Purpose

The fetched data is normalized into a consistent schema. It then prioritizes threats based on severity & exploit-like patterns (with a score), writes JSON outputs, and displays the results on a dashboard.

How it works

• fetch_intel.py pulls and normalizes CVE data
• Threats are tagged with patterns like rce, auth_bypass, path_traversal, poc, and in_the_wild
• A ranked prioritized_threats.json feed powers the dashboard
• delta.json tracks what changed between runs
• telegram_alert.py turns the current snapshot into a readable hourly alert

Why I built this

This is my first project with OpenClaw and i wanted to build an autonomous pipeline using python, showcasing results on a live front-end & outbound alerts of the latest update via Telegram.

Scope

Claw Glancer is a prototype. The next potential upgrades could be exploit-evidence related reference, correlation, asset relevance and/or customized alerts.