toprank

I'd like to build a Python-based Claude Code skill to empower an AI agent with advanced SEO and SEM audit capabilities, specifically integrating with Google Ads and Google Search Console. The goal is to create a tool that can analyze performance data, identify critical issues, and provide precise, actionable recommendations, much like an expert digital marketing consultant.

The project should focus on establishing secure connections to the Google Ads API and Google Search Console API. Once integrated, the skill needs to perform comprehensive audits across various dimensions. For Google Ads, this includes evaluating conversion tracking health, assessing keyword effectiveness to identify wasted spend, pinpointing irrelevant search queries, analyzing impression share for ranking opportunities, and calculating overall spend efficiency. For SEO, it should ideally analyze traffic, surface ranking impediments, and suggest on-page fixes like meta tag rewrites or structured data additions.

Architecturally, the solution should be modular, with distinct components for Google Ads and SEO functionalities. The core analysis engine should be robust and reusable, capable of processing data from Google's APIs and distilling it into clear, prioritized insights. This engine needs to be designed to power a conversational AI interface, ensuring that the outputs are structured and easily consumable by an agent.

The interaction should be conversational, allowing the AI to request specific audits, for example, `/toprank:ads-audit`. The output should be a structured summary, ideally a scorecard highlighting the status of each audited dimension (e.g., 'Critical', 'Warning', 'OK') along with a concise explanation. Crucially, it must then list prioritized, concrete actions the AI agent or user can take, such as "Pause X keywords" or "Add Y negative keywords." The overall experience should be about data-driven decisions and actionable guidance, not just dashboards.