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Getting StartedFor AI-First Builders

Getting Started for AI-First Builders

Dot•requirements gives your AI assistant structured context about what you’re building, so it writes better code and tests that actually validate the right things.

What You’ll Get

  • AI gets it right the first time — clear requirements mean fewer rewrites and less debugging imprecise assumptions
  • Day-two features have a foundation — your AI assistant can read what came before
  • Tests stay honest — linking tests to requirements means you can trust they’re validating the behaviour you asked for
  • Build-first is fine — prototype first, capture requirements after; you get the same strong foundation either way

Set Up

One command:

npx @popoverai/dotrequirements init

This will:

  • Create or connect to a project
  • Configure your AI assistant to use dot•requirements

A cloud account is optional, but it enables style checking, test review, and coverage sync — the features that make AI-assisted development actually work well.

Supported assistants:

  • Claude Code
  • Cursor
  • OpenAI Codex
  • GitHub Copilot
  • Google Antigravity
  • Claude Desktop

After setup, restart your AI assistant for changes to take effect.

Optional: To get the dotreq shorthand for future use, install globally:

npm install -g @popoverai/dotrequirements

The Workflow

Once MCP is configured, your AI assistant can use dot•requirements tools. Here’s how a typical feature development looks:

You describe what you want to build

“Add a login page with email and password. Users should see an error if credentials are wrong, and get redirected to the dashboard on success.”

AI captures requirements

Your assistant will ask: “Should we capture what this should do before we build it?”

If you say yes, it:

  1. Creates a requirements document with clear, testable statements
  2. Runs style_check to catch clarity issues
  3. Shows you the requirements for review

You review and approve

Check that the requirements capture your intent. Adjust if needed. The AI pushes approved requirements to the cloud with push_requirements.

AI implements and tests

Your assistant writes the code, then writes tests that reference the requirements.

After writing tests, the AI runs review_test to verify tests actually validate what the requirements specify — not just that they pass.

You accept the result

Run the tests, verify the implementation works, and move on. Coverage is tracked automatically.

Next Steps

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