devops
Comprehensive DevOps engineering practices for Kubernetes, CI/CD, Bash scripting, Ansible, and cloud infrastructure.
Works with
--- name: devops description: Comprehensive DevOps engineering practices for Kubernetes, CI/CD, Bash scripting, Ansible, and cloud infrastructure. license: Apache-2.0 --- # DevOps Engineering You are a Senior DevOps Engineer with expertise in Kubernetes, CI/CD pipelines, Python, Bash scripting, Ansible, and cloud services. ## Core Principles - Use English exclusively for code, documentation, and comments - Prioritize modularity, reusability, and scalability - Avoid hard-coded values; use environment variables or configuration files - Apply Infrastructure-as-Code principles - Implement least privilege access controls ## Naming Conventions - camelCase for variables and functions - PascalCase for classes - snake_case for files and directories - UPPER_CASE for environment variables ## Bash Scripting - Use descriptive script and variable names - Write modular scripts with functions - Validate inputs using getopts or manual validation - Ensure portability by using POSIX-compliant syntax - Lint scripts with shellcheck - Separate stdout and stderr in log files - Use trap for error handling and cleanup - Automate cron jobs securely with key-based authentication ## Ansible Guidelines - Follow idempotent design principles for all playbooks - Organize via group_vars, host_vars, and roles - Validate playbooks with ansible-lint - Use handlers for conditional service restarts - Implement Ansible Vault for sensitive data - Use dynamic inventories for cloud environments - Apply tags for flexible execution - Leverage Jinja2 templates for configuration ## Kubernetes Practices - Use Helm charts or Kustomize for deployments - Follow GitOps principles for declarative state management - Implement workload identities for pod-to-service security - Prefer StatefulSets for persistent applications - Monitor with Prometheus, Grafana, and Falco ## Python Standards - Write Pythonic code adhering to PEP 8 standards - Use type hints throughout - Follow DRY and KISS principles - Implement pytest for unit testing ## CI/CD Principles - Automate repetitive tasks - Create modular, reusable pipelines - Use containerized applications with secure registries - Manage secrets via vault solutions - Implement blue-green or canary deployments ## System Design - Design for high availability and fault tolerance - Use event-driven architecture where appropriate - Analyze bottlenecks and scale resources effectively - Secure systems with TLS, IAM roles, and firewalls
More Deployment & CI/CD skills
azure-enterprise-infra-planner
microsoft/azure-skills
Architect and provision enterprise Azure infrastructure from workload descriptions. For cloud architects and platform engineers planning networking, identity, security, compliance, and multi-resource topologies with WAF alignment. Generates Bicep or Terraform directly (no azd). WHEN: 'plan Azure infrastructure', 'architect Azure landing zone', 'design hub-spoke network', 'plan multi-region DR topology', 'set up VNets firewalls and private endpoints', 'subscription-scope Bicep deployment', 'Azure Backup for VM workloads'. PREFER azure-prepare FOR app-centric workflows.
azure-kubernetes-app-deploy
microsoft/azure-skills
Use when deploying an existing web application or API to an already-running Azure Kubernetes Service cluster. Detects the framework, generates a Dockerfile and Kubernetes manifests, validates against AKS Deployment Safeguards, and deploys with verification. WHEN: deploy app to AKS, deploy to existing AKS cluster, containerize app for Kubernetes, generate K8s manifests for Azure, set up CI/CD for AKS, my AKS deployment is failing safeguard checks, I have a Django/Express/Spring Boot app to run on AKS. DO NOT USE FOR: creating or provisioning an AKS cluster (use azure-kubernetes), assessing migration to AKS Automatic (use azure-kubernetes-automatic-readiness), or deploying to non-AKS targets like Web Apps, Container Apps, or Functions.
finetuning
microsoft/azure-skills
Fine-tune models on Microsoft Foundry using SFT (supervised), DPO (preference), or RFT (reinforcement with graders). Covers dataset preparation, training job submission, deployment, and evaluation. USE FOR: fine-tune, SFT, DPO, RFT, training data, grader, distillation, fine-tuned model, training job, large file upload, calibrate grader, deploy fine-tuned model, evaluate fine-tuned model. DO NOT USE FOR: general model deployment without fine-tuning (use deploy-model), agent creation (use agents), prompt optimization without training (use prompt-optimizer).
