Datadog Query Recipes

Use this skill for Langfuse production telemetry research where the main work is finding the right Datadog data path. Keep findings evidence-based and include the exact Datadog links or query shapes that support the answer.

Required Scope

Unless the user explicitly narrows the scope, cover every production environment:

• prod-us
• prod-eu
• prod-hipaa
• prod-jp

Query both Datadog sites when needed. Default to the EU site for prod-eu and the US site for the other prod environments, but verify with a small count or facet query before concluding an environment has no data.

Before querying live Datadog, load the relevant Datadog MCP guidance for the data domain you need: traces, logs, metrics, and visualizations.

Workflow

1. Identify the entity and signal: tenant ID, org ID, project ID, route, queue, service, error class, or metric.
2. Read only the relevant reference:
   • Prod environment/site routing: references/environments.md
   • Public API tenant or legacy endpoint usage: references/public-api-tenant-usage.md
   • Queue inventory, queue consumers, and queue metrics: references/queue-consumers.md
3. Start with aggregate queries, grouped by environment, service, route, queue, project, org, status, or error facets as appropriate.
4. Fetch raw spans, logs, or traces only after aggregation identifies the cluster or sample you need.
5. For tenant-specific HTTP usage, prefer trace correlation over single-span queries when tenant tags and route tags live on different spans.
6. Report the windows, environments, sites, query links, and any sampling or missing-data caveats.

When To Use Other Skills

• Use debug-issue-with-datadog when a Linear issue, GitHub issue, incident report, or monitor needs root-cause analysis and patch recommendations.
• Use detect-prod-regressions when the user asks for a proactive production sweep or baseline comparison.
• Use linear-bug-triage only after a human approves sharing measured findings in Linear.

Output Expectations

Summarize what was checked, including:

• Datadog site and env values covered.
• Time windows.
• Core filters or metrics used.
• Count, rate, latency, queue depth, trace sample, or "No measurements found".
• Datadog links or trace IDs that let the human rerun the query.