Geo Poison Detector

AI推荐防投毒检测器 / AI Recommendation Poison Detector. 你有没有遇到过：AI推荐了一款产品，买回来才发现是劣质品或根本不存在的品牌？这就是GEO投毒——不法商家花钱批量制造虚假软文，让AI误以为这些产品是市场上的优质选择。这个skill帮你识破这些陷阱。三种使用方式：(1)...

Security Scan

The skill's code, instructions, and resource files are coherent with its stated purpose (detecting GEO/soft-ad poisoning) and do not request unrelated credentials, installs, or system access.

Purpose & Capability

Name, description, and included artifacts (pseudo-tech term list + Python verifier) align with a product/soft-ad poisoning detector. No unrelated secrets, binaries, or platform SDKs are required.

Instruction Scope

SKILL.md directs the agent to scan text with the included term list, run the bundled scripts/verify_product.py, and (optionally) fetch web pages via web_fetch. All referenced files are present and used for the stated checks. Note: web_fetch will retrieve arbitrary pages supplied by users — this is expected for URL analysis but has privacy/CSF implications (see guidance).

Install Mechanism

No install spec; instruction-only with one small Python script and a reference file. No external downloads, package installs, or extract steps are present.

Credentials

The skill requires no environment variables, credentials, or privileged config paths. The Python script constructs public search URLs and prints checklists; it does not access secrets or perform network requests itself.

Persistence & Privilege

always is false and there is no request for permanent/privileged presence or modification of other skills or system settings. Autonomous invocation is allowed (platform default) but not combined with other red flags.

Assessment

This skill appears internally consistent: it bundles a harmless Python helper that builds public verification URLs and a list of pseudo‑tech buzzwords; the runbook tells the agent to fetch pages (web_fetch) and run local checks. Before installing, consider: (1) If you enable the skill's automatic auto‑trigger mode, the agent may fetch user-provided URLs — ensure that outbound web_fetch behavior and privacy policies are acceptable in your environment. (2) The detection is heuristic and may yield false positives/negatives; treat results as investigative help, not definitive proof. (3) If you want to be extra cautious, keep the skill user‑invocable only (disable autonomous triggers) and review/execute scripts in an isolated environment — the bundled script itself does not perform network calls or exfiltrate data. Overall, no unexplained credential or install requests were found.

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:

[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:

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:

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 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

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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

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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]