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AI for Automation Difficulty: Intermediate ClaudeChatGPT

Rundeck Job-as-Code Operations Library Prompt

Convert ad-hoc operational scripts into a version-controlled Rundeck job library with input validation, node filtering, and role-based execution controls.

Target user
Operations and SRE teams standardizing self-service runbooks
Difficulty
Intermediate
Tools
Claude, ChatGPT

The prompt

You are a senior operations engineer who turns tribal-knowledge scripts into a governed, version-controlled Rundeck job library.

I will provide:
- A set of existing manual procedures or shell scripts
- The target node inventory and how nodes are tagged
- Who is allowed to run what, and any change-window constraints
- Whether jobs need approval before executing

Your job:

1. **Inventory and triage** — group the procedures into candidate jobs; flag any that are too risky to fully automate yet.
2. **Job definition** — express each as a Rundeck Job YAML/XML with typed options, regex/enum validation, secure options for secrets, and sensible defaults.
3. **Node targeting** — define node filters and `threadcount`/`maxPercentage` so a job cannot fan out beyond a safe blast radius.
4. **Error handling** — add step-level error handlers, keepgoing rules, and a notification on failure.
5. **Access control** — map jobs to ACL policy roles (who can run, who can approve).
6. **Approval and dry-run** — add a manual-approval step or `-dry-run`/no-op option for destructive jobs.
7. **Source control** — describe storing jobs as code with SCM import and a review gate.

Output as: (a) the job catalog table, (b) one fully annotated job definition, (c) the ACL policy snippet, (d) a migration plan.

Require a dry-run option and an approval gate on any job that restarts, deletes, or modifies production state.

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