mcpbeat Sign in

Senior Devops Skill for Claude

> DevOps for CI/CD, containers, Kubernetes, and Terraform. Use when building pipelines, containerizing apps, managing clusters, provisioning cloud infra, deploying with blue- green/canary, or handling infrastructure incidents.

9k tokens
context cost
the whole folder, loaded on every use
10
files
ships runnable scripts
0
copies elsewhere
how many repositories repackaged it
447
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/borghei/Claude-Skills --skill senior-devops

What comes with it

32 834 bytes besides the instruction
references/cicd_pipeline_guide.md
references/cloud_platform_guide.md
references/deployment_strategies.md
references/devops-workflows-and-operations.md
references/infrastructure_as_code.md
references/kubernetes_patterns.md
scripts/deployment_manager.py
scripts/pipeline_generator.py
scripts/terraform_scaffolder.py

The instruction itself

7 sections, as written by the author

Senior DevOps Engineer

The agent generates CI/CD pipelines, scaffolds Terraform infrastructure, and manages deployments with strategy selection, health checks, and rollback support.

Core Capabilities

  • CI/CD pipeline generation — fail-fast, cached, immutable-artifact pipelines for GitHub Actions, GitLab CI, Jenkins, and CircleCI with matrix testing and promotion gates.
  • Containerization — production multi-stage Dockerfiles with non-root users, healthchecks, and runtime secret injection.
  • Kubernetes deployment — Deployments with liveness/readiness/startup probes, resource limits, and security context; Helm, HPA/VPA/KEDA, network policies, RBAC.
  • Infrastructure as Code — Terraform module scaffolding, remote state with locking, environment separation, and CI drift detection.
  • Deployment strategies — rolling, blue-green, canary, and feature-flag rollouts with health checks and automated rollback.
  • Monitoring & SLOs — Four Golden Signals dashboards, SLO/error-budget targets, and deployment-freeze recommendations.

When to Use

  • Building or optimizing a CI/CD pipeline.
  • Containerizing an app or deploying to Kubernetes.
  • Provisioning cloud infrastructure with Terraform.
  • Choosing and executing a deployment strategy (blue-green/canary).
  • Handling an infrastructure incident or rollback.

Clarify First

Before generating pipelines or infra, confirm these inputs. If any is unknown or vague, ASK — do not assume:

  • [ ] CI/CD platform — GitHub Actions / GitLab CI / Jenkins / CircleCI (changes the generated pipeline config)
  • [ ] Deployment strategy — rolling / blue-green / canary / feature-flag (sets health checks and rollback in the deployment plan)
  • [ ] Target cloud & IaC scope — provider and which Terraform modules are needed (drives terraform_scaffolder)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

Tools

| Tool | Purpose | Command |

|------|---------|---------|

| pipeline_generator.py | Analyze a project and generate a CI/CD pipeline config (GitHub Actions, GitLab CI, Jenkins, CircleCI) | python scripts/pipeline_generator.py <project-path> --json |

| terraform_scaffolder.py | Scaffold a Terraform module structure with state config | python scripts/terraform_scaffolder.py <target-path> --json |

| deployment_manager.py | Produce a deployment plan with health checks and rollback | python scripts/deployment_manager.py <target-path> --json |

All tools support --verbose/-v, --json for machine-readable output, and --output/-o for file writing.

References

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

  • references/devops-workflows-and-operations.md — the three end-to-end workflows (containerize & deploy, Terraform IaC, CI/CD design) with worked Dockerfile/YAML/HCL examples, the deployment-strategy selection matrix and canary ladder, monitoring essentials (Four Golden Signals, SLO targets), anti-patterns, and the troubleshooting table. Read when executing any workflow or diagnosing an incident.
  • references/cicd_pipeline_guide.md — pipeline patterns, platform comparisons, optimization.
  • references/infrastructure_as_code.md — Terraform patterns, module design, state management.
  • references/deployment_strategies.md — strategy details, rollback procedures, traffic management.
  • references/kubernetes_patterns.md — Helm charts, HPA/VPA/KEDA decisions, network policies, and RBAC patterns.
  • references/cloud_platform_guide.md — AWS/GCP/Azure service comparison, multi-cloud strategy, and cost optimization.

Integration Points

| Skill | Integration |

|-------|-------------|

| senior-secops | Security scanning in CI/CD, container image scanning, compliance checks |

| senior-architect | Infrastructure design decisions, service topology |

| senior-backend | Application containerization, health endpoints, config management |

| code-reviewer | Terraform plan review, pipeline config review |

| incident-commander | Incident escalation, postmortem, rollback procedures |


Last Updated: June 2026

Version: 2.2.0

Other skills for the same job

different authors, same section of the catalogue
Azure Kubernetes Automatic Readiness
by microsoft
vendor ×3

Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.

13k tokens
Capacity
by microsoft
vendor ×3

Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.

6k tokens scripts
Customize
by microsoft
vendor ×3

Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).

8k tokens
Deploy Model
by microsoft
vendor ×3

Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).

26k tokens scripts
Preset
by microsoft
vendor ×3

Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).

9k tokens
Lamindb
by christophacham
×3

This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.

22k tokens
Latchbio Integration
by christophacham
×3

Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.

12k tokens
Modal
by christophacham
×3

Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.

17k tokens

How to use it

Copy the folder

Take borghei/senior-devops from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.