Datadog Query Recipes Agent Skill

| Research Langfuse production telemetry with reusable Datadog queries. Use for tenant or project activity, API usage, queue behavior, spans, logs, metrics, or ad hoc measurements across production regions; pair with debug-issue-with-datadog for root-cause analysis.

5k tokens

context cost

the whole folder, loaded on every use

files

instructions only

copies elsewhere

how many repositories repackaged it

32482

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/langfuse/langfuse --skill datadog-query-recipes

What comes with it

16 895 bytes besides the instruction

agents/openai.yaml

references/environments.md

references/public-api-tenant-usage.md

references/queue-consumers.md

The instruction itself

5 sections, as written by the author

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

Identify the entity and signal: tenant ID, org ID, project ID, route, queue,

service, error class, or metric.

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

Start with aggregate queries, grouped by environment, service, route,

queue, project, org, status, or error facets as appropriate.

Fetch raw spans, logs, or traces only after aggregation identifies the

cluster or sample you need.

For tenant-specific HTTP usage, prefer trace correlation over single-span

queries when tenant tags and route tags live on different spans.

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 weekly-production-review when

the user asks for a weekly engineering overview of production bugs, pages,

and incidents.

Use incident-alert-tickets when the

research is anchored to a named production alert or monitor: look up

documented causes before measuring, and record new ones only after human

approval.

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.

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How to use it

Copy the folder

Take langfuse/datadog-query-recipes from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.