This repository is a VS Code extension that integrates Draw.io (diagrams.net) into the Visual Studio Code editor. It enables users to create, edit, and view Draw.io diagrams directly within VS Code, supporting standalone files (.drawio, .dio) and inline diagrams embedded in Markdown via fenced code blocks or HTML comments. The extension provides offline functionality by bundling a version of Draw.io, with optional online fallback, and includes features like Live Share collaboration, code linking from diagram symbols to source code, diagram export/conversion, and theme synchronization. The architecture follows standard VS Code extension patterns with a main entry point (src/index.ts), extension core (src/Extension.ts), editor service (src/DrawioEditorService.ts), Draw.io client for iframe communication (src/DrawioClient/), and specialized features for Live Share, code linking, and inline Markdown editing. Communication between the extension and the embedded Draw.io iframe occurs via postMessage API, with state management using Mobx and resource cleanup via disposable patterns. The extension handles diagram persistence through file system operations and metadata embedding in SVG/PNG files. Key reusable components include a configuration abstraction utility (VsCodeSetting.ts), a Mobx autorun disposable tracker (autorunTrackDisposables.ts), an event-driven Draw.io client for iframe communication (DrawioClient.ts), a Markdown diagram parser (diagramParser.ts), and a Live Share session manager (LiveshareSession.ts). These components demonstrate loose coupling, clear separation of concerns, and patterns applicable beyond this specific extension. The project is actively maintained with automated workflows for updating the bundled Draw.io submodule, indicating low technical drift and strong ongoing maintenance.
This repository implements an AI-powered diagram generation and editing application that integrates draw.io with large language models. Users describe diagrams in natural language, and the system generates, edits, and validates draw.io XML diagrams through a chat interface. The architecture is a Next.js 16 web application with App Router, supporting multiple AI providers (OpenAI, Anthropic, Google, AWS Bedrock, DeepSeek, Ollama, etc.) via the Vercel AI SDK. It includes a desktop Electron app with offline draw.io support, an MCP server for external AI agent integration (Claude, Cursor, VS Code), and an admin panel for runtime configuration of models, access controls, and observability (Langfuse). The application is self-hostable via Docker and deployable to Vercel or Cloudflare Workers. The core workflow involves a chat interface where users send prompts, optionally upload files (images, PDF, text) or URLs, and the AI responds with tool calls to display, edit, or append diagrams. The system handles streaming XML generation, debounced diagram updates, and automatic XML validation/fixing to correct common LLM output issues. It supports multi-page diagrams, shape libraries (AWS, Azure, GCP, etc.), and experimental VLM-based diagram validation. Session history, templates, and diagram versions are persisted in IndexedDB, with optional server-side quota management via DynamoDB. The codebase demonstrates strong engineering practices with comprehensive unit and E2E tests, CI/CD pipelines, multi-platform builds (web, desktop, MCP server), and i18n support for English, Chinese, and Japanese. It features reusable patterns such as a registry-driven admin settings system, DNS-based SSRF protection, and a standalone XML validation library. While the project is actively maintained and uses modern frameworks, its large dependency surface and complex configuration precedence (env vars, admin panel, file-based) introduce some maintenance overhead.
drawio-skill is a comprehensive toolkit for generating, manipulating, validating, and publishing draw.io architecture diagrams from natural language, codebases, infrastructure-as-code, API specifications, and other structured sources. It provides a unified CLI (`diagramctl.py`) and an optional MCP server (`diagramctl_mcp.py`) that expose workflows for building, syncing, viewing, testing, reviewing, querying, what-if analysis, story generation, and publishing diagrams. The system uses a formal Diagram Intermediate Representation (IR) to separate semantic meaning from geometry, enabling incremental reconciliation, multi-view projection, architecture contract enforcement, and accessible HTML story output. It includes 15+ importers for languages (Python, JS, Go, Rust), IaC (Terraform, Kubernetes, Docker Compose), schemas (SQL, OpenAPI, AsyncAPI, Protobuf, GraphQL), and live infrastructure (Docker, Terraform state). The project is packaged as an AI agent skill compatible with Claude Code, Cursor, Copilot, and other Agent Skills hosts, and ships with GitHub Actions for CI integration. While primarily an application, it also bundles curated icon indices and style presets that could be considered data assets.
This repository is a curated collection of reusable draw.io shape libraries, editable diagram examples, and a Docusaurus-based guideline website for creating consistent SAP Business Technology Platform (BTP) solution architecture diagrams. It provides standardized icons for SAP BTP services (organized by category and size), connectors, area shapes, text elements, brand logos, and pre-built diagram templates (e.g., SAP Task Center, SAP Cloud Identity Services, SAP Private Link Service). The guideline website documents design principles, atomic components, and best practices for diagram creation. The repository is not a runnable application but a design asset library with a static site for documentation. It serves SAP solution architects and technical writers who need to produce professional, on-brand architecture diagrams.
This repository serves as a comprehensive guide and utility for managing Visual Studio Code extensions. It provides a curated list of extensions categorized by functionality (themes, productivity, data science, etc.) with descriptions and screenshots. The repository includes a PowerShell script for bulk installing extensions and a set of tools (Python and Bash) for comparing extension lists between different environments. Additionally, it contains a standalone decision tree implementation in Python, which, while not directly related to the main purpose, demonstrates solid algorithmic implementation. The main value lies in the extension recommendations and the practical scripts for managing VS Code setups, making it a useful reference for developers seeking to enhance their coding environment.