Can I use natural language to ask for Datadog metrics?

Yes, you can ask questions like 'show me the latency of service-x' or 'check the CPU usage' and the skill will identify the intent and fetch the relevant data.

How does the Datadog Auto-Detector trigger?

The skill activates automatically when it detects Datadog URLs, observability-related keywords like 'error rate', or specific service names within your conversation.

Which Datadog resources are supported?

It supports a wide range of resources including Logs, APM Traces, Metrics, Dashboards, Monitors, Incidents, RUM, and Infrastructure/Host data.

Does it fetch data every time I mention Datadog?

No, it includes intelligence to skip fetching if the information was recently retrieved, if you're speaking in past tense, or if the reference is purely informational.

How does this skill handle large amounts of log data?

It uses a specialized 'datadog-analyzer' subagent to process thousands of data points and return a condensed summary of roughly 800-1200 tokens, preserving your main conversation's context window.

Datadog Auto-Detector

Name: Datadog Auto-Detector
Author: schovi

byschovi

Analíticas y Monitorización

Automatically detects and analyzes Datadog resource mentions to provide real-time observability context within your workflow.

Acerca de

The Datadog Auto-Detector skill empowers Claude to act as an observability partner by automatically recognizing Datadog URLs, service metrics, and natural language log queries. It uses an intelligent three-tier architecture to identify when a user needs live data, then spawns a specialized subagent to fetch and summarize complex Datadog resources. By condensing high-volume telemetry into actionable summaries, it enables seamless incident investigation, performance comparison, and health monitoring without manual context switching or token exhaustion.

Características Principales

Subagent-based data summarization for efficient context management
Natural language query detection for service health and error rates
Context-aware fetching logic to prevent redundant API calls and token waste
0 GitHub stars
Intent classification to tailor data retrieval for investigations or status checks
Automatic URL recognition for logs, APM, metrics, dashboards, and monitors

Casos de Uso

Troubleshooting production incidents by providing a Datadog log or trace URL for instant analysis
Comparing error rates and latency between microservices using simple natural language commands
Proactively monitoring service health during code reviews or deployment debugging sessions

Acerca de

Características Principales

Subagent-based data summarization for efficient context management
Natural language query detection for service health and error rates
Context-aware fetching logic to prevent redundant API calls and token waste
0 GitHub stars
Intent classification to tailor data retrieval for investigations or status checks
Automatic URL recognition for logs, APM, metrics, dashboards, and monitors

Casos de Uso

Troubleshooting production incidents by providing a Datadog log or trace URL for instant analysis
Comparing error rates and latency between microservices using simple natural language commands
Proactively monitoring service health during code reviews or deployment debugging sessions