Restructure existing web pages so AI assistants can extract and cite answers accurately. Get rewritten sections, comparison tables, FAQs, and JSON-LD structured data, a machine-readable description of page content, while preserving readability.
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Rewrite existing pages so AI assistants can extract, quote, and cite them. Claude restructures your content into answer-ready blocks — direct answers, comparison tables, stats with sources, schema — without wrecking the human reading experience. Platform: AI visibility.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Takes a page you already rank with (or want to) and restructures it for answer engines: front-loaded direct answers, extractable claims, clean entity naming, FAQ blocks, and schema markup. The goal is making your page the easiest source for an AI to quote accurately.
Paste the page content and the target question set. Claude audits it against extraction patterns — does the first paragraph answer the question, are claims specific and attributable, are comparisons in structured tables, is your brand named consistently. It then rewrites the weak sections and generates the JSON-LD schema to match.
A pricing comparison post buries the answer in paragraph six. Claude moves a 40-word direct answer to the top, converts the prose comparison into a table with concrete numbers, adds an FAQ section matching real query phrasing, and outputs FAQPage + Product schema. The page starts appearing as a cited source in Perplexity answers within weeks of reindexing.
This skill works best with live account data. Connect the free Ryze MCP once and Claude reads your Google Ads, Meta Ads, GA4 and Search Console directly:
https://connector.get-ryze.ai/mcpclaude mcp add ryze --transport http https://connector.get-ryze.ai/mcpSetup guide: https://www.get-ryze.ai/how-to-connect-claude-to-google-meta-ads-mcp
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Analyze A/B test results to assess whether observed differences are likely due to chance. Check test duration and sample size, compare audience segments, distinguish statistical findings from practical value, and generate hypotheses for follow-up tests.
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Plan and analyze A/B tests for Google and Meta advertising. Estimate required traffic and duration, track results, and assess whether observed differences are statistically significant, meaning unlikely to be explained by chance alone.
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Review Google and Meta campaign structures against goals, budgets, and conversion data. Identify excessive segmentation, overlapping targeting, and fragmented budgets, then get consolidation recommendations, an explained account structure, and a migration plan.
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Analyze successful Google and Meta ads to find useful patterns in headlines, body text, and calls to action. Generate new copy variants that preserve those patterns, with platform formatting, test priorities, and suggested pairings for comparison.
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Review Google Ads extensions, the extra links and details shown with ads, across campaigns. Find missing coverage, weak performance, and outdated information, then get replacement copy based on successful ads and landing pages, plus rollout priorities.
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Analyze spending, revenue, and conversions across advertising channels to recommend budget shifts. Compare overall efficiency with the revenue gained from additional spending, identify saturation, and produce a gradual reallocation plan with monitoring priorities.
中文简介正在准备中。当前显示英语简介。