How to vibe codeVerifiedDR

Prerequisites

• Node.jsfree

  Runtime environment for Next.js app, CLI utility, and MCP server

• GitHubfree

  Code hosting and deployment pipeline integration

• 

  OpenAI API Keypay-as-you-go (~$10/mo)

  Required for automated AI visibility prompt execution and analysis

AI coding tools

• Claude Code

  Autonomous terminal coding agent capable of scaffolding the full-stack Next.js app, database schema, and MCP server

• Cursor

  IDE for inspecting complex dashboard UI components and fine-tuning Tailwind styling

Build guide

1. 01Project Scaffolding and Database Schema

   Initialize a Next.js project with Tailwind CSS, configure Turso SQLite connection, and set up the core database tables for domains, TrueDR scores, AI prompts, and visibility actions.

   Create a new Next.js project using TypeScript and Tailwind CSS. Configure a Turso database client using `@libsql/client` with schema definitions for `websites` (id, url, classic_dr, true_dr, traffic, owner_verified, created_at), `ai_prompts` (id, website_id, prompt_text, target_country, last_checked), `citations` (id, prompt_id, engine, cited, source_urls, timestamp), and `visibility_actions` (id, website_id, title, impact, effort, completed, created_at). Set up clean directory structures for server actions and API routes.

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2. 02TrueDR Scoring Logic and Domain Inspector

   Implement the core TrueDR calculation engine that aggregates backlink profile breadth, spam patterns, and traffic validation into a trust-adjusted score.

   Build a TypeScript service function that calculates 'TrueDR' for a given domain URL. The function must accept raw metrics (classic DR, referring domains count, follow-link ratio, monthly organic traffic, spam backlink count, website age in days, and owner verification status). Implement a deterministic penalty formula that deducts points for high spam ratios and unvalidated traffic, returning an adjusted integer score from 0 to 100 alongside a per-signal breakdown object. Create a dashboard page component that inputs a URL, calls this server action, and displays a clean visual breakdown card comparing classic DR to TrueDR.

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3. 03AI Visibility Prompt Engine

   Implement automated daily checks against AI search and answer engines (ChatGPT, Perplexity) to verify brand citations and source URLs.

   Create an API route and background runner that executes tracked buyer-intent prompts across OpenAI (ChatGPT) and Perplexity Sonar API. Each prompt run must pass geographic target parameters where supported, query the LLM for brand recommendations, and parse the returned response and citation URLs to detect whether the target domain is named. Store the result in the `citations` table. Build a corresponding dashboard view showing a 12-week citation progression graph and missing source URL aggregation charts (e.g., tracking which third-party review sites appear when your brand is skipped).

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4. 04Visibility Growth Action Loop

   Build an automated diagnostics engine that aggregates scores, indexing gaps, and citation misses into a prioritized list of actionable growth tasks.

   Implement a recommendation engine in `lib/actions.ts` that inspects a website's current TrueDR gap, unindexed pages, keyword standing, and AI citation misses. Generate a ranked list of 'Visibility Growth' items scored by expected impact, confidence, and effort. Build a React dashboard component that lists these prioritized tasks with interactive action triggers (e.g., drafting partner outreach emails or previewing indexing fix guides).

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5. 05Partner Marketplace and Directory

   Create a discovery marketplace matching complementary websites by category, traffic, and TrueDR for safe co-marketing and backlink collaborations.

   Build a peer marketplace interface where verified website owners can browse complementary categories, view TrueDR and monthly traffic metrics, and initiate outreach requests without exposing direct emails. Implement secure message threads and partnership status tracking tables in Turso, ensuring contact details remain private until requests are accepted.

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6. 06Model Context Protocol (MCP) Server and CLI Utility

   Expose core metrics and visibility growth tasks via an MCP server configuration and a local npm CLI (`vdr`) so AI coding agents can fetch insights.

   Build a Model Context Protocol (MCP) server using the official SDK that exposes tools for fetching a website's TrueDR score, running AI visibility checks, and listing prioritized growth actions. Also create a lightweight CLI script (`bin/vdr.ts`) packaged for local execution that reads configuration files and prints JSON metrics directly to the terminal for Claude Code and Cursor integrations.

   ⧉copy promptopen in Claude CodeCopen in Codexopen in Cursor

Cost vs paying for VerifiedDR