Build SEO Agent Skills as Workspaces

The article explains why single‑prompt AI SEO skills fail and outlines a systematic approach to building reliable SEO agent skills. It identifies three core problems: lack of tools to actually check a website, no verification of output, and no memory for consistency. The author then describes a workspace architecture that includes an instruction manual (AGENTS.md), scripts (e.g., crawl_site.js), reference criteria, memory logs, and output templates. The crawler evolution is detailed through five versions: raw curl (blocked), Playwright script (crashed on large sites), rate limiting (failed on JS sites), browser rendering (inconsistent output), and finally templates plus memory (stable, consistent, reliable). The final version incorporates rate limiting, resume capability, JavaScript rendering detection, and structured output. The article emphasizes that consistent output and institutional knowledge are achieved through templates and memory logs.

This guide serves as a practical blueprint for agencies to build reproducible, auditable SEO audit agents that can crawl sites, parse sitemaps, and report issues reliably.

Overall, the piece demonstrates how to move from ad‑hoc prompts to a fully engineered agent workspace that ensures consistency and reliability.