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Research Langfuse production telemetry with reusable Datadog queries.
6 of 24 test cases passed with the skill.
Tokens, turns and time describe resource use, not a dollar price.
Lift withheld: 7 comparable cases; this report requires 8.
A test case the skill fixed. Same model, same prompt, with and without the skill.
During a telemetry search for prod-jp database query errors over the past hour, no error logs were returned after verification queries. How should this result be represented in the final summary?
| Checked | Without skill | With skill |
|---|---|---|
| Includes an explicit No measurements found entry for prod-jp. | Missed | Passed |
The instructions your model reads when the skill loads.
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.
Unless the user explicitly narrows the scope, cover every production environment:
prod-usprod-euprod-hipaaprod-jpQuery 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.
references/environments.mdreferences/public-api-tenant-usage.mdreferences/queue-consumers.mdreferences/export-freshness-lag.mdtest cases passed with the skill
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