by-stage pi

AI-powered debugging and inspection for Kubernetes clusters using Inspektor Gadget. - AI-powered interface for Kubernetes troubleshooting and monitoring - One-click Inspektor Gadget deployment and removal - Intelligent output summarization and analysis - Automatic gadget discovery from Artifact Hub 1. Ensure you have Docker and a valid kubeconfig file 2. Configure the MCP server in VS Code (see IN

Implement comprehensive observability for service meshes including distributed tracing, metrics, and visualization. Use when setting up mesh monitoring, debugging latency issues, or implementing SLOs for service communication.

Calculate portfolio risk metrics including VaR, CVaR, Sharpe, Sortino, and drawdown analysis. Use when measuring portfolio risk, implementing risk limits, or building risk monitoring systems.

A Model Context Protocol server implementation for Last9 that enables AI agents to seamlessly bring real-time production context — logs, metrics, and traces — into your local environment to auto-fix code faster. - View demo announcement blog post - Installation - Configuration - Tools Documentation - Development You can connect to Last9 MCP in two ways: This is the easiest and cleanest setup. You

English | 中文 A lightweight JVM monitoring and diagnostic MCP (Multi-Agent Communication Protocol) server implementation based on native JDK tools. Provides AI agents with powerful capabilities to monitor and analyze Java applications without requiring third-party tools like Arthas. - Zero Dependencies: Uses only native JDK tools (jps, jstack, jmap, etc.) - Lightweight: Minimal resource consumption

Automate Datadog tasks via Rube MCP (Composio): query metrics, search logs, manage monitors/dashboards, create events and downtimes. Always search tools first for current schemas.

A Model Context Protocol (MCP) server for detecting, monitoring, and analyzing potential wildfires globally using multiple data sources including NASA FIRMS, OpenWeatherMap, and Google Earth Engine. This MCP server provides AI assistants with tools to detect and analyze wildfire activity anywhere in the world by integrating real-time satellite data, weather forecasts, air pollution metrics, and ge

You are a monitoring and observability expert specializing in implementing comprehensive monitoring solutions. Set up metrics collection, distributed tracing, log aggregation, and create insightful da