Creating Ansible Playbooks
About
This skill generates production-ready Ansible playbooks for automating server configuration, software deployment, and infrastructure management. It creates multi-platform playbooks based on user requirements while incorporating security best practices. Use it when you need to quickly create reliable Ansible automation for your configuration management tasks.
Quick Install
Claude Code
Recommended/plugin add https://github.com/jeremylongshore/claude-code-plugins-plusgit clone https://github.com/jeremylongshore/claude-code-plugins-plus.git ~/.claude/skills/Creating Ansible PlaybooksCopy and paste this command in Claude Code to install this skill
Documentation
Overview
This skill empowers Claude to generate Ansible playbooks, streamlining infrastructure automation. It takes your specifications and translates them into executable Ansible code, allowing for repeatable and reliable deployments.
How It Works
- Receiving User Request: Claude receives the user's request for an Ansible playbook, including details about the desired configuration.
- Generating Playbook: Based on the user's input, Claude utilizes the
ansible-playbook-creatorplugin to generate a complete Ansible playbook. - Presenting the Playbook: Claude presents the generated Ansible playbook to the user for review and execution.
When to Use This Skill
This skill activates when you need to:
- Automate server configuration management tasks.
- Deploy applications across multiple servers consistently.
- Create repeatable and reliable infrastructure setups.
Examples
Example 1: Setting up a web server
User request: "Create an Ansible playbook to install and configure Apache on Ubuntu servers."
The skill will:
- Generate an Ansible playbook that installs the Apache web server and configures it with a default virtual host.
- Present the playbook to the user, ready for execution against Ubuntu servers.
Example 2: Deploying a Docker container
User request: "Generate an Ansible playbook to deploy a Docker container running Nginx on CentOS servers."
The skill will:
- Generate an Ansible playbook that installs Docker, pulls the Nginx image, and runs it as a container on CentOS servers.
- Provide the playbook to the user for immediate deployment.
Best Practices
- Specificity: Provide detailed requirements for the desired configuration to generate accurate playbooks.
- Security: Review the generated playbooks for security best practices before deploying them in production.
- Testing: Always test generated playbooks in a staging environment before applying them to production servers.
Integration
This skill integrates with Claude's core capabilities by providing a specialized tool for Ansible playbook creation. It enhances Claude's ability to assist with DevOps tasks and infrastructure automation.
GitHub Repository
Related Skills
sglang
MetaSGLang is a high-performance LLM serving framework that specializes in fast, structured generation for JSON, regex, and agentic workflows using its RadixAttention prefix caching. It delivers significantly faster inference, especially for tasks with repeated prefixes, making it ideal for complex, structured outputs and multi-turn conversations. Choose SGLang over alternatives like vLLM when you need constrained decoding or are building applications with extensive prefix sharing.
evaluating-llms-harness
TestingThis Claude Skill runs the lm-evaluation-harness to benchmark LLMs across 60+ standardized academic tasks like MMLU and GSM8K. It's designed for developers to compare model quality, track training progress, or report academic results. The tool supports various backends including HuggingFace and vLLM models.
content-collections
MetaThis skill provides a production-tested setup for Content Collections, a TypeScript-first tool that transforms Markdown/MDX files into type-safe data collections with Zod validation. Use it when building blogs, documentation sites, or content-heavy Vite + React applications to ensure type safety and automatic content validation. It covers everything from Vite plugin configuration and MDX compilation to deployment optimization and schema validation.
llamaguard
OtherLlamaGuard is Meta's 7-8B parameter model for moderating LLM inputs and outputs across six safety categories like violence and hate speech. It offers 94-95% accuracy and can be deployed using vLLM, Hugging Face, or Amazon SageMaker. Use this skill to easily integrate content filtering and safety guardrails into your AI applications.
