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You are an expert Vite Plugin Architect and Senior Frontend Infrastructure Engineer. You specialize in extending Vite’s internal capabilities, managing complex build pipelines, and optimizing developer experience (DX) through custom server middleware and virtual module resolution.
We are developing a sophisticated PROJECT_TYPE that requires deep integration with the Vite 5 lifecycle. The goal is to create a robust, production-ready Vite plugin that orchestrates custom file transformations, manages virtual assets, and maintains a seamless Hot Module Replacement (HMR) flow. The implementation must adhere to Vite’s plugin container architecture and TypeScript best practices.
Design and implement a custom Vite 5 plugin that satisfies the following requirements:
transform hook to handle FILE_EXTENSION files, ensuring they are compiled into valid JavaScript/TypeScript.resolveId and load to inject dynamic configuration objects (e.g., virtual:plugin-config) into the app.configureServer to add custom API routes (e.g., [SERVER_ROUTE]) that interact with the dev server.handleHotUpdate hook and the server.ws API to propagate granular updates for WATCHED_TARGETS.renderChunk or generateBundle hooks for custom asset post-processing and minification.optimizeDeps to prevent unnecessary re-bundling of external dependencies.fs.watch or chokidar instance within the plugin lifecycle to sync EXTERNAL_TOOL_NAME changes with the Vite dev server.Plugin type from 'vite'. Ensure proper JSDoc comments for exported functions.vite.config.ts snippet demonstrating how to initialize the plugin with CONFIG_OPTIONS.A proven free prompt for Vite 5 plugin custom dev server is: "Develop a custom Vite 5 plugin for specialized workflow. Plugin features: 1. Transform hook for custom file types. 2. Virtual modules for config injection. 3. HMR API for hot updates. 4. Dev server mi..." — You can copy it for free on PromptsVault AI and paste it directly into ChatGPT, Claude, or Gemini.
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This prompt works with all major AI tools — ChatGPT (GPT-4o), Claude 3 (Anthropic), Google Gemini, Grok (xAI), Microsoft Copilot, Perplexity, Mistral, and Llama. The prompt is written in plain language so it's compatible with any large language model.