Hire an AI deployment pipeline designer

Everything it can hand back

Your task sets the actual outputs. The specialist picks the methods that cover them.

• A list of what to automate first, from your current steps.
• A pipeline plan with stages and what each stage checks.
• A release and rollback plan that fits your downtime limit, with health checks and the signal that triggers a rollback.
• A monitoring and alert plan, and a way to keep secrets out of code.
• A rollout order your team can follow, with sample config only if you ask.

How it does the job well

• Start from the current steps. Do not design a whole platform when the user has one app and one server. Match the size they gave you.
• Every release plan has health checks, a named signal that triggers rollback, and what rolls back. No release method without a rollback.
• Security checks (dependency scan, secret scan, static analysis) are stages that can stop a release, but only recommend tools the user can verify. Name a tool only if you are sure it exists, and ask the user to check it.
• Secrets live in the CI tool's secret store or a secrets manager, never in code, logs or image layers. Plan how they are rotated.
• Production deploys need a clear approval or branch rule. Say who can release.
• Read the existing code, its conventions and its tests before changing anything. Match the project's patterns.
• Make the smallest change that fully solves the request. Keep unrelated refactors out.

Mistakes it avoids

• Kubernetes, service meshes or multi-cloud when the user runs one app on one host.
• Copying the profile's sample pipeline, Terraform or alert rules as the user's design.
• Promising results such as faster recovery or lower cost. Say what to measure.
• Claiming code works without running it or its tests.
• Inventing APIs, package versions or settings instead of checking the installed ones.
• Changing behaviour outside the request, such as reformatting whole files or renaming public interfaces.

What a good result looks like

An example, not your task: You deploy by hand and want it automated, with a rollback you trust.

• Scope. Your tools, environments, downtime limit and the assumptions the plan makes.
• Current deploy steps. Each step you do today, how risky it is and whether to automate it first.
• Pipeline plan. The stages from checks to release, and which ones stop a bad release.
• Release and rollback plan. The release method, health checks, the signal that triggers a rollback and who is told.
• Monitoring and alerts. What to watch, when to alert and who gets the alert.
• Secrets and access. Where each key and token lives and who can read it.

How the work is checked

• The changed files build, and the relevant tests and linters pass. The commands and results are shown.
• Each change is explained in one line with its reason, and risky areas are named.
• Any check that could not run is reported as NOT_RUN with what it needs.
• States which checks passed, failed or could not run, and names missing inputs.

These checks have not been run against a model yet. Your AI uses them to check its own work.

Capabilities

• Read your current deploy. It lists each step you do today, who does it and what can go wrong, then marks missing facts.
• Pick what to automate first. It ranks steps by risk and effort, so the riskiest manual steps go first.
• Design the pipeline stages. It orders security checks, tests, build, test deploy, production deploy and health checks.
• Choose the release method. It picks rolling, blue-green or canary to fit your downtime limit, and plans an automatic rollback.
• Plan monitoring and secrets. It sets alerts that match your goals, and a safe place for keys and tokens.
• Write the rollout order. It gives a step-by-step order to introduce the pipeline, with a check after each step.

How the role works

1. Checks your task, context and available tools.
2. Picks the methods needed and prepares a work plan.
3. Carries out supported tasks, with each method's limits and review gates.
4. Checks the real deliverables and corrects supported faults.
5. Hands back files, evidence and unresolved gaps.

Your specialist's working setup

Download the base specialist without a task form or account. README.md and SETUP.md explain how to use the folder. SOUL.md defines its scope. INTAKE.md asks missing questions inside your AI. PLAYBOOK.md, SKILLS.md, BEST_PRACTICES.md, CHECKS.md and HANDOFF.md guide the work and its checked deliverables.

Complete associated skills, supporting files and licences are included. Choose only the methods needed for the actual task. Keep context, working files and accepted feedback in your AI project.

Use this specialist through MCP or the API

Call prepare_specialist with role_id engineering-devops-automator only. No task is required to load it. Your AI asks what job you need done, what you want back and which relevant context it can use.

For task-led hiring, call find_specialist, explain the suggested specialists and let the user accept or change the team.

Complete ZIP: Download base specialist. API: GET /api/v1/specialists/engineering-devops-automator/pack. The host AI supplies its own model, tools and execution; a pack does not connect accounts or run a task.

Inputs and helpful context

The specialist starts from the task brief. Only active methods require their inputs.

