Background: It took me about 20 seconds to crash a staging worker in Kubernetes:

The above run will show up as "lost" in the next 30 minutes.

This PR uses prlimit to set RLIMIT_AS on each forked child process, capping virtual address space so a runaway
run crashes itself instead of OOM-killing the pod.

It's opt-in by detection: active when prlimit (from util-linux) is available on Linux; no-op on macOS / local dev, and it adds util-linux to the worker Docker image so it's available

Testing on staging

1. Deploy this branch to a worker connected to app.staging.openfn.org
2. Create a workflow with a job that spikes memory beyond the configured limit, e.g.:

  fn(state => {
    const arr = [];
    while (true) { arr.push(new Array(1e6).fill('x')); }
    return state;
  });

3. Run the workflow and confirm:

• The run fails with an OOM error (not a pod restart)
• Other concurrent runs on the same worker are unaffected
• The worker recovers and picks up new runs normally

4. Check worker logs for cgroup memory enforcement enabled at startup and killed by SIGKILL (probable OOM) on the failing run
5. Verify no leftover openfn-worker-* directories under the cgroup root after the run completes

AI Usage

Please disclose whether you've used AI anywhere in this PR (it's cool, we just
want to know!):

• I have used Claude Code
• I have used another model
• I have not used AI

You can read more details in our
Responsible AI Policy

Release branch checklist

Delete this section if this is not a release PR.

If this IS a release branch:

• Run pnpm changeset version from root to bump versions
• Run pnpm install
• Commit the new version numbers
• Run pnpm changeset tag to generate tags
• Push tags git push --tags

Tags may need updating if commits come in after the tags are first generated.