How do I authenticate the Datadog CLI skill?

You must export the DD_API_KEY and DD_APP_KEY environment variables in your terminal session before Claude can interact with Datadog.

Is the output human-readable?

Yes, by including the --pretty flag, the skill returns human-friendly, colored output that is easy for both users and Claude to interpret.

What types of Datadog data can this skill access?

The skill provides access to logs (search, tail, context), metrics, distributed traces, error summaries, service lists, and dashboard configurations.

Can I use this with Datadog accounts outside the US?

Yes, you can use the --site flag (e.g., --site datadoghq.eu) to specify your specific Datadog region for all commands.

Does this skill support log aggregation?

Yes, use the 'logs agg' command to aggregate logs by specific facets, helping you identify the scale of issues across different dimensions.

Datadog Observability CLI

Name: Datadog Observability CLI
Author: leonardocouy

byleonardocouy

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Analíticas y Monitorización

Enables Claude to investigate production issues by searching logs, querying metrics, and analyzing distributed traces via the Datadog platform.

Acerca de

This skill integrates Datadog's comprehensive observability suite directly into the Claude Code environment, allowing for rapid incident triage and production debugging. It enables AI agents to execute complex log searches, monitor real-time metrics, correlate distributed traces, and manage dashboards without leaving the terminal. By providing a structured interface for error summarization and log pattern analysis, it significantly reduces the mean time to resolution (MTTR) for system outages and performance bottlenecks, making it an essential tool for SREs and DevOps engineers.

Características Principales

Full dashboard management including CRUD operations and dashboard lists.
Advanced log searching, tailing, and pattern grouping for rapid debugging.
Comparative log analysis to detect anomalies between different time periods.
1 GitHub stars
Distributed trace correlation to map full request lifecycles across services.
Time-series metrics querying to monitor system performance and health.

Casos de Uso

Rapidly triaging production incidents by identifying error patterns and service-wide spikes.
Investigating latency issues by correlating logs with specific distributed trace IDs.
Automating the generation and management of Datadog dashboards for new infrastructure.

Acerca de

Características Principales

Full dashboard management including CRUD operations and dashboard lists.
Advanced log searching, tailing, and pattern grouping for rapid debugging.
Comparative log analysis to detect anomalies between different time periods.
1 GitHub stars
Distributed trace correlation to map full request lifecycles across services.
Time-series metrics querying to monitor system performance and health.

Casos de Uso

Rapidly triaging production incidents by identifying error patterns and service-wide spikes.
Investigating latency issues by correlating logs with specific distributed trace IDs.
Automating the generation and management of Datadog dashboards for new infrastructure.