How to use Cursor to prototype a dependency checker

The shouldiuse.dev team describes its first professional experiment with heavily LLM-assisted coding. The goal was to make dependency-health information accessible through a website built around OpenSSF Scorecard, which the team already used.

Three people independently created frontend prototypes using v0, Lovable and Cursor. Although the prototypes appeared quickly, combining their ideas proved difficult because they used different frameworks and versions. The team reports that frontend detail work and small adaptations consumed the most effort.

In parallel, they wrote a Go backend using the Scorecard library and deliberately experimented with Copilot, different models and prompts. They added community metrics from the GitHub API and generated textual summaries with OpenAI. The recommendation prompt combined role and format instructions, Scorecard results, community data and the questions shown in the website’s FAQ. Results were stored in PostgreSQL for subsequent retrieval.

The team also added an authenticated remote MCP server so coding assistants could access dependency checks from an IDE. At publication, the service supported public GitHub repositories and required a personal access token when requesting checks.

Who it's for: Teams exploring AI-assisted prototypes who can reconcile divergent implementations and evaluate dependency evidence themselves.

Prompt
Suggested prompt (written by Agents In Use, not the author's)

Source: Hacker News · louis_w_gk