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

📚 Quick Start | 🔧 MCP Setup | 🛠️ Features & Tools

• 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
• Security Engineers: Define governance policies that integrate into deployment workflows with optional Kyverno enforcement

• DevOps Engineers: Quickly diagnose and fix Kubernetes issues without deep troubleshooting expertise
• SRE Teams: Automate root cause analysis and generate executable remediation commands
• Support Teams: Handle incident response with AI-guided investigation and repair workflows

• Documentation Maintainers: Automatically validate documentation accuracy and catch outdated content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

• 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 Agents: Integrate all capabilities with Claude Code, Cursor, or VS Code for conversational workflows
• Multiple AI Providers: Choose from multiple AI models and providers to optimize for quality, cost, and reliability - see AI Model Configuration
• REST API: Access all tools via standard HTTP endpoints for CI/CD pipelines, automation scripts, and traditional applications

🔍 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: Smart intent clarification gathers missing context, then provides 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

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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 ✨

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🔍 AI-Powered Root Cause Analysis: Multi-step investigation loop identifies the real cause behind Kubernetes failures
🛠️ Executable Remediation: Generates specific kubectl commands with risk assessment and validation
⚡ Dual Execution Modes: Manual approval workflow or automatic execution based on confidence thresholds
🔒 Safety Mechanisms: Automatic fallback to manual mode when validation discovers additional issues
🎯 Cross-Resource Intelligence: Understands how pod issues may require fixes in different resource types (storage, networking, etc.)

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📖 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

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🏛️ 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 (Qdrant) for intelligent pattern matching based on user intent
📋 Best Practices: Share deployment standards across teams through reusable patterns

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🛡️ Policy Creation: Define governance policies that guide users toward compliant configurations
⚠️ Compliance Integration: Policies create required questions with compliance indicators during deployment
🤖 Kyverno Generation: Automatically generates Kyverno ClusterPolicies for active enforcement
🎯 Proactive Governance: Prevents configuration drift by embedding compliance into the recommendation workflow
🔍 Vector Storage: Uses Qdrant Vector DB for semantic policy matching and retrieval

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🎯 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

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⚡ 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 🎯 Multiple AI Providers: Choose from multiple AI models and providers to optimize for quality, cost, and reliability - see AI Model Configuration

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

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

• MCP Setup Guide - Complete configuration instructions for AI tools integration
• Tools and Features Overview - Comprehensive guide to all available tools and features

MCP server won't start:

• Verify environment variables are correctly configured in .mcp.json env section
• Check session directory exists and is writable
• Ensure ANTHROPIC_API_KEY is valid

"No active cluster" errors:

• Verify kubectl connectivity: kubectl cluster-info
• Check KUBECONFIG path in environment variables
• Test cluster access: kubectl get nodes

• Issues: GitHub Issues

We welcome contributions! Please:

• Fork the repository and create a feature branch
• Run integration tests to ensure changes work correctly (see Integration Testing Guide)
• Follow existing code style and conventions
• Submit a pull request with a clear description of changes

MIT License - see LICENSE file for details.

DevOps AI Toolkit - AI-powered development productivity platform for enhanced software development workflows.