Borrowing it
Nothing to install: this file belongs to MisonL/Ling. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/MisonL/Ling/main/.agents/skills/geo-fundamentals/SKILL.mdgit clone --depth 1 https://github.com/MisonL/LingWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/misonl/ling/geo-fundamentals)<a href="https://agentmods.dev/skills/misonl/ling/geo-fundamentals"><img src="https://agentmods.dev/badge/skills/misonl/ling/geo-fundamentals/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/misonl/ling/geo-fundamentals"><img src="https://agentmods.dev/badge/skills/misonl/ling/geo-fundamentals.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00030 | $0.01139 |
| Opus 5 | $0.00015 | $0.00570 |
| Sonnet 5 | $0.00006 | $0.00228 |
| Haiku 4.5 | $0.00003 | $0.00114 |
Grade A, and why
geo-fundamentals scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 156 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GEO 基础
针对 AI 驱动型搜索引擎的优化。
1. 什么是 GEO？
GEO = Generative Engine Optimization（生成式引擎优化）
| 目标 | 涉及平台 |
|---|---|
| 在 AI 的回答中被引用 | ChatGPT、Claude、Perplexity、Gemini |
SEO 与 GEO 对比
| 维度 | SEO | GEO |
|---|---|---|
| 目标 | 排名第一 | 被 AI 引用 |
| 平台 | AI 引擎 | |
| 指标 | 排名、CTR | 引用率 |
| 核心 | 关键词 | 实体、数据 |
2. AI 引擎版图
| 引擎 | 引用样式 | 机遇 |
|---|---|---|
| Perplexity | 编号样式 [1][2] | 引用率最高 |
| ChatGPT | 行内/脚注 | 自定义 GPTs |
| Claude | 上下文关联 | 长文本内容 |
| Gemini | 来源板块 | 与 SEO 有交集 |
3. RAG 检索因子
AI 引擎如何选择要引用的内容：
| 因子 | 权重（估算） |
|---|---|
| 语义相关性 | ~40% |
| 关键词匹配 | ~20% |
| 权威性信号 | ~15% |
| 时效性 | ~10% |
| 来源多样性 | ~15% |
4. 易被引用的内容类型
| 元素 | 作用原则 |
|---|---|
| 原创统计数据 | 独特、可引用的数据 |
| 专家语录 | 权重转移 |
| 清晰的定义 | 易于被提取 |
| 分步指南 | 提供可操作价值 |
| 对比表格 | 结构化信息 |
| FAQ 常见问题 | 直接给出答案 |
5. GEO 内容检查清单
内容元素
- 基于问题的标题
- 顶部包含摘要/TL;DR
- 带有来源说明的原创数据
- 专家语录（姓名、头衔）
- FAQ 章节（3-5 个问答）
- 清晰的定义
- “最后更新”时间戳
- 作者附带资质说明
技术元素
- 含日期的 Article schema（结构化数据）
- 作者的 Person schema
- FAQPage schema
- 极速加载（< 2.5s）
- 整洁的 HTML 结构
6. 实体构建
| 动作 | 目的 |
|---|---|
| Google 知识面板（Knowledge Panel） | 实现实体识别 |
| 维基百科（如具备知名度） | 建立权威来源 |
| 全网信息保持一致 | 实体整合 |
| 行业媒体提及 | 增强权威信号 |
7. AI 爬虫访问
关键 AI User-Agent
| 爬虫 | 对应引擎 |
|---|---|
| GPTBot | ChatGPT/OpenAI |
| Claude-Web | Claude |
| PerplexityBot | Perplexity |
| Googlebot | Gemini（共享） |
访问决策
| 策略 | 适用场景 |
|---|---|
| 允许所有 | 渴望获得 AI 引用 |
| 屏蔽 GPTBot | 不希望被用于 OpenAI 训练 |
| 选择性允许 | 允许部分、屏蔽其他 |
8. 效果衡量
| 指标 | 追踪方式 |
|---|---|
| AI 引用量 | 手动监控 |
| “According to [品牌]” 提及量 | 在 AI 中检索 |
| 竞品引用量 | 对比占有率 |
| AI 引流流量 | 使用 UTM 参数 |
9. 反模式
| [FAIL] 禁止 | [OK] 推荐 |
|---|---|
| 发布不带日期的内容 | 添加时间戳 |
| 模糊的来源引用 | 明确标注来源名称 |
| 忽略作者信息 | 展示其资质背景 |
| 内容空洞 | 全面覆盖 |
记住： AI 会引用清晰、权威且易于提取的内容。努力成为最好的答案。
脚本
| 脚本 | 用途 | 执行命令 |
|---|---|---|
scripts/geo_checker.py |
GEO 审计（AI 引用就绪度） | python scripts/geo_checker.py <project_path> |
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 156 lines · 30 tokens per session scan A 2645f48c7f73
geo-fundamentals is a skill published in the GitHub repository MisonL/Ling (8 stars, last pushed 5mo ago), licensed MIT. It adds 30 tokens to every session and 1,139 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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