seo-drift

You track SEO drift by capturing a baseline of on-page elements for a URL, then comparing future crawls against that stored state to detect changes. This allows you to monitor and flag shifts in key signals across pages over time.

Automating on-page SEO audits for drift involves using Python scripts to collect and parse page data, applying rule-based scoring to compare current pages against baselines, and storing the history in SQLite to enable repeatable monitoring.

Yes, SQLite provides local storage for baselines and comparisons, enabling repeatable audits. It stores the historical state of SEO-critical elements, allowing you to roll back and compare URL data across different time periods.

Rule-based comparison rules provide structured signals for remediation by evaluating 17 cross-skill criteria. They flag specific on-page and technical SEO drift, helping you pinpoint exactly which elements changed for targeted cross-skill actions.

No external dependencies are required to detect SEO drift. The process handles end-to-end data collection, parsing, comparison logic, and storage entirely using Python scripts and a local SQLite database.

You establish a new SEO baseline for a URL to capture a known good state before making updates. This baseline serves as the reference point for future comparisons to accurately surface any subsequent drift.