This skill integrates with Surfer SEO’s content optimization API to analyze and improve content for search engine rankings. It creates content audits via POST to /v1/content_editors with target keyword and country parameters, receiving back NLP-based optimization recommendations. The skill compares existing content against Surfer’s guidelines for: word count range (min/max based on top-ranking competitors), heading count and structure (H2, H3 distribution), paragraph count, image count, and bold/strong tag usage. The core optimization loop analyzes semantic term coverage—Surfer provides a list of NLP-identified terms with target frequency ranges, and the skill identifies which terms are missing, underused, or overused in the content. It calculates a content score (0-100) matching Surfer’s scoring methodology based on weighted compliance across all ranking factors. The skill suggests specific insertions: where to add missing terms naturally, which headings to restructure, and where additional paragraphs or images would improve the score. For title tag optimization, it checks character length (50-60 chars), keyword position (front-loaded preferred), and power word inclusion. Meta description optimization targets 150-160 characters with keyword inclusion and call-to-action phrasing. Output includes a before/after content score comparison, itemized recommendations with priority ranking, and an auto-optimized version of the content with changes highlighted.