ProdPilot

Takes a web app you built with an AI coding assistant from "it works on my machine" to a live, verified deployment, without leaving your IDE.

AI coding assistants make it quick to build a Node.js API or a React app that runs on your laptop. Getting it to run in production is where it breaks: credentials hardcoded in the source, no security headers, no container setup, no health check, and a deploy that fails with an error you have never seen.

ProdPilot finds those gaps, has your IDE's own coding agent fix them one at a time, checks every fix itself, and deploys the project to Render with a CI/CD pipeline once it is ready. It works as a Model Context Protocol (MCP) server: a small program on your machine that the agent in VS Code with GitHub Copilot, Cursor or Devin can call as a set of tools. ProdPilot calls no AI service itself.

It is for developers building Node.js with Express APIs or React with Vite apps who want them deployed properly, not just deployed.

Features

• A production audit of 50 rules across security, secrets, environment, build, connectivity, API, structure, observability and git hygiene, scored from 0 to 100.
• Fixes your agent applies and ProdPilot verifies. The agent reports a fix; ProdPilot re-runs that rule's own check on your files and decides. A fix that breaks something that already worked is undone.
• Predictable changes. 44 of the 50 rules come with an exact change for the agent to apply; only 6 ask it to write code, inside limits ProdPilot sets.
• A gate that asks whether it will really deploy. A score of at least 90, no critical failures, and an estimate from a model trained on 684 real Render deployments.
• Deployment in one call. Nine stages, from a local Docker build to a live, smoke tested Render service with a GitHub Actions pipeline. It stops at the first stage that fails and tells you why.
• Your credentials stay yours. They live in a file only your account can read, never in the project, and repository secrets are encrypted before they leave your machine.
• Works in the IDE you already use. One command connects VS Code, Cursor or Devin, formerly Windsurf.

Quick start

You need Python 3.11 to 3.14, Node.js 20 or later, Docker running, git, a GitHub token and a Render API key.

pipx install prodpilot          # 1. install
prodpilot setup                 # 2. store your GitHub token and Render API key
prodpilot connect vscode        # 3. connect your IDE: vscode, cursor, or devin --project PATH

1.0.0rc1 is not on PyPI yet. Until it is, install from a clone of this repository with pip install . in place of step 1.

Then check that everything ProdPilot needs is in place:

prodpilot doctor

ProdPilot doctor

Config file: ~/.prodpilot/config.toml
  found
  github_token: stored
  render_api_key: stored
  permissions: access is limited to the current user

Tools ProdPilot runs:
  git: git version 2.54.0.windows.1
  Node.js: v26.3.0
  Docker: daemon 29.7.2

All checks passed.

In VS Code, run MCP: List Servers and start prodpilot. Open your project and ask the agent:

Use ProdPilot to audit this project and fix what it reports, one rule at a time.

Installation

Check the install with prodpilot --help, which lists serve, setup, doctor and connect.

ProdPilot is developed and tested on Windows 11, and the repository's test workflow is configured for Linux and macOS.

Usage

Audit and fix

Use ProdPilot to audit this project and fix what it reports, one rule at a time, reporting each fix with prodpilot_fix_applied.

The agent asks ProdPilot for the exact change each failing rule needs, applies it, and reports back. ProdPilot re-runs the rule's check and answers resolved, unresolved or blocked, whatever the agent claimed. On one of the deliberately broken sample projects in this repository, the first audit scores 13 out of 100 with 6 critical failures; after the fix loop it scores 96 with none.

Deploy

Commit your changes, then:

Deploy this project with prodpilot_deploy.

The deploy creates a real Render service on the free plan and pushes to your GitHub repository, so the agent is told to confirm with you first. You get a live URL and a GitHub Actions workflow that redeploys on every push and checks the service afterwards. A small Express API that the fix loop took from 69 to 100 went live on Render this way, all nine stages passing, in 82 seconds.

When something is refused

ProdPilot always says which condition failed. These are real messages:

score 89 but 1 critical rule(s) still fail: SEC-003
score 99 meets the threshold, but the model estimates a 5% chance of deploying, below its operating point of 37%
build failed, missing dependency: npm error The `npm ci` command can only install with an existing package-lock.json or

Tools and commands

The five tools your IDE's agent sees:

The commands you run:

How it works

flowchart LR
    A[Your project] --> B[Audit: 50 rules, score 0 to 100]
    B --> C[Fix loop: your agent applies, ProdPilot verifies]
    C --> B
    C --> D{Gate: score 90 or more, no critical failure, model estimate}
    D -- refused --> C
    D -- cleared --> E[Deploy: 9 stages]
    E --> F[Live on Render, with CI/CD]

Each rule has its own check, and a fix only counts when that check passes on the files on disk. Each rule gets three attempts, and the loop runs up to five times while the gate stays shut. The deploy stages are pre-flight checks, environment sealing, a local Docker build and run, a push of the generated files, the Render deployment, deploy monitoring, a smoke test of the live service, and CI/CD wiring. The design, the model and the data behind it are in docs/architecture.md.

Privacy and security

• ProdPilot talks only to GitHub, Render, and, while building your project, Docker Hub and the npm registry. It sends no telemetry.
• Your GitHub token and Render API key are stored outside every project, in a file restricted to your account.
• Secrets go to the test container at run time, never into the image, and to GitHub encrypted with your repository's public key.

Report a vulnerability privately as described in SECURITY.md.

Limitations

• Two stacks only: Node.js with Express, and React with Vite.
• One deployment target: Render, on its free plan.
• For the 6 rules where the agent writes the change, how often a live agent's change passes on the first try has not been measured yet.
• A project whose code requires a file it does not contain can pass the audit; the Docker build during the deploy catches it and names the missing module.
• Render's own automatic deploy stays on, so a push can start a second deploy beside the workflow's; the workflow waits for the newer one.
• This is a beta. Not yet observed on real services: a deploy started by an IDE agent, the generated workflow running on GitHub Actions, and prodpilot connect inside Cursor and Devin.

What's new in 1.0.0rc1

• The deployability model ships inside the package.
• prodpilot connect sets up VS Code, Cursor and Devin in one command.
• prodpilot doctor checks git, Node.js and Docker.
• The generated CI/CD workflow waits for its own deploy before checking the service.
• Runs on Python 3.11 to 3.14.

The full list is in CHANGELOG.md.

Documentation

• Quick start and troubleshooting
• Architecture: the audit, the fix loop, the gate, the model and the data
• IDE compatibility, observed in each client
• The whole chain run on every sample project
• Measured fix reliability
• The deployability model's evaluation

Contributing

git clone https://github.com/prodpilotai/ProdPilot
cd ProdPilot
python -m venv .venv
.venv/bin/pip install -e . --group dev     # on Windows: .venv\Scripts\pip; needs pip 25.1 or later
.venv/bin/python -m pytest

The tests reach no live service: Render and GitHub are scripted. Tests that build real Docker images are skipped when no Docker daemon is running. Please open an issue before a large change, and run the suite before sending a pull request.

Questions and bug reports go to GitHub issues.

License

MIT, see LICENSE. Bundled third-party code is listed in THIRD_PARTY_NOTICES.md.

Credits

Built by Muhammad Sudais Khalid, Muhammad Farooq Khan and Muhammad Talha Khan as the Final Year Project BSAI-FYP-2026 of the Department of Artificial Intelligence, Shifa Tameer-e-Millat University, at the Artificial Intelligence Technology Centre, National Centre for Physics, Islamabad. Supervised by Mr. Rehan Naveed Abbasi, with industrial supervision by Dr. Rana Fayyaz Ahmad and Muhammad Junaid Asif.