Skill UI

This extension provides comprehensive distributed tracing capabilities for the KubeSphere Observability Platform, adhering to the OpenTelemetry standard. It guides users through the entire lifecycle, including component installation (Generator, Collector, Agent), configuration, and querying complex tracing data, spans, and service graphs. It is essential for monitoring microservices and understanding complex system interactions.

A comprehensive pre-production audit tool that scans a codebase for critical issues across eight dimensions: security, database, deployment, code quality, AI/LLM usage, dependencies, frontend, and observability. It intercepts deployment commands, forcing users to resolve critical failures before shipping code. The audit is stack-agnostic and provides detailed pass/fail/manual reports.

This skill simulates a world-class Senior System Design Engineer with 20+ years of experience at top tech companies. It helps users design, review, and scale complex, highly available, and resilient distributed systems. It covers every aspect from database sharding and microservices decomposition to caching strategies, observability stacks, and CI/CD pipelines, providing detailed blueprints and mathematical scaling estimates.

A portable, framework-agnostic system designed for robust field-side observability in React or React Native apps. It establishes a strongly typed event taxonomy, ensures best-effort, non-blocking data fan-out across multiple analytics providers, and enforces consent gates for all telemetry data. This system complements synthetic testing by capturing real user vitals (LCP, INP, CLS) and behavioral events in production.

A systematic skill designed to investigate data incidents, including freshness delays, volume anomalies, schema changes, and ETL failures. It guides the agent through a comprehensive process using Monte Carlo's observability metadata and optional direct data querying to pinpoint the root cause.

This skill integrates with Monte Carlo's observability platform to provide a comprehensive health report for data assets. It checks critical metrics including data freshness, active alerts, monitoring coverage, asset importance, and the health of upstream dependencies. Activates when users inquire about the status, reliability, or quality of a data table.

This skill provides comprehensive monitoring advisory services for data assets and AI agents. It helps users assess data coverage gaps, analyze use-case criticality, and automatically create data quality monitors (table, metric, validation). Furthermore, it enables monitoring of AI agent performance, latency, and observability, ensuring robust data governance across the entire ML lifecycle.

This skill exports cost-tracking telemetry data into formats suitable for external observability platforms like Prometheus, Grafana, and Datadog. It allows cost data, previously confined to the AgentDB namespace, to be visualized and monitored in real-time dashboards via Prometheus textfile collectors or structured JSON webhooks.

This tool guides the creation of actionable monitoring dashboards for observability platforms like Grafana and SigNoz. Instead of merely displaying raw metrics, it focuses on structuring data to answer critical operational questions: Is the system healthy? Where are the bottlenecks? What changes occurred? And what action should be taken? It transforms raw metrics into decision-making tools.