Skill UI

Install and configure the Manifest observability plugin, setting up telemetry, configuring API keys, and troubleshooting integration.

This skill investigates data incidents like freshness delays, volume anomalies, and ETL failures using Monte Carlo MCP tools. It combines observability metadata with optional direct data querying to identify root causes systematically.

This skill evaluates the health of data assets using Monte Carlo's observability platform. It generates structured reports on freshness, alerts, monitoring coverage, importance, and upstream dependencies. Use it when users inquire about asset status, reliability, or quality.

Routes ambiguous data-observability requests to the appropriate Monte Carlo skill or workflow. Activates reactively for unclear alerts, incidents, coverage gaps, or general data quality issues, asking clarifying questions first to avoid premature tool calls.

Analyzes data coverage gaps and creates monitors for warehouse tables and AI agents. Covers use-case analysis, data monitor creation, monitors-as-code YAML, and agent observability.

Diagnoses data pipeline performance issues such as slow jobs, expensive queries, and latency trends using Monte Carlo's cross-platform observability. Works across Airflow, dbt, Databricks, and warehouse query engines to find bottlenecks, detect regressions, and identify root causes.

Surfaces Monte Carlo data observability insights—health, lineage, alerts, and monitors-as-code—before developers edit dbt models or SQL pipelines, ensuring risk-aware code changes.

Configure New Relic for full-stack observability: install infrastructure agents on Linux, Docker and Kubernetes, integrate APM agents for Node.js, Python, Java and Go, add custom events, metrics and spans, and write NRQL queries for dashboards and alerts.

This skill enables building production-ready monitoring, logging, and tracing systems for enterprise applications. It covers implementing observability strategies, managing SLIs and SLOs, and handling incident response workflows. Expertise includes distributed tracing, log management, alerting, and reliability engineering practices using tools like Prometheus, Grafana, and OpenTelemetry to ensure system stability and performance.

Set up comprehensive monitoring and observability: metrics, distributed tracing, log aggregation, dashboards, alerts, and SLOs for application and infrastructure health.

This skill helps you design comprehensive SLO frameworks and establish meaningful SLIs for service reliability. It focuses on creating monitoring systems that balance reliability with feature velocity using error budget-based engineering practices. Ideal for defining reliability targets, building dashboards, and aligning technical metrics with business priorities.

Implement vendor-neutral observability using OpenTelemetry standards for traces, metrics, and logs. Configure collectors, set up Kubernetes deployments, and instrument Python services. Supports Prometheus, Jaeger, and Loki backends. Essential for unified monitoring in microservices and infrastructure environments.