network-mentor-ai

An AI-assisted web application for helping users understand, review, and improve drainage or utility network models through an interactive browser experience. The repository appears to be a modern TypeScript project built from a TanStack Start template and extended with Supabase-backed services, suggesting a hosted application with authentication, storage, or database-driven features. github

Overview

network-mentor-ai appears to be an engineering-focused assistant application designed to act as a “mentor” for network modeling work. The repository structure shows a frontend application in src/, a backend or cloud integration layer in supabase/, and recent activity labeled “Update site info for publish,” which strongly suggests the project is being prepared for deployment as a web app rather than kept as a local-only prototype. github

The repository currently has no README, no description, no topics, no website, and no releases, so this file is intended to give the project a professional foundation and a clear statement of purpose. github

What the app is for

At a high level, this project looks like a browser-based assistant for infrastructure network workflows, especially where users need guidance interpreting models, checking configurations, or navigating engineering decisions. The name network-mentor-ai, the supabase/ directory, and the active site-publishing changes together point to an application that combines a modern user interface with cloud-backed AI or data services. github

A practical product definition for the repo is:

• Help users ask questions about network models in plain language.
• Provide structured engineering guidance or recommendations.
• Surface model concepts, terminology, and workflow hints in a more accessible way.
• Create a reusable web interface for future SWMM- or network-related AI tools.
• Support persistent sessions, user data, or app configuration through Supabase. github

Repository status

The current GitHub landing page provides a clear snapshot of the repo’s maturity and structure. github

• Repository: SWMMEnablement/network-mentor-ai. github
• Visibility: Private. github
• Default branch: main. github
• Commit count shown on the landing page: 21 commits. github
• Latest visible commit: Update site info for publish. github
• Main folders: .lovable, src, supabase. github
• Key files include .env, package.json, bun.lock, bunfig.toml, vite.config.ts, tsconfig.json, eslint.config.js, and components.json. github
• Language mix reported by GitHub: TypeScript 97.6%, CSS 2.0%, JavaScript 0.4%. github
• No description, website, topics, releases, or published packages are currently listed. github

Technology stack

The repo structure points to a modern full-stack web application with a TypeScript frontend and Supabase integration. github

Area	Evidence in repo	Likely role
Frontend language	TypeScript 97.6% github	Main application logic and UI.
Build tool	vite.config.ts github	Local dev server and production build pipeline.
Runtime/package manager	bun.lock, bunfig.toml github	Bun-based install and script execution.
Code quality	eslint.config.js github	Linting and style enforcement.
Formatting	.prettierrc, .prettierignore github	Consistent formatting.
UI/component setup	components.json github	Structured component system.
App source	src/ github	Primary frontend code.
Backend/cloud integration	supabase/ github	Database, auth, functions, or configuration support.
Environment config	.env github	Local secrets and deployment settings.

The presence of TanStack-style template files in the initial history indicates the repo likely started from a tanstack_start_ts scaffold and was then adapted into a domain-specific application. github

Core concept

The strongest README framing for this repo is to position it as an engineering copilot for network modeling rather than a general chatbot. That framing better matches the repository name and the SWMM-focused organization context visible on GitHub. github

A useful description is:

network-mentor-ai is a web application that helps engineers and analysts understand network model structure, ask workflow questions, and receive AI-assisted guidance in a focused interface designed for infrastructure and hydraulic modeling tasks. github

Suggested features

The current repository page does not expose actual src implementation details, so the list below is written as a feature-oriented project definition based on the visible structure and naming. github

Current or intended capabilities

• AI-guided network assistance: A conversational or prompt-driven interface for helping users interpret model structure, concepts, and workflow decisions.
• Engineering-focused UX: A product identity centered on network modeling rather than general-purpose chat.
• Supabase-backed application services: Support for persistent app state, authentication, storage, database records, or server-side workflows through the included supabase/ directory. github
• Browser-based deployment: Recent “publish” changes suggest the app is being prepared for hosted use rather than only local development. github
• Modern TypeScript architecture: Strongly typed code organized for maintainability and future extension. github

Use cases

This repo is well positioned for several practical use cases in water, wastewater, stormwater, or other utility-network workflows:

• Answering terminology and workflow questions for newer modelers.
• Providing guidance on network setup and interpretation.
• Acting as an onboarding assistant for complex modeling environments.
• Offering contextual explanations of nodes, links, assets, and common engineering patterns.
• Serving as the foundation for future model-review or QA assistance tools.

For example, a user could ask how to think about a problematic reach, why a network representation behaves unexpectedly, or what information should be checked before rerunning a simulation.