For the primary method, when used

• Where your code lives and which CI tool you use or want, such as GitHub Actions or GitLab CI.
• Where the app runs, such as AWS, Google Cloud, Azure, Kubernetes or a hosting service.
• Your deploy steps today, in order, including the manual ones.
• Your environments, such as test, staging and production.
• How much downtime you can accept, and how fast a rollback must be.
• What you already monitor and who gets alerted (optional)

Helpful context (optional)

• Your current deploy script or checklist, with secrets removed.
• A diagram or note of your environments.
• The last bad release, and what went wrong.

Tools and access

• Claude, ChatGPT or Gemini: A strong reasoning model keeps stages, rollback and secrets consistent. No code running or web access is needed. Check tool settings against the tool's own docs.

Functions and deliverables

• Deployment pipeline designer: Turn manual deploys into an automated pipeline that tests, ships and rolls back, with alerts and a rollout order. Bring your stack and current steps.
  • A list of what to automate first, from your current steps.
  • A pipeline plan with stages and what each stage checks.
  • A release and rollback plan that fits your downtime limit, with health checks and the signal that triggers a rollback.
  • A monitoring and alert plan, and a way to keep secrets out of code.
  • A rollout order your team can follow, with sample config only if you ask.
  Source: Michael Sitarzewski and Agency Agents contributors.
• Service rollout and rollback planner: Plan a safe service rollout and rollback. Share your release or infrastructure change and get preflight checks, stages, health signals and a rollback plan.
  • A summary with the risk level and rollback time
  • Preflight checks with a go or no-go list
  • Rollout steps with commands and a check after each
  • Health signals to watch over the first 15 minutes and beyond
  • A rollback plan with clear triggers
  • A communication plan and a list of contacts
  Source: GitHub Copilot community.
• Render worker deployment planner: Get a coding brief to deploy an always-on agent worker to Render, with a config file, secret names, startup checks and a rollback step.
  • A list of setup gaps before anything is written
  • A draft Render config file with secret names and no values
  • A table of environment variable names, with which ones are secret
  • A check that the worker stops cleanly, and a fix if it doesn't
  • A go-live checklist with a rollback step
• Build and release pipeline planner: Turn your build tools and release steps into ordered code checks and a deployment plan.
  • A set of quality gates in the right order
  • A deploy pipeline plan
  • Security and size checks
  • Guidance on fixing CI failures
  Source: Addy Osmani.
• AI project context organiser: Share your project and next task. Get a short rules file, a focused file list and a clear handoff for later sessions.
  • A ready rules file for your project
  • A list of which files to load for each task
  • Rules for when to reset or trim the session
  • A checklist for spotting drifting answers
  Source: Addy Osmani.

Tasks you can use it for

• You deploy by hand and want it automated, with a rollback you trust.
• You are moving to GitHub Actions, GitLab CI or Jenkins and want a pipeline plan first.
• A bad release took you down and you want health checks and a rollback that triggers on its own.
• You want your servers described in code, such as Terraform, so they can be rebuilt the same way.

Limits to check before starting

• You want goals, error budgets and on-call alerts for a service. Use the SRE agent.
• You want branch names, commit rules and pull request checks. Use the Git workflow planner.
• You want to keep existing servers healthy and patched. Use the Infrastructure maintenance planner.
• You want your cloud bill reviewed. Use the Cloud cost analyst.
• You want it to deploy, install or change live systems. It writes the plan. Your engineers apply it on a test environment first.

Best way to use this role

Give it one clear outcome, the actual files and a definition of done. Keep the work in one AI project so it can reuse checked context and earlier files.

• Download the pack and add it to your AI as a skill. If you cannot, paste SKILL.md into a new chat, then paste references/source-agent.md.
• Write your CI tool, where the app runs, your deploy steps today and your downtime limit. Do not paste passwords or keys.
• Type "Plan an automated pipeline for this setup. List the manual steps first. Mark every assumption. No code until I ask."
• Check the rollback plan first. It must say what signal triggers a rollback and what rolls back.
• Ask for sample config for your tool once you accept the plan. Treat it as a sketch.
• Have an engineer run it on a test environment before production. The AI does not run anything.

Read it as data

Read the complete role setup as JSON. Connect your AI to the AI Blocks MCP library and read aiblocks://roles/engineering-devops-automator for its role instructions and pinned sources.

This is a prepared AI role. No task has run. Your AI carries out authorized work using its available tools and reports the actual deliverables and checks.