Skill UI

Automates configuring and aggregating infrastructure metrics across compute, storage, network, containers, load balancers, and databases with Prometheus, Datadog, or CloudWatch for dashboards and capacity tracking.

Automates crafting APM dashboards by identifying required metrics, selecting golden signals, and generating configurations for Grafana, Datadog, or other monitoring platforms, so you can quickly visualize application health and performance.

Automates Datadog monitoring workflows via Composio’s Rube MCP, letting you query metrics, search logs, manage monitors and dashboards, and create events or downtimes after connecting to the MCP toolkit and tools list.

Automate deploying production-ready monitoring stacks like Prometheus, Grafana, and Datadog, delivering configuration files, deployment scripts, and guided instructions for infrastructure-focused observability setups.

A comprehensive, AI-assisted methodology for diagnosing complex software errors in production environments. This skill guides users through the entire debugging lifecycle, from initial triage and gathering observability data (Sentry, DataDog, Jaeger) to generating hypotheses, selecting appropriate debugging strategies (Time-Travel, Chaos Engineering), and performing AI-powered root cause analysis. It culminates in proposing validated fixes and prevention measures.

This skill outlines a comprehensive, multi-agent orchestration workflow designed for sophisticated production issue resolution. It systematically combines AI code assistants, observability platforms (like Sentry/DataDog), and automated techniques (e.g., git bisect, distributed tracing) into a rigorous four-phase process: Issue Analysis, Root Cause Investigation, Fix Implementation, and Verification. It guides best practices for reducing Mean Time To Recovery (MTTR) and improving system resilience in complex, multi-system environments.

This skill guides you through advanced integration patterns to connect Sentry with your full observability stack. Learn how to correlate error events from structured loggers (like Winston/Pino) using Event IDs, track business metrics (counters, gauges) within Sentry, and link performance data across multiple tools (Grafana, Datadog). Achieve end-to-end visibility, ensuring every error is tied back to its root cause and business context.

This guide provides a comprehensive setup for Vercel observability. It covers enabling built-in analytics, collecting detailed runtime logs, implementing structured logging, configuring external log drains (e.g., Datadog, Sentry), and integrating OpenTelemetry tracing. Use this when monitoring production deployments and setting up robust error tracking.

samber/oops is a robust library that transforms standard Go error handling by adding crucial structured context. Instead of generic error messages, it allows developers to attach key-value attributes, stack traces, error codes, and user-facing messages directly to the error object. This is essential for modern microservices, allowing APM tools (Datadog, Sentry) and on-call engineers to diagnose failures accurately by understanding the full context of the failure, such as the domain, user ID, and specific query that failed.

A comprehensive toolkit for building robust, structured logging pipelines in Golang. It provides advanced modules for handler composition (slog-multi), throughput control (slog-sampling), attribute transformation (slog-formatter), and integrates with major backend logging sinks (e.g., Sentry, Loki, Datadog, Elasticsearch). Use this when building complex Go applications that require deterministic log routing, sampling, and data sanitization.

This skill provides a comprehensive framework for setting up robust observability for Navan API integrations in production environments. It guides users through implementing structured logging, tracking request latency, classifying various errors (auth, rate limit, server), and monitoring the OAuth token refresh lifecycle. Includes practical steps and YAML examples for configuring alerts and building dashboards in platforms like Datadog, CloudWatch, or Prometheus/Grafana.

This tool empowers users to query, filter, and aggregate logs from Datadog directly from the command line using the Composio CLI. It bypasses the graphical UI, providing structured JSON output for complex analysis, cross-service correlation, and automated reporting. Ideal for deep dive investigations, tracking latency regressions, or building scheduled operational digests.