If you have ever applied to a role and thought, “My experience fits, but my resume is not telling the story the way the job post expects,” this mini-app is for you.

In this walkthrough, we will build a self-service ATS verifier using Google Opal. You upload two documents: the job post and your resume. The app returns three things you can act on immediately: an alignment score, concrete recommendations to improve your resume, and a short list of critical missing skills to decide whether to invest more time.

Opal is a Google Labs tool for building “mini-apps” that chain prompts, model calls, and tools together using a visual editor. You can describe what you want in plain language, then tweak the workflow one step at a time in the graph view.

·        No code. You build with natural language plus a visual editor.

·        Instant hosting. Opal publishes a shareable app link without you managing servers.

·        Fast iteration. When the output is off, you edit a prompt, re-run a step, and keep moving.

One important nuance: Opal is built to host the mini-app for you. That is the feature. If you truly need to run this within your own network or VPC, Opal remains a great “prototype factory,” but you will re-implement the final version elsewhere.

Before you build, make sure you have the following ready:

·        A Google account and access to Opal in your country (the editor is optimized for desktop).

·        A modern desktop browser.

·        Two documents you can upload: a job post and a resume.

·        A quick decision on privacy: do not upload confidential resumes or internal job posts you would not want stored in Drive.

Data handling matters. Opal’s FAQ notes that Google does not use your Opal prompts or generated output to train its generative AI models, and also points out that Opals live as files in your Google Drive. Treat that like any other cloud document and set sharing permissions accordingly.