Triage CI Failure

Triage CI failure

Goal

Answer one question: is this failure caused by this PR's own changes ? with hard evidence. Every verdict below must cite the evidence that produced it — a bare "looks flaky, retry" or "looks broken, fix it" is not an acceptable output.

This skill only diagnoses. Never take action (writing a fix, retrying a job) on your own: only present your investigation results to the user.

Owning team: @DataDog/agent-devx

Step 0 — Preflight

Both ddgl and pup are required, and both live in the same places: locally, or inside a dda env dev.

which ddgl pup

If pup is present but not authenticated, either run pup auth login or use the dd-auth skill. If pup can't be made to work, say so and continue with Steps 1 and 4 only: Steps 2 and 3 are unavailable, and the verdict should state that limitation rather than silently producing a weaker one.

Step 1 — Collect the failures

ddgl jobs list --failed --json --no-pager [--ref <ref> | --pipeline <id>]

An empty [] means there's nothing to triage — stop here.

Also fetch pipeline state:

ddgl pipelines get --json [--ref <ref> | --pipeline <id>]

If the pipeline is still running, a job you're about to triage may yet be auto-retried into success. Note that in the verdict rather than treating the failure as final.

For each failed job, look at its failure_reason, i.e. the failure reason as determined by gitlab. Treat it as an aditionnal data point, not the be-all-end-all. For example, a runner_system_failure can be caused by a change of this PR (e.g. a malformed image:). See @references/signals.md for more details.

Step 2 — CI Visibility baseline

Check if this job is failing everywhere (i.e. on main), or if it is often flaky using CI Visibility.

For each failed job, ask how it behaves elsewhere:

pup cicd events aggregate \
  --query='ci_level:job @ci.pipeline.name:DataDog/datadog-agent @git.branch:main @ci.job.name:"<exact job name>"' \
  --compute=count --group-by='@ci.status' --from='2d'

Check @references/signals.md for additional queries that can help if this first one is inconclusive. Come out of this step with a working hypothesis (upstream, flake, or pr-code) for later steps to confirm or overturn — not a final verdict.

Step 3 — Incident correlation

If Step 2 pointed clearly at pr-code, skip to Step 4.

Use the helper script to search for an active CI incident matching the failing job:

.agents/skills/triage-ci-failure/scripts/incidents.py search \
  --at <job-failure-ISO8601-timestamp> \
  --job '<exact failing job name>' [--job '<another one>' ...]

Read the match tier in the output (exact, base, prefix, token, none) — anything but none is worth reading the timeline for:

.agents/skills/triage-ci-failure/scripts/incidents.py timeline <IR-nnnnn>

This is where you find out how far along the fix is — not just whether one exists.

• stable usually means a rollback or workaround has already landed and the affected job(s) should pass again on a rebase.
• resolved (or completed) is the stronger signal: the incident is fully closed out.

Look for a rollback, a merged fix PR, or an explicit state transition to tell which.

If nothing matches, widen deliberately rather than re-running the same call — escalate through the tier ladder in references/signals.md:

1. (default, above) services:datadog-agent-ci, default window.
2. Same, a much wider window — for old branches whose failure was fixed on main long before you rebased onto them.
3. Drop the service filter, search free text instead, using a keyword pulled from the job log in Step 4 (an image reference, a host, an endpoint, a bucket name).

Step 4 — Read the log

Always do a quick sanity check here, even when Step 3 was conclusive — a time-and-name correlation is strong evidence but not proof. Skim the job's diff against main and the last ~50 lines of its log, and confirm the failure signature actually looks like what the incident describes.

You can obtain the job's log via ddgl:

ddgl logs --job <ID> [--output <some_file>]

If it lines up, you're done — the full cookbook below is skippable. If it doesn't, or Step 3 didn't produce a confident match at all, work through @references/evidence.md's cookbook.

You're looking for two things:

1. the command that actually failed and its exit status
2. whether the failure happened in the job's own work or in its setup/teardown.