DevOps AI Toolkit

# DevOps AI Toolkit <div align="center">  </div> DevOps AI Toolkit is an AI-powered development productivity platform that enhances software development workflows through intelligent automation and AI-driven assistance. 📚 [Quick Start](./docs/quick-start.md) | 🔧 [MCP Setup](./docs/mcp-setup.md) | 🛠️ [Features & Tools](./docs/mcp-tools-overview.md) ## 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 - **Security Engineers**: Define governance policies that integrate into deployment workflows with optional Kyverno enforcement ### Kubernetes Issue Remediation - **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 <!-- ### Platform Building DEVELOPER NOTE: This tool is under active development with incomplete functionality. Not recommended for production use. - **Platform Engineers**: Install and configure platform tools conversationally without memorizing script paths and commands - **New Team Members**: Build platform infrastructure through zero-knowledge guided workflows - **DevOps Teams**: Create and manage Kubernetes clusters through natural language interactions --> ### Documentation Testing - **Documentation Maintainers**: Automatically validate documentation accuracy and catch outdated content - **Technical Writers**: Identify which sections need updates and prioritize work effectively - **Open Source Maintainers**: Ensure documentation works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 - **REST API**: Access all tools via standard HTTP endpoints for CI/CD pipelines, automation scripts, and traditional applications ## 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**: 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 📖 [Learn more →](./docs/mcp-recommendation-guide.md) #### 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 ✨ ``` 📖 [Learn more →](./docs/mcp-capability-management-guide.md) ### Kubernetes Issue Remediation 🔍 **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.) 📖 [Learn more →](./docs/mcp-remediate-guide.md) <!-- ### Platform Building DEVELOPER NOTE: This tool is under active development with incomplete functionality. Not recommended for production use. 🗣️ **Natural Language Operations**: Install tools and create clusters through conversation without memorizing commands 🔍 **Dynamic Discovery**: Automatically discovers 21+ available platform operations from infrastructure scripts 🤖 **AI-Powered Intent Mapping**: Understands variations like "Install Argo CD", "Set up ArgoCD", "Deploy Argo CD" 💬 **Conversational Configuration**: Guides through parameter collection step-by-step with sensible defaults 🎯 **Zero-Knowledge Onboarding**: New users successfully build platforms without documentation 📖 [Learn more →](./docs/mcp-build-platform-guide.md) --> ### 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 📖 [Learn more →](./docs/mcp-documentation-testing-guide.md) ### 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 (Qdrant) for intelligent pattern matching based on user intent 📋 **Best Practices**: Share deployment standards across teams through reusable patterns 📖 [Learn more →](./docs/pattern-management-guide.md) ### Policy Management & Governance 🛡️ **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 📖 [Learn more →](./docs/policy-management-guide.md) ### 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 📖 [Learn more →](./docs/mcp-prompts-guide.md) ### 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](./docs/mcp-setup.md) for complete configuration instructions. --- 🚀 **Ready to deploy?** Jump to the [Quick Start](./docs/quick-start.md) guide to begin using DevOps AI Toolkit. --- ## See It In Action [](https://youtu.be/8Yzn-9qQpQI) This video explains the platform engineering problem and demonstrates the Kubernetes deployment recommendation workflow from intent to running applications. ## Documentation ### 🚀 Getting Started - **[MCP Setup Guide](docs/mcp-setup.md)** - Complete configuration instructions for AI tools integration - **[Tools and Features Overview](docs/mcp-tools-overview.md)** - Comprehensive guide to all available tools and features ## Troubleshooting ### MCP Issues **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` ## Support - **Issues**: [GitHub Issues](https://github.com/vfarcic/dot-ai/issues) ## Contributing We welcome contributions! Please: - Fork the repository and create a feature branch - Run integration tests to ensure changes work correctly (see [Integration Testing Guide](docs/integration-testing-guide.md)) - Follow existing code style and conventions - Submit a pull request with a clear description of changes ## License MIT License - see [LICENSE](LICENSE) file for details. --- **DevOps AI Toolkit** - AI-powered development productivity platform for enhanced software development workflows.