The proposed SEO audit method relies on a graph-based approach to verify redirects, canonical tags, and hreflang attributes. By treating site links as nodes and edges, developers can identify broken connections, redirect loops, and missing return links that standard crawlers might overlook.
The system requires three components: a snapshot of HTTP observations, a set of deterministic rules, and a list of violations. The audit script processes a JSON array containing URL data, status codes, and link annotations, performing checks without external dependencies or live HTTP requests.
The implementation uses a Python script that normalizes URLs and traverses link chains. It detects issues such as redirect loops, unreachable targets, and missing hreflang backlinks. The tool is designed to be integrated into CI/CD pipelines to prevent regressions before deployment.
For large-scale audits, the author suggests caching final nodes and using strongly connected component searches to optimize performance. The audit should focus on critical templates like homepages, categories, and product cards rather than blocking all warnings during initial runs.
