🔥 CloudOps Automation at Scale 🦅

🌟 You can use Enterprise-grade CloudOps Automation Runbooks, integrating AWS Cloud Foundations & FinOps best practices for DevOps and SRE teams 🌐

🏆 The CloudOps Runbooks 👁️

The CloudOps Runbooks project shows solid foundation but needs optimization in several key areas: dependency management, CI/CD pipeline efficiency, code organization following cloud-foundations patterns, and integration of battle-tested AWS automation tools.

• 🏆 Mission: Our mission is to simplify CloudOps Automation for DevOps and SRE teams by providing an extensive, community-driven repository of actions and runbooks that streamline day-to-day operations.
• 👁️ Vision: Our vision is to be the 🥇 One-Stop Multi-Cloud Platform Engineering & Best Practices Solution for all CloudOps Automation needs, allowing DevOps and SRE teams to automate their workflows with ease, improve efficiency, and minimize toil.

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🎯 Key Features

Core Capabilities

• 🏗️ AWS Cloud Foundations Integration - Battle-tested templates and automation from AWS best practices
• 📊 Cloud Foundations Assessment Tool (CFAT) - Automated AWS account assessment and compliance checking
• 🔍 Comprehensive Inventory Scripts - Full AWS resource discovery and management
• 🎛️ Control Tower Automation - Streamlined AWS Control Tower deployment and management
• 🔐 Identity & Access Management - AWS IAM and SSO/IDC automation
• 📝 Centralized Logging - CloudTrail and Config aggregation patterns
• 🏷️ Tagging Governance - Organizational tagging policies and enforcement

Technical Excellence

• ⚡ Lightning-Fast Toolchain - Powered by uv for dependency management, ruff, ty
• 🐳 Production-Ready PyPi and Containers - Multi-arch Docker images with Wolfi base
• 🧪 Comprehensive Testing - Automated testing with pytest and coverage reporting
• 📚 Rich Documentation - MkDocs-powered documentation site
• 🤖 AI-Ready - Integration points for OpenAI/Anthropic and local LLMs

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🚀 Quick Start

1. Installation via pip (Recommended) or uv (Fastest)

pip install runbooks

uv pip install runbooks

2. Installation via Docker

docker run -it nnthanh101/runbooks:latest runbooks --help

3. Run in VSCode's DevContainer 🐳

1. Open the project in VSCode.
2. Install the Remote - Containers extension.
3. Reopen in Container:
   Command Palette Ctrl+Shift+P → Reopen in Container.

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Python Template

🛠️ Features

• 🥉 ✅Lightning-Fast Toolchain: Powered by 📦 uv - Next-generation Python dependency and build management, 💅 ruff - Linting and formatting at blazing speed, and 🧪 pytest - Robust testing framework with coverage reports.

• 🥈 ✅Effortless CI/CD Pipelines: 🛠️ Taskfile Automation - Say goodbye to manual SDLC repetitive tasks, 🐳 Containerized Workflows – 🛡️ Security-first practices and Multi-stage Wolfi-based Docker builds for scalable production-ready environments, and ⚙️ Auto-publish to PyPI and GitHub Container Registry (GHCR) with GitHub Actions.

• 🥇 ☑️CloudOps Automation and FinOps Toolkit – Pre-configured hybrid-cloud workflows and seamlessly integrations (jupyterlab, mkdocs, boto3, moto) for managing cloud infrastructure 🌐.

  Feature	Toolchain	Purpose
  🛠️ Configuration	pyproject.toml	Centralized configuration for dependencies, testing, and linting.
  🧹 Task Automation	Taskfile	Automates repetitive tasks like linting, testing, and publishing.
  📦 Python Dependencies	uv	Lightning-fast dependency resolution, caching, and builds.
  💅 Linting & Formatting	ruff	Enforces code quality standards, auto-formatting, and import sorting.
  🧪 Testing Framework	pytest	Comprehensive unit tests, integration tests with coverage reporting.
  🐳 Docker Integration	Dockerfile + DevContainer	Optimized wolfi-based multi-stage builds for CI/CD and local development environments.
  🦾 CI/CD Pipelines	GitHub Actions	Automated builds, tests, and deployments to PyPI and GHCR.
  📝 Security Compliance	chainguard/wolfi-base + SBOM + Attestations	Ensures compliance, vulnerability scanning, and security transparency.

Project Structure

🛠 End-to-end Production-grade project structure for successful 💎 CloudOps Automation and Visual Analytics FinOps projects 🚀

cloudops-automation/
├── .devcontainer/     ## Dev Container configurations
│   └── Dockerfile     ## Container image build file
├── .github/           ## CI/CD workflows
│   ├── workflows/     ## GitHub Actions workflows
│   └── templates/     ## Workflow templates
├── .vscode/           ## IDE-specific configurations
├── config/            ## Configuration files (YAML, JSON)
├── data               🔍 Where all your raw and processed data files are stored.
│   ├── external       <- Data from third-party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, unprocessed, immutable data dump.
│
├── docs               📓 A default mkdocs project; see mkdocs.org for details
│   ├── api/                 ## API documentation
│   ├── architecture/        ## Architecture diagrams
│   ├── tutorials/           ## Tutorials and guides
│   ├── getting-started.md   ## Quickstart guide
│   └── index.md             ## Overview documentation
│
├── logs/                    ## Log files for debugging
|
├── models             🧠 Store your trained and serialized models for easy access and versioning.
│
├── notebooks          💻 Jupyter notebooks for experiments and visualization.
│   ├── data_exploration.ipynb
│   ├── data_preprocessing.ipynb
│   ├── model_training.ipynb
│   └── model_evaluation.ipynb
│
├── pyproject.toml     <- Project configuration file with package metadata for 
│                         runbooks and configuration for tools like black
│
├── src/                            ## 🧩 Source code for use in this project.
│   ├── runbooks/                   ## Main module for CloudOps Runbooks automation
│   │   ├── __init__.py             ## Package initializer
│   │   ├── calculator.py           ## [Python101] Calculator
│   │   ├── config.py
│   │   ├── exceptions.py
│   │   ├── utils.py                ## Utility scripts (logging, configs)
│   │   └── cleanup.py              ## Cleanup automation runbook
│   ├── main.py     
├── test/                           ## Unit and integration tests
│   ├── conftest.py
│   ├── __init__.py
│   ├── test_calculator.py          ## [Python101] Test cases for calculator
│   ├── test_utils.py               ## Test cases for utils
│   └── test_exceptions.py         
├── templates/                      ## Terraform and CloudFormation templates
├── tools/                          ## Developer tools and scripts
├── .dockerignore                   ## Docker ignore file
├── .env                            ## Environment variables
├── .gitignore                      ## Git ignore file
├── .python-version                 ## Python version management
├── .gitignore
├── mkdocs.yml                      # Documentation generator configuration
├── README.md          🤝 Explain your project and its structure for better collaboration.
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            📊 Generated analysis (reports, charts, and plots) as HTML, PDF, LaTeX.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   🛠 The requirements file for reproducing the analysis environment, for easy environment setup.
└── Taskfile           <- Taskfile with convenience commands like `task data` or `task train`