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。

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:

CODEBLOCK0

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:

• - INLINECODE2
• INLINECODE3
• INLINECODE4
• INLINECODE5
• INLINECODE6
• 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:

• - INLINECODE8
• INLINECODE9
• INLINECODE10
• Any URL from: WeChat (mp.weixin.qq.com), Zhihu, Baijiahao, Medium, blog sites

Action:

1. 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:
CODEBLOCK2

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 INLINECODE14

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)

Step 5 — Risk verdict

Output Format

Quick badge (Mode 1 auto-trigger):
CODEBLOCK3

Full report (Mode 2 quick-check or Mode 3 URL, or when user asks for details):
CODEBLOCK4

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