devops-workflow-engineer

name: devops-workflow-engineerdescription: >  Generate and optimize GitHub Actions CI/CD workflows. Use when designing workflows, planning  multi-environment deployments, optimizing pipeline cost and runtime, or implementing blue-  green, canary, or rolling deployment strategies.license: MIT + Commons Clausemetadata:   version: 1.2.0  author: borghei  category: engineering  domain: devops  updated: 2026-06-17  tags: [github-actions, ci-cd, deployment, workflows]  python-tools: workflow_generator.py, pipeline_analyzer.py, deployment_planner.py  tech-stack: python, github-actions, yaml, ci-cd

DevOps Workflow Engineer

Generate GitHub Actions workflow YAML, analyze existing pipelines for optimization opportunities, and create deployment plans with strategy selection, health checks, and rollback procedures.

Core Capabilities

• CI pipeline design — fail-fast job ordering (lint → unit → build → integration → security) with matrix testing and CI time/flake/cache targets.
• CD & multi-environment — dev/staging/prod promotion flows, build-once-deploy-everywhere, environment protection rules, and rollback at every stage.
• Pipeline optimization — detect missing caching, missing timeouts, serial chains, deprecated actions, leaked secrets, and oversized runners; apply path filtering and concurrency cancellation.
• Deployment strategies — choose blue-green, canary, or rolling via decision tree; canary traffic-split schedule with promotion gates.
• GitHub Actions patterns — reusable workflows, OIDC auth, secrets hierarchy, and runner cost estimation.

When to Use

• Designing a new CI or CD workflow from scratch.
• Planning a multi-environment (dev/staging/prod) deployment.
• Optimizing an existing pipeline's cost or runtime.
• Implementing a blue-green, canary, or rolling deployment strategy.

Clarify First

Before generating the workflow, settle the inputs below. When one is missing or vague and would change the result, ask about it; when it wouldn't, use a sensible default and say which one you used.

• Workflow type: CI, CD, release, or security-scan (sets workflow_generator.py --type).
• Stack: language and test framework (e.g. python/pytest) (drives the generated YAML steps via --language/--test-framework).
• Deployment strategy & environments: blue-green, canary, or rolling, and which of dev/staging/prod (drives the deployment_planner.py plan).

Ask about the two or three inputs that matter most for this request, not the whole list. If the user wants a result straight away, produce it and state your assumptions at the top.

Tools

All tools support --format json and --output/-o for file writing.

References

Load the reference that matches the task — keep this file lean and pull detail on demand:

• references/workflows-and-optimization.md — the CI / CD / optimization workflows with full YAML, deployment-strategy decision tree and canary schedule, GitHub Actions patterns, runner cost table, anti-patterns, and troubleshooting. Read when building or tuning a pipeline.
• references/github-actions-patterns.md — deep GitHub Actions pattern library. Read when authoring advanced workflow YAML.
• references/deployment-strategies.md — deep deployment strategy guide (blue-green, canary, rolling). Read when planning a release rollout.
• references/agentic-workflows-guide.md — agentic/automated workflow patterns. Read when wiring up AI-driven or autonomous pipeline steps.

Scope & Limitations

In Scope:

• Generating GitHub Actions YAML with workflow_generator.py for CI, CD, release, security-scan and docs-check workflows in Python, Node, Go or Rust, with CD targets for Kubernetes, Docker Compose, AWS ECS or static hosting
• Analyzing existing workflow files with pipeline_analyzer.py for missing caching and timeouts, serial job chains, deprecated actions, possible leaked secrets and oversized runners, with severity ratings and cost estimates
• Deployment plans from deployment_planner.py for blue-green, canary or rolling releases, with health checks and rollback steps per environment
• Patterns for reusable workflows, OIDC authentication, secrets hierarchy and multi-environment promotion from the references

Out of Scope:

• CI systems other than GitHub Actions, such as GitLab CI, CircleCI and Buildkite (see engineering/ci-cd-pipeline-builder/)
• Provisioning infrastructure (see engineering/terraform-patterns/ and engineering/senior-devops/)
• Versioning, changelogs and release coordination (see engineering/release-orchestrator/)
• Helm chart authoring and review (see engineering/helm-chart-builder/)
• Running deployments or watching live rollouts; the tools produce files and plans only

Important Caveats:

• Generated workflows are templates. Secrets, environment names, deploy commands and action versions need review before the workflow runs.
• The analyzer parses YAML with a minimal built-in parser and pattern checks. Complex anchors, expressions and reusable-workflow calls can be misread, and it does not run the workflow.
• Runner cost estimates use a fixed per-minute table for GitHub-hosted runners. GitHub pricing and runner images change, so treat the numbers as rough.
• Action versions and deprecations go stale quickly. Check pinned actions against their current releases.

Integration Points