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。
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.
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:
[Your normal recommendation answer]
---
🛡️ 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.
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:
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。
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:
- Use
web_fetchto retrieve the article content - Run the full 5-step detection workflow on the fetched text
- Also note the source domain as part of Step 4 source quality assessment
- Output the full report format
Example interaction:
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.
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 Type | CN Example | Global Example | Trust |
|---|---|---|---|
| Major retailer official | 京东/天猫旗舰店 | Amazon/BestBuy official | High |
| Gov/standards body | 国家标准委/CNIPA | FDA/CE/ISO | High |
| Mainstream media | 央视/人民日报 | NYT/BBC/Reuters | High |
| Brand official site | 品牌官网 | brand.com | Medium |
| Self-media only | 百家号/头条/微信 | Medium blogs/affiliate | Low |
| Unknown/unverifiable | 来源不明 | Unknown | Very Low |
Step 5 — Risk verdict
| Result | Threshold |
|---|---|
| 🟢 SAFE | 0–1 low-weight indicators |
| 🟡 SUSPICIOUS | 2+ medium OR 1 high-weight indicator |
| 🔴 HIGH RISK | 2+ high-weight OR confirmed fake specs |
Output Format
Quick badge (Mode 1 auto-trigger):
🛡️ GEO Check: 🟢 SAFE — no poisoning signals detected
Full report (Mode 2 quick-check or Mode 3 URL, or when user asks for details):
[🟢/🟡/🔴] <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
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]
- Usage: