About this skill

Problem Solved

It is hard to tell whether AI search systems recommend a brand, mention it, or leave it invisible. GEO Master treats answers from platforms such as Kimi, Xunfei, Wenxin, DeepSeek, Qwen, Doubao, Mita, and Hunyuan as observable signals, producing a visibility score and optimization notes.

How It Works

The skill crawls multiple AI search answers, analyzes why a brand appears, and generates a report. geo_searcher.py handles platform querying, geo_analyzer.py applies a local analysis framework for recommendation reasoning, and geo_report.py emits scores and suggestions. Free and Standard tiers mainly use local Playwright; Pro uses the Tavily API for real-time search recommendations and reduces local environment and IP friction. Scores range from 0-29 for weak visibility to 80-100 for active recommendation, with setup via config.json, GEO_QUOTA_FILE, and an optional Feishu Webhook.

Boundaries

Free allows one brand and three platforms per month, with quota reset on the first. Pro requires TAVILY_API_KEY or GEO_API_KEY, and verification failure downgrades service. Results can be affected by login walls, selectors, and platform volatility, so treat them as diagnostic signals rather than a complete brand tracking system.

Screenshots

Use Cases

Check whether Kimi or DeepSeek recommends a new product launch and score it
Summarize monthly brand exposure levels across nine AI search platforms
Configure Tavily API, quotas, and Feishu notifications for a monitoring workflow
Explain low-scoring platforms and compile optimization recommendations

Best For

Marketing owners tracking brand exposure need periodic AI recommendation levels
Product operators checking whether DeepSeek or Qwen mentions a new feature
Data analysts converting multi-platform AI answers into scored reports
Independent developers adding Feishu Webhook and quota control to brand monitoring