Quickstart

This tutorial gets your first scored pageview into the dashboard. You create a project, paste the beacon into your page, load it in a browser, and watch the observation land in Logs. It takes a few minutes and needs only a publishable key.

By the end you will have a live observation with a score, a decision, and a user-agent classification.

1. Create a project and a publishable key

   Open the dashboard and create a project for the site you want to track. In the project’s Settings, create a publishable key. It looks like fs_pub_live_… and is safe to expose in the browser.

   Copy the full key — you paste it into the beacon next.

2. Paste the beacon into your page

   Add this <script> tag to your page’s HTML, just before </body>. Replace fs_pub_live_… with your key.

   <script
     async
     src="https://api.formshield.dev/js/formshield.js"
     data-fs-project-key="fs_pub_live_…"
     data-fs-action="pageview"
     data-fs-mode="pageload"
   ></script>

   data-fs-mode="pageload" records exactly one scored pageview on load and does no form handling. The tag is async and auto-initializes — there is no other code to write.

3. Load your page

   Open the page in a browser. On window load the beacon collects browser and automation signals and posts one page.view observation to FormShield. There is no visible UI and nothing blocks rendering.

   A real human visit scores low and resolves to allow.

4. Watch it land in the dashboard

   Open the project’s Logs in the dashboard. FormShield scores the pageview instantly at the edge; the observation appears in Logs within a few minutes, once events sync from the edge buffer. It carries a score, a decision (allow for a normal browser), the reasons that fired, and a user-agent classification (browser, OS, device).

   Click the observation to see the full IP profile and signal breakdown.

What you just recorded

The observation carries the scoring result FormShield computed at the edge.

A visit from an automation tool tells a different story — webdriver automation pushes the score past the block threshold:

{
  "score": 0.93,
  "decision": "block",
  "reasons": ["automation_detected", "ua_bot", "bot:automation"]
}

Next steps