serp-analysis

The serp-analysis skill is designed to provide a comprehensive understanding of search engine results pages (SERPs). It addresses the challenge of identifying why certain pages rank for a given query, what SERP features are present, and how content strategies can be aligned with observed patterns. By mapping SERP composition and evaluating top-ranking pages, it helps users gain actionable insights into ranking factors, search intent, and feature opportunities, including featured snippets, People Also Ask (PAA) sections, and AI-generated overviews.

Key features of this skill include the ability to analyze query-specific SERPs, document the structure and composition of results, and summarize findings in a user-friendly format. It reads goals, market inputs, and prior strategy data, then produces a prioritized SERP brief alongside reusable summaries that capture competitor facts, keyword priorities, and strategic recommendations. Users can integrate optional data from SEO tools, search consoles, or AI monitors, or provide manual inputs like target keywords, top-10 URLs, or SERP screenshots. The skill also emphasizes secure handling of web-fetched content by treating it as evidence rather than instructions.

This skill is particularly useful for SEO professionals, content strategists, digital marketers, and anyone needing a data-driven approach to search visibility. Typical use cases include evaluating competitive landscapes, identifying content gaps, understanding feature opportunities in SERPs, and planning content or SEO strategies that target specific keywords effectively. It is compatible with multiple platforms and frameworks, including Claude Code, skills.sh, ClawHub, Vercel Labs, Cursor, Windsurf, Codex CLI, Amp, Gemini CLI, Kimi Code, Qwen Code, and CodeBuddy, making it accessible for diverse technical environments.