DevOps AI Toolkit

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DevOps AI Toolkit is an AI-powered development productivity platform that enhances software development workflows through intelligent automation and AI-driven assistance.

Who is this for?

Kubernetes Deployment

- Developers: Deploy applications without needing deep Kubernetes expertise - Platform Engineers: Create organizational deployment patterns that enhance AI recommendations with institutional knowledge and best practices, and scan cluster resources to enable semantic matching for dramatically improved recommendation accuracy

Documentation Testing

- Documentation Maintainers: Automatically validate documentation accuracy and catch outdated content - Technical Writers: Identify which sections need updates and prioritize work effectively - Open Source Maintainers: Ensure documentation works correctly for new contributors

Shared Prompts Library

- Development Teams: Share proven prompts across projects without file management - Project Managers: Standardize workflows with consistent prompt usage across teams - Individual Developers: Access curated prompt library via native slash commands

AI Integration

- AI Agents: Integrate all capabilities with Claude Code, Cursor, or VS Code for conversational workflows

Key Features

Kubernetes Deployment Intelligence

🔍 Smart Discovery: Automatically finds all available resources and operators in your cluster 🧠 Semantic Capability Management: Discovers what each resource actually does for intelligent matching 🤖 AI Recommendations: Get deployment suggestions tailored to your specific cluster setup with enhanced semantic understanding 🔧 Operator-Aware: Leverages custom operators and CRDs when available 🚀 Complete Workflow: From discovery to deployment with automated Kubernetes integration

Capability-Enhanced Recommendations

Transform how AI understands your cluster by discovering semantic capabilities of each resource:

The Problem: Traditional discovery sees sqls.devopstoolkit.live as a meaningless name among hundreds of resources.

The Solution: Capability management teaches the system that sqls.devopstoolkit.live handles PostgreSQL databases with multi-cloud support.

Before Capability Management:

User: "I need a PostgreSQL database"

AI: Gets 400+ generic resource names → picks complex multi-resource solution

Result: Misses optimal single-resource solutions

After Capability Management:

User: "I need a PostgreSQL database"  

AI: Gets pre-filtered relevant resources with rich context

Result: Finds sqls.devopstoolkit.live as perfect match ✨

Get Started: See the Tools and Features Overview for complete guide to all available tools including capability management, deployment recommendations, and workflow integration.

Documentation Testing & Validation

📖 Automated Testing: Validates documentation by executing commands and testing examples 🔍 Two-Phase Validation: Tests both functionality (does it work?) and semantic accuracy (are descriptions truthful?) 🛠️ Fix Application: User-driven selection and application of recommended documentation improvements 💾 Session Management: Resumable testing workflows for large documentation sets

Organizational Pattern Management

🏛️ Pattern Creation: Define organizational deployment patterns that capture institutional knowledge 🧠 AI Enhancement: Patterns automatically enhance deployment recommendations with organizational context 🔍 Semantic Search: Uses Vector DB technology for intelligent pattern matching based on user intent 📋 Best Practices: Share deployment standards across teams through reusable patterns

Shared Prompts Library

🎯 Native Slash Commands: Prompts appear as /dot-ai:prompt-name in your coding agent 📚 Curated Library: Access proven prompts for code review, documentation, architecture, and project management 🔄 Zero Setup: Connect to MCP server and prompts are immediately available across all projects 🤝 Team Consistency: Standardized prompt usage with centralized management

AI Integration

⚡ MCP Integration: Works seamlessly with Claude Code, Cursor, or VS Code through Model Context Protocol 🤖 Conversational Interface: Natural language interaction for deployment, documentation testing, pattern management, and shared prompt workflows

Setup Required: See the MCP Setup Guide for complete configuration instructions.

See It In Action

This video explains the platform engineering problem and demonstrates the Kubernetes deployment recommendation workflow from intent to running applications.

Quick Start

Prerequisites

For Kubernetes deployment and documentation testing:
- Claude API key (required for AI analysis)
- Get your API key from Anthropic Console (requires account login)
<!-- dotai-ignore: Console URL may return 403 - expected behavior for auth-protected endpoint -->
- Set it as environment variable: export ANTHROPIC_API_KEY=your_api_key_here

For shared prompts library:
- No API key required - Works with any MCP-enabled coding agent (other features like deployments do require ANTHROPIC_API_KEY)

For Kubernetes deployment recommendations:
- kubectl configured with cluster access
- Verify cluster access with: kubectl get nodes
- Should show your cluster nodes without authentication errors
<!-- dotai-ignore: kubectl verification command output format - implementation-specific -->

For documentation testing:
- Documentation files to test (Markdown, HTML, etc.)
- File system access to the documentation you want to validate

For organizational pattern management:
- Vector DB service (Qdrant) for pattern storage and semantic search
- OpenAI API key (required) for semantic pattern matching and vector operations
- See the Pattern Management Guide for complete setup

Installation

DevOps AI Toolkit is designed to be used through AI development tools via MCP (Model Context Protocol). No direct installation needed - simply configure your AI tool to connect to the MCP server.

Usage

🎯 Recommended: Docker Setup (Complete Stack)
Perfect for getting all features working immediately with minimal setup:

1. Download Docker Compose configuration:

curl -o docker-compose-dot-ai.yaml https://raw.githubusercontent.com/vfarcic/dot-ai/main/docker-compose-dot-ai.yaml

2. Set environment variables and create MCP configuration:
```bash