PromptVault is a curated, structured library of AI prompts built for software engineers. It lets users browse by category, fill in prompt variables, generate ready-to-paste prompts, and save favorites for quick access.
- Structured prompt library with categories like DSA, debugging, system design, SQL, web dev, and interviews.
- Prompt builder that fills placeholders and adds contextual preambles (Learning, Interview, Production).
- Detailed vs concise prompt variants with a copy-ready output.
- Favorites stored locally for quick recall.
- Fast client-side search and category filtering.
- React 19 + TypeScript
- TanStack Router + TanStack Start
- Vite
- Tailwind CSS (v4) + Radix UI primitives
- Cloudflare compatibility via wrangler config
- Node.js (modern LTS recommended)
- Bun (recommended) or npm/pnpm/yarn
bun installbun run devbun run buildbun run previewbun run lint
bun run formatsrc/
components/ # App UI and prompt cards
components/ui/ # Reusable UI primitives (Radix + Tailwind)
data/prompts.ts # Prompt catalog, templates, and categories
hooks/ # Theme and favorites persistence
routes/ # Route components (library, favorites, prompt detail)
router.tsx # Router configuration
styles.css # Tailwind theme and global styles
Browse all prompts, filter by category, or search by title/description. Each prompt has a description and a one-click copy action.
Each prompt can be customized with user inputs, a context mode, and detail level. The UI highlights filled placeholders and generates the final copyable prompt.
Favorites are stored in local storage, so they persist across sessions without a backend.
- Global metadata and styles are set in the root route.
- Prompt data lives in
src/data/prompts.tsand can be extended with new categories, templates, or prompt chains. - Theme toggling is handled client-side via
localStorage.
This project is compatible with Cloudflare Workers (see wrangler.jsonc). If you plan to deploy elsewhere, use the standard Vite build output and adjust hosting as needed.
Initial UI scaffolding and rapid prototyping were assisted using Lovable. All prompt design, system logic, feature structuring, and refinements were implemented and customized independently.