Follow PR
SkillMonitoring & opsLets your agent watch a GitLab CI pipeline for a pull request and report success or investigate failures.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Follow PR skill
About this capability
Monitor the current PR's GitLab pipeline to completion, then report success or investigate a failure. Use when the user asks to follow, babysit, watch, or wait on a PR/pipeline, or just after pushing to / creating a PR.
What this skill tells your AI
The instructions your AI receives, as published by datadog/datadog-agent in .agents/skills/follow-pr/SKILL.md and read by ahel’s review.
Watch the latest Gitlab CI pipeline for the current PR to a terminal state and report the outcome.
Step 0: Ensure correct environment
The appropriate tool for this usecase is ddgl, and more specifically ddgl attach.
Check if ddgl is available - which ddgl. If so, move to Step 1. Otherwise, use a dev env as specified below.
Ensuring a dev env
First, check if you are running in a dev env: test -f /.started will exit 0 if so. If you are in an outdated devenv without ddgl, stop and notify the user to recreate his dev env.
Otherwise, check for the existence of a dev env by using dda env dev show.
If there are existing dev envs:
- Check if the current repo is properly mounted into that env (
reposandextra_(mount|volume)_specsfields) - Check the current state of that dev env.
If the environment is already started and contains the right repo, move to the next step.
Otherwise, create one by using ./create_devenv.sh, then use the environment ID printed by the script in subsequent commands.
Using a dev env
To run commands inside a dev env, use the following template:
dda env dev run --id <dev-env-id> -- [command]
Watch out for space-splitting. For example:
dda env dev run --id follow-pr-attach-7C2C42F6 -- ddgl attach --detail=normal --follow --plain
Step 1: Determine the target
If the user gave a ref, branch, or pipeline ID, pass it through (--ref <ref> or --pipeline <id>).
Otherwise omit both — ddgl attach resolves the pipeline for the current branch on its own.
Step 2: Start monitoring
All pipeline discovery, polling, follow/rebind, and timeout handling is covered by the internals of ddgl attach.
Do not implement a second polling loop or persist monitoring state of your own.
Check whether you have a long-lived monitoring tool available, one that can run a command in the background and forward each stdout line as it arrives, without a timeout of its own (e.g. Claude Code's Monitor tool).
With such a tool: start it on
ddgl attach --plain --follow --detail=full [--ref <ref> | --pipeline <id>]
and wait for a [FINAL] line — no --timeout needed.
Without one: run it in the foreground, bounded so the invocation cannot outlive your own harness timeout:
ddgl attach --plain --follow --detail=full --timeout 600 [--ref <ref> | --pipeline <id>]
If the [FINAL] line reports a timeout (not a pipeline outcome), start an identical invocation again.
This is safe: attach is stateless and each invocation begins with a fresh snapshot of the pipeline.
NOTE: If the pipeline is already terminal or does not exist when you start monitoring, the user might have just pushed and the pipeline is still waiting to be created. In this case, wait for 60 seconds and then re-attempt monitoring. The
--followargument will make sureddgl attachalways monitors the latest pipeline for the ref.
Step 3: Interpret the output
You may see:
[POLL]- rollup summary after a changed poll tick (jobs done/total, stage, failure count). Informational only.[INFO]- an informational log fromddglitself.[PIPE]- a change in the pipeline status.[JOB]- a job finished running and changed state.[FINAL]- the terminal, authoritative outcome. Treat this line as the source of truth regardless of the command's exit code — it names the pipeline id, terminal status, and, on failure, the failed job names.
Step 4: Act on the outcome
- Pipeline Success: stop monitoring and report the pipeline succeeded.
- Some job failed, but the pipeline is still running:
Note that most jobs on
datadog-agentCI have at least one retry for combating flakiness - especially jobs running e2e tests, kmt etc. Unit test, linter or build failures are less likely to be flakes. If you think it is likely the job's failure is just a flake, continue monitoring - gitlab will retry the job once automatically. Otherwise, ask the user whether to continue monitoring, or if this job failure is already a problem. In the latter case, move to Step 5 - Pipeline failed or canceled: Stop monitoring, report the status, and move to Step 5.
- Timeout
[FINAL]: re-invokeddgl attachas in Step 2; this is not a true terminal outcome. - Unexpected error (from
ddglitself, or from the monitoring tool): report what happened. Do not attempt a recovery action.
Step 5: follow-up on failures
Use other ddgl features to investigate failures on the pipeline that failed
Using the pipeline id from the [FINAL] line:
ddgl jobs get --pipeline <id> --failed --json— failed-job metadata.mkdir -p failures && ddgl logs --pipeline <id> --failed --output failures/— export failed-job log output tofailures/- Compare the failure evidence against the current PR's diff. Decide whether the failure is likely caused by this PR, needs more evidence, or is unrelated (e.g. flaky infra, an unrelated pre-existing failure).
- If PR-caused, propose the smallest concrete fix — do not apply it. If not, or inconclusive, report the evidence and your reasoning.
Signals
- GitHub stars
- 4k
- Forks
- 1k
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
follow-pr- Source
- github.com/datadog/datadog-agent