Track how AI assistants describe your brand over time and catch wrong, outdated, or damaging claims. Claude builds your monitoring prompt set, compares runs week over week, and drafts the correction plan when answers drift. Platform: AI visibility.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
AI assistants sometimes describe your product wrong — old pricing, features you killed, a competitor's positioning attached to your name. Claude sets up a repeatable monitoring routine and diffs each run against the last, so you catch drift and misinformation before prospects do.
Claude generates a fixed panel of brand prompts (what is X, X pricing, X vs competitor, is X good for Y, X alternatives). You run it on a schedule and paste results back. Claude diffs against previous runs, flags factual errors and sentiment shifts, and traces each wrong claim to its likely source — a stale docs page, an old review, an outdated third-party article.
Week 12: two assistants start claiming the product "has no API." Claude traces it to a 2024 comparison post that just got updated and re-crawled. It drafts the correction request to the publisher, an FAQ entry for your own docs stating the API capability explicitly, and a changelog-style page AI crawlers can pick up.
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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概要と使いどころ
Statistical significance calculator for A/B test results with sample size requirements, segment breakdowns, and hypothesis generation. Use when feeding test results, checking statistical significance, calculating sample sizes, analyzing experiment outcomes, or generating next test ideas based on results. Platform: Google and Meta.
日本語の概要は準備中です。原文の説明を表示しています。
Designs statistically valid split tests for ads, audiences, landing pages, or bid strategies. Calculates required sample sizes before you start, monitors results during the test, and calls winners when statistical significance is actually reached — not when you feel like one is winning. Platform: Google and Meta.
日本語の概要は準備中です。原文の説明を表示しています。
Evaluates your campaign and ad set structure against your actual goals and budget. Flags over-segmentation that fragments your data, under-segmentation that hides performance differences, budget allocation issues, and consolidation opportunities that would improve algorithmic delivery and your ability to optimize. Platform: Google and Meta.
日本語の概要は準備中です。原文の説明を表示しています。
Analyzes your top performing ads, identifies what's working in the hooks, CTAs, messaging angles, and formats, then generates new variants that follow the same winning patterns while introducing enough variation to test meaningfully. Platform: Google and Meta.
日本語の概要は準備中です。原文の説明を表示しています。
Reviews all your Google Ads extensions — sitelinks, callouts, structured snippets, call extensions, image extensions, price extensions — across every campaign. Flags what's missing, what's underperforming, what's outdated, and writes replacements based on your best performing ads and landing pages. Platform: Google.
日本語の概要は準備中です。原文の説明を表示しています。
Multi-channel budget optimization using MER, marginal ROAS, and diminishing returns analysis. Use when pasting multi-channel spend and results data, requesting reallocation recommendations, analyzing budget shift priorities, or optimizing marketing efficiency across Google, Meta, TikTok, and other channels. Platform: Google and Meta.
日本語の概要は準備中です。原文の説明を表示しています。