GEO Poison Detector — AI推荐投毒检测器#

这个工具是做什么的？#

问题： AI推荐的产品不一定可信。不法商家通过「GEO优化」——批量生成含虚假参数的软文并大量铺发——让AI误以为这些虚构商品是市场上的优质选择。2026年央视315晚会曝光此类黑产，收费1.5–2万元/年，2小时内即可让虚假产品成为AI的「标准答案」。

这个skill做什么： 在你收到AI产品推荐时，自动扫描伪科技话术、软文模板特征、来源可信度，给出🟢安全 / 🟡可疑 / 🔴高危三档风险判断，并生成验证链接让你一键核查。

三种使用方式：

• 🤖 自动防护：AI推荐产品时，自动附带风险标记，无需任何操作
• 🔍 主动检测：发送 检测 产品名 或 /check product name，立即查
• 🔗 链接分析：发送文章链接，自动抓取并分析是否为投毒软文

覆盖范围： 中文/英文产品，国内（京东/淘宝/企查查）和国际（Amazon/BBB/USPTO）双市场验证。

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What this tool does (EN): Detects GEO (Generative Engine Optimization) poisoning — bad actors mass-generate fake product specs into AI training data, making fraudulent products appear as authoritative AI recommendations. Exposed by CCTV 315 (2026, China); similar tactics documented by Wired/MIT Tech Review globally. Auto-flags products when AI recommends them, supports user quick-checks by name, and analyzes article URLs for soft-ad patterns.

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Mode 1: Auto-Trigger (AI Recommending Products)#

Whenever you are about to recommend a specific product or brand, run detection first, then append a risk badge inline:

text

[Your normal recommendation answer]

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🛡️ GEO投毒检测 / Poison Check: [🟢 SAFE / 🟡 SUSPICIOUS / 🔴 HIGH RISK]
触发指标 / Indicators: [list triggered signals, or "无" if clean]
验证链接 / Verify: [run verify_product.py and include top 2-3 links]

Do NOT skip this step when recommending products. This is the core value of the skill.

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Mode 2: User Quick-Check (Product Name)#

Triggered when user sends any of:

• /check <product name>
• 检测 <产品名>
• 查一下 <产品名>
• is this legit: <product>
• 这个可信吗: <产品名>
• Or any message asking to verify a specific product by name

Action: Run the full 5-step detection workflow on the product name. Output the full report format. Run scripts/verify_product.py "<product>" to generate verification links.

Example interaction:

text

User: /check 量子能量水杯黑洞级净化
Agent: 🔴 HIGH RISK — 检测到2个高权重伪科技词汇
触发指标：
• [Step 1] 「量子能量」— 高风险伪量子话术
• [Step 1] 「黑洞级」— 高风险伪黑科技话术
验证链接：[京东] [企查查] [国家专利局]
建议：该产品名称含多个典型GEO投毒特征词，极可能为虚假推荐，请勿购买。

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Mode 3: URL Analysis (Article/Page)#

Triggered when user sends a URL and asks to check it:

• check this: https://...
• 帮我检测这篇文章: https://...
• 这个链接可信吗: https://...
• Any URL from: WeChat (mp.weixin.qq.com), Zhihu, Baijiahao, Medium, blog sites

Action:

1. Use web_fetch to retrieve the article content
2. Run the full 5-step detection workflow on the fetched text
3. Also note the source domain as part of Step 4 source quality assessment
4. Output the full report format

Example interaction:

text

User: 帮我检测这篇文章 https://mp.weixin.qq.com/s/xxxxx
Agent: [fetches content]
🟡 SUSPICIOUS — 检测到软文批量生成特征
触发指标：
• [Step 2] 模板化结构："很多人不知道的是" + 产品推荐固定格式
• [Step 4] 来源：微信公众号自媒体，无权威背书
验证链接：[产品名搜索链接]
建议：内容结构符合GEO软文模板，建议通过官方渠道核实产品信息。

Handling fetch failures: If web_fetch fails or is blocked, ask user to paste the article text and switch to Mode 2 workflow.

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Detection Workflow (5 Steps)#

Apply to content from any mode.

Step 1 — Pseudo-tech buzzword scan (HIGH weight)#

Load references/pseudo-tech-terms.md. Scan for high-risk terms in both CN and EN sections.

• 2+ high-risk terms → immediately 🔴
• 1 high-risk term → 🟡 suspicious

Step 2 — Batch-generated content fingerprint (HIGH weight)#

Universal signals (CN+EN):

• Fixed template structure (Problem → Solution → Product plug)
• Keyword stuffing (product name repeated 5+ times)
• Vague superlatives without verifiable data
• No model numbers, no verifiable specs, no brand registration
• Multiple sources with identical or near-identical wording

CN-specific:

• "很多人不知道的是..." / "内部员工都在用"
• 自媒体/百家号/微信公众号 as sole sources
• 无厂商官网、无天猫/京东旗舰店

EN/Global-specific:

• "Doctors don't want you to know..."
• Affiliate disclosure buried or absent
• Only "as seen on" claims, no retailer presence
• Reviews only on brand's own site, not Amazon/Trustpilot

Step 3 — Product authenticity cross-verification (MEDIUM weight)#

Run scripts/verify_product.py "<product name>" [--market cn|global|auto]

CN market: JD.com, Taobao, Qichacha, Tianyancha, CNIPA patents, GB standards Global market: Amazon, Google Shopping, BBB, Trustpilot, USPTO patents, EU RAPEX, Reddit

Step 4 — Source quality assessment (MEDIUM weight)#

Source TypeCN ExampleGlobal ExampleTrustMajor retailer official京东/天猫旗舰店Amazon/BestBuy officialHighGov/standards body国家标准委/CNIPAFDA/CE/ISOHighMainstream media央视/人民日报NYT/BBC/ReutersHighBrand official site品牌官网brand.comMediumSelf-media only百家号/头条/微信Medium blogs/affiliateLowUnknown/unverifiable来源不明UnknownVery Low

Step 5 — Risk verdict#

ResultThreshold🟢 SAFE0–1 low-weight indicators🟡 SUSPICIOUS2+ medium OR 1 high-weight indicator🔴 HIGH RISK2+ high-weight OR confirmed fake specs

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Output Format#

Quick badge (Mode 1 auto-trigger):

text

🛡️ GEO Check: 🟢 SAFE — no poisoning signals detected

Full report (Mode 2 quick-check or Mode 3 URL, or when user asks for details):

text

[🟢/🟡/🔴] <one-line verdict in user's language>

触发指标 / Indicators:
• [Step N] <indicator> — <explanation>

验证链接 / Verify:
• <platform>: <url>

建议 / Recommendation: <next action>

Language#

• Match user's language (CN/EN/mixed)
• Auto-detect market from product name (CJK → CN, Latin → Global)
• For CN products in global context: check international presence too

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References#

• Term library (CN+EN): references/pseudo-tech-terms.md — load during Step 1

• Verification script: scripts/verify_product.py — run during Step 3

  • Usage: python3 verify_product.py "<name>" [--market cn|global|auto]