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GoogleMakesMeBetterGoogleMakesMeBetter/claude-skills-collection/skills/deployment/devops-engineer/SKILL.md

devops-engineer

Skill1 starsChanged 9 months agoNo licenceView on GitHub

Use this agent when building or optimizing infrastructure automation, CI/CD pipelines, containerization strategies, and deployment workflows to accelerate software delivery while maintaining reliability and security. Specifically:\\n\\n<example>\\nContext: A team has repetitive manual infrastructure provisioning processes that take hours and need to migrate to Infrastructure as Code with multiple cloud environments.\\nuser: \"We're manually provisioning servers for dev, staging, and production. It's error-prone and slow. We need to automate this with IaC using Terraform, support AWS and Azure, and ensure consistency across environments.\"\\nassistant: \"I'll architect a comprehensive IaC solution. I'll design modular Terraform modules for compute, networking, storage, and databases. I'll set up multi-environment structures with dev/staging/prod configurations, implement state management, create automated drift detection, and establish GitOps workflows so deployments trigger on code changes. I'll also add security scanning and compliance automation.\"\\n<commentary>\\nUse the devops-engineer agent when implementing Infrastructure as Code, automating infrastructure provisioning, or migrating from manual processes to fully automated, version-controlled infrastructure management.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: A development team struggles with slow, unreliable deployments and wants to establish modern CI/CD practices with multiple deployment strategies.\\nuser: \"Our deployments take 2 hours, fail frequently, and have no rollback capability. We need a proper CI/CD pipeline with containerization, automated testing gates, canary deployments, and quick rollbacks.\"\\nassistant: \"I'll design a complete CI/CD transformation. I'll containerize your application with Docker, build an automated pipeline using GitHub Actions or GitLab CI with test automation and security scanning, implement canary and blue-green deployment strategies, set up comprehensive monitoring with automated rollbacks, and create runbooks for incident response. I'll target 10+ deployments per day with sub-30-minute MTTR.\"\\n<commentary>\\nInvoke this agent when establishing or improving CI/CD pipelines, implementing deployment automation, reducing mean time to deployment, and enabling teams to deploy safely and frequently.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: Production incidents reveal gaps in monitoring, observability, and incident response processes that delay recovery and create toil for the team.\\nuser: \"We had an outage last week that took 4 hours to detect and 2 hours to resolve. We need better monitoring, alerting, observability, and documented incident response procedures.\"\\nassistant: \"I'll establish production observability and incident response. I'll implement comprehensive metrics collection, centralized logging, distributed tracing, and intelligent alerting with alert routing. I'll create SLOs and error budgets to balance feature velocity with reliability. I'll establish on-call procedures, create runbooks for common incidents, and implement blameless postmortem processes. This will reduce MTTR to under 30 minutes and build a healthy on-call culture.\"\\n<commentary>\\nUse this agent when building monitoring and observability infrastructure, establishing incident response procedures, reducing mean time to resolution, and improving operational reliability and team satisfaction.\\n</commentary>\\n</example>

We split this skill into the rules it gives an agent

2 rules. 0 are common, 2 are distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 27 edited versions we read keep these.

  • 2

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

No licence file, so all rights are reserved — read it at the source. We count its rules but don't quote them. Read them on GitHub.

“Common” is measured: kept by at least 6 in 10 of the 27 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

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