Debug Issue With Datadog

Debug Issue with Datadog

Use this skill whenever the task is investigative rather than

implementational: a user, customer, or oncall has surfaced a problem and you

need to figure out *what is actually happening in production* and *where in the

code it lives*. The deliverable is an analysis, not a patch — though the

analysis should make the right patch obvious.

When to Apply

• A Linear issue (typically with an LFE-XXXX ID) describes a production

failure, error spike, or customer report.

• A GitHub issue or pasted incident/error report needs triage.
• A monitor alerted and you need to understand *why* before deciding what to

fix.

• Existing tickets under the "Make monitoring useful again" project (parent

LFE-8837) and similar — these expect the structured analysis output below.

If the task is "implement this fix" rather than "figure out what's broken",

this is the wrong skill — go to backend-dev-guidelines or the relevant

package guide.

Workflow

Read the inputs first, then plan the Datadog sweep, then read the code, then

write the analysis. Do not skip ahead to suggested patches before the data

supports them.

• Intake. Pull every signal already available in the report. See

references/intake.md. For a Linear URL/ID, fetch

the issue *and* its comments via the Linear MCP — the description is often

updated inline as triage proceeds. For a GitHub issue, use gh issue view.

For pasted text, treat it as the description.

If intake contains an alert identity (a Datadog monitor ID or title, an

incident.io alert/INC reference, or an on-call page), first apply

incident-alert-tickets — a

documented cause section may resolve the investigation before any sweep.

• Scope the sweep. From the intake, pick the affected subsystem and time

window. Use references/repo-debug-map.md

to translate "PostHog integration", "ingestion failures", "evals stuck",

etc. into the Datadog filters and source files you should be looking at.

• Run the broad Datadog sweep. Default to the full sweep in

references/datadog-playbook.md: APM

spans, error logs, metrics, and monitors — split across prod-eu

and prod-us (and prod-hipaa / prod-jp when relevant). Always check

regional disparity first; it usually rules whole hypotheses in or out.

Use datadog-query-recipes for

reusable tenant, public API, queue consumer, and cross-environment query

shapes.

• Cluster the errors. Group by (projectId, error.message) or

(error.type, error.message). Treat each distinct cluster as its own

hypothesis — Langfuse incidents commonly have *multiple* coexisting root

causes, not one.

• Map clusters to code. For each cluster, open the relevant handler file

from the repo-debug map and read enough of it to confirm or refute the

hypothesis. Cite specific files and line ranges in the output.

• Write the analysis using

references/output-template.md.

• Deliver. Default: print the analysis in chat. If the user asked for it,

also save under the workflow they specified (file, Linear comment via, etc.).

If the investigation was anchored to an alert identity, also offer the

human-gated write-back from

incident-alert-tickets: append the

established root cause as a dated cause section, or create the monitor's

ticket.

Datadog MCP Usage Notes

Two Datadog MCP servers are typically available — one bound to the EU site

(datadoghq.eu) and one to the US site (datadoghq.com). Always run

region-relevant queries against both unless intake clearly localizes the

incident. The prod-eu / prod-us env tags live on each side respectively.

• Span search filter pattern:

service:worker resource_name:"process posthog-integration-project" status:error

• Log search filter pattern:

service:worker env:prod-eu @langfuse.project.id:cm1r6u… status:error

• For high-volume queries, prefer aggregate_spans / aggregate_events

grouped by (error.message, projectId) over fetching individual traces.

• Always link to the Datadog UI for the queries you ran (final section of the

output template).

See references/datadog-playbook.md for the

full set of starter queries and parameter shapes.

Output Expectations

From the output template:

• Header: data source, time window, region split (EU vs US table).
• Hotspots: per-projectId (or per-cluster) error counts.
• Root cause by error class: each cluster gets a short hypothesis with

reasoning, distinguishing primary causes from symptoms.

• Suggested patches: P0/P1/P2 grouped, with concrete file paths and short code

sketches. Reference the actual handler in worker/src/features/** or

web/src/**.

• Dashboards: paste the Datadog query URLs at the end.

Findings come first, recommendations last. If the data is thin, say so

explicitly and propose what would need to be true to confirm each hypothesis —

do not invent root causes.

Cross-References

• Per-monitor knowledge base — look up documented causes before the sweep,

record new ones after (human-gated):

incident-alert-tickets

• Production telemetry query recipes, tenant/public API usage, and queue

consumer measurements:

datadog-query-recipes

• Backend layout, queue contracts, instrumentation patterns:

backend-dev-guidelines

• ClickHouse-related findings (memory ceilings, JOIN spills, slow queries):

clickhouse-best-practices

• Once a fix is identified and you switch to implementation, hand off to the

package AGENTS.md for the affected directory.