Project structure

The live repo page shows the following top-level structure. github

network-mentor-ai/
├─ .lovable/             # Lovable project metadata or AI-assisted build scaffolding
├─ src/                  # Main application source code
├─ supabase/             # Supabase configuration, schema, or backend logic
├─ .env                  # Local environment variables
├─ .gitignore
├─ .prettierignore
├─ .prettierrc
├─ bun.lock
├─ bunfig.toml
├─ components.json
├─ eslint.config.js
├─ package.json
├─ tsconfig.json
└─ vite.config.ts

As the codebase becomes more stable, this section should be expanded to show actual internal folders such as:

src/
├─ components/           # Reusable UI components
├─ routes/               # Pages or route handlers
├─ lib/                  # Shared utilities and helpers
├─ features/             # Domain-specific app modules
├─ services/             # API or AI service integration
├─ hooks/                # State and data hooks
└─ styles/               # Styling and theme files

Getting started

The presence of package.json, bun.lock, and bunfig.toml makes Bun the most likely default workflow. github

Prerequisites

• Bun installed locally.
• Git installed.
• Access to any required Supabase project configuration.
• A local .env file with required keys and URLs if they are not already present. github

Installation

git clone https://github.com/SWMMEnablement/network-mentor-ai.git
cd network-mentor-ai
bun install

Start development server

bun run dev

Build for production

bun run build

Preview production build

bun run preview

If the actual script names in package.json differ, revise the commands above to match the repository exactly. github

Environment configuration

The repository includes a tracked .env file, which means environment configuration is already part of the project setup. For a cleaner long-term workflow, it is usually better to keep a .env.example file in version control and reserve .env for local secrets. github

A typical environment section for this repo could look like:

VITE_SUPABASE_URL=your_supabase_url
VITE_SUPABASE_ANON_KEY=your_supabase_anon_key
OPENAI_API_KEY=your_api_key

Only include variables here that are actually used by the application.

Supabase integration

The visible supabase/ folder is the clearest sign that this project is more than a static frontend. In a repo like this, Supabase may be handling one or more of the following: github

• Authentication and user sessions.
• Prompt or chat history persistence.
• Structured storage of engineering content or reference material.
• Edge functions or server-side actions.
• Role-based access for private tools.

This section should be updated once the exact schema or services are known.

Development guidelines

Because this is likely an AI-enabled engineering application, the codebase will be easier to maintain if responsibilities are separated cleanly:

• Keep domain logic independent from UI components.
• Encapsulate AI prompt construction and response parsing in dedicated service layers.
• Store infrastructure-specific definitions and terminology in structured files or typed modules.
• Avoid scattering Supabase queries directly across presentational components.
• Add clear boundaries between frontend state, cloud persistence, and AI orchestration.

That structure will make it much easier to evolve the app from a prototype into a dependable internal tool.

Testing

No test framework is visible on the repository landing page, so this section should be treated as a recommended next step. github

A strong testing plan for this project would include:

• Unit tests for utility and validation logic.
• Component tests for prompt flows and core UI behavior.
• Integration tests for Supabase-backed data operations.
• End-to-end tests for sign-in, question flow, and response handling.
• Regression tests for engineering-specific prompts and expected output patterns.

Deployment

The latest commit message, “Update site info for publish,” indicates the application is being readied for deployment. Once the hosting platform is confirmed, this section should document: github

• Production URL.
• Deployment provider.
• Environment variables required in production.
• Build command and output behavior.
• Release and rollback workflow.

Roadmap

The current repository metadata suggests a project that is actively evolving but not yet documented publicly. Useful next steps include: github

• Add a repository description, topics, and website metadata. github
• Replace inferred feature descriptions with actual app screenshots and workflows.
• Document Supabase setup and schema dependencies.
• Add .env.example and stop tracking sensitive environment values if needed. github
• Add test coverage for key user flows.
• Publish a first tagged release when deployment stabilizes. github

Contributing

Contributions should improve either engineering usefulness, product clarity, or code maintainability. Good pull requests should:

• Focus on one user-facing feature or backend concern at a time.
• Explain the workflow improvement in plain language.
• Include screenshots for UI changes.
• Note any new environment variables or Supabase migrations.
• Update this README when setup or behavior changes.

Metadata suggestions

The repo currently has no description, topics, or website listed on GitHub. A strong starting point would be: github

• Description: AI-assisted web app for mentoring and guiding infrastructure network modeling workflows.
• Topics: ai, typescript, supabase, vite, engineering, swmm, network-modeling, web-app.
• Website: Add the deployed app URL after publication.

License

No license is currently visible on the repository page. Until a license file is added, reuse and redistribution are limited to the repository owner’s default rights. github

A placeholder section can be:

License details will be added once the intended distribution model is finalized.

Acknowledgments

The repository is maintained under the SWMMEnablement organization and shows recent commits from both dickinsonre and lovable-dev[bot], which suggests a workflow combining domain expertise with AI-assisted application development. github

The two biggest upgrades to this README would be adding the actual package.json scripts and documenting what lives inside src/ and supabase/. github