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datadog-analysis

Analyze Datadog logs, metrics, and APM data to identify root causes.

654|77|Updated Jan 20, 2026
Search Tags:#investigation#logs#analysis#metrics#datadog#apm
One-click install
npx skills add https://github.com/incidentfox/incidentfox --skill datadog-analysis
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: datadog-analysis
Source: https://github.com/incidentfox/incidentfox/tree/main/sre-agent/.claude/skills/observability-datadog
Command: npx skills add https://github.com/incidentfox/incidentfox --skill datadog-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires httpx, and includes scripts (resource) components.

What problem does it solve?

This skill helps teams quickly understand and triage issues by querying and analyzing Datadog logs, metrics, and APM data. It provides scripted workflows and a reference for Datadog query language.

Core Features & Use Cases

  • Statistics-first diagnostics: run structured statistics to gauge event volume, error rate, top services, and recurring patterns before collecting raw logs.
  • Strategic log sampling: fetch targeted logs to accelerate root-cause analysis without overwhelming data volumes.
  • Query guidance: built-in Datadog Query Language (DQL) references and templates for common filters (service, host, status, time ranges) and aggregation.

Quick Start

Run the statistics-first workflow to establish context:

  • python .claude/skills/observability-datadog/scripts/get_statistics.py --time-range 60 Then fetch targeted logs:
  • python .claude/skills/observability-datadog/scripts/sample_logs.py --strategy errors_only --service my-service Optional: explore with the Datadog client:
  • python -c "from datadog_client import search_logs; print(search_logs('service:my-service', time_range_minutes=60, limit=20))"

Frequently Asked Questions about datadog-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I analyze Datadog logs and metrics to find a root cause during an incident?▼

To analyze Datadog logs and metrics for root causes, run a statistics-first workflow to gauge event volume and error rates, then use strategic log sampling to fetch targeted logs without overwhelming data volumes.

What is the best way to fetch targeted Datadog logs for a specific service?▼

The best way to fetch targeted Datadog logs is using strategic log sampling scripts, allowing you to filter by specific strategies like errors only and by service name to accelerate root cause analysis.

How do I query Datadog APM data and event statistics across multiple services?▼

You can query Datadog APM data and event statistics across multiple services by executing structured Python-based scripts that communicate with the Datadog API to provide real-time or post-incident insights.

Do I need direct Datadog API keys to run log analysis scripts?▼

You do not necessarily need direct Datadog API keys, as the analysis scripts support querying the Datadog API via a proxy credential flow or direct API keys configured through your environment.

Does this approach provide guidance for writing Datadog Query Language filters?▼

Yes, this approach provides built-in Datadog Query Language references and templates for common filters such as service, host, status, and time ranges to help structure your log aggregations.

Can I use Python to automate Datadog metrics and log investigations?▼

Yes, you can use Python to automate Datadog investigations by utilizing the included Python-based client and scripts to search logs, run statistics, and query metrics programmatically.

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