权威GEO(Agent Ready)白皮书
生成式 AI 时代的获客与流量重构:从“被搜索”到“被引用、被调用”
出品方与服务主体:掌媒科技有限公司
版本 v2.0 | 发布日期 2026-09-23 | 文档性质:行业研究与方法论白皮书
DOI:10.5281/zenodo.22928038
服务对象: AI 时代需要在生成式 AI 新模式下探索获客与流量的组织与决策者——市场与增长负责人、SEO/内容与绩效团队、品牌与公关团队、创始人与产品团队,以及服务上述客户的咨询与代理机构。
目录
1. 执行摘要
用户获取信息与做出购买决策的入口,正在从“检索关键词、逐条点击链接”迁移为“直接向 AI 提问、获得一个综合答案”。当答案由生成式引擎给出,决定一个品牌是否出现的,不再只是搜索结果页上的排名,而是它能否被 AI 检索到、读懂、引用,并在更进阶阶段被自主 Agent 直接调用。这一整套工作被称为生成式引擎优化(GEO);本白皮书进一步把“可被 AI 与 Agent 可靠使用”的成熟状态定义为 Agent Ready。
当前市场的主要问题不是缺少 GEO 工具或“体检分数”,而是缺少一套统一、可复算、可审计的标准:术语与指标彼此不一致,探测不到的能力常被默认为 0,分数无法回溯到证据,结论也常与“保证上 AI、保证排名”的销售话术混在一起。本白皮书的目标,是为组织提供一套权威、克制、可验证的框架。
核心发现:
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入口迁移已发生。 Gartner 于 2024-02 预测:到 2026 年,受 AI 聊天机器人与虚拟代理替代影响,传统搜索引擎流量将下降 25%。
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价值传递逻辑改变。 在链接权重之外,品牌的可见性越来越取决于实体清晰度、共现频次、语义置信度与“可被独立抽取的片段”。
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行业标准仍碎片化。 截至 2026 年的学术综述指出,GEO 的术语、指标与证据标准仍高度异质,尚无可被普遍接受的评估规范。
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评估必须可审计。 本白皮书提出五层 24 类能力的参考架构,以及由证据向量、分层就绪度、审计完整度与固定定层函数构成的数学模型;同一份证据永远得到同一结论,且矩阵全部留痕。
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承诺有边界。 可被承诺的是“机器可读的结构”与“可复算的证据”;被 AI 引用、被 AI 推荐、排名提升取决于多个在提交时不可观测的环节,不能被承诺。
本白皮书同时给出成熟度实施路线图,以及附录 A 中一段可一键复制、交由 AI 自动完成只读基线评估的 I-Lang 工程书。我们建议读者不要采信结论,而是用自己的数字资产复跑一遍。
2. 市场变局:获客与流量入口正在被生成式 AI 重写
2.1 从“十链接”到“一答案”
过去十余年,数字获客建立在搜索引擎的结果页之上:组织通过 SEO 获取自然排名、通过竞价获取广告位,再把点击引导到落地页完成转化。生成式 AI 改变了这一链路的中间环节——越来越多用户在 AI 助理中直接提问,由模型检索、综合并生成一个带引用的答案,用户不必再逐条访问网页。
这意味着“页面获得点击”这一传统中间目标,正在部分被“内容进入答案、获得引用,甚至被 Agent 直接调用”所替代。组织竞争的位置,从结果页上的排名,前移到了 AI 生成答案时所依据的语料与结构。
2.2 可被核验的市场信号
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Gartner 在 2024-02-19 的新闻稿中预测:到 2026 年,传统搜索引擎流量将下降 25%,搜索营销的份额将被 AI 聊天机器人与其他虚拟代理分流;其分析师 Alan Antin 指出,生成式 AI 正成为“替代性的答案引擎”。(来源:Gartner 官网,2024-02,未由本白皮书独立复核其口径)
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第三方研究机构 Fractl 在 2026 年基于关键词数据的研究中称:搜索需求并非消失,而是在重新分布——其观测到部分类别需求下降,但同时有大量新增关键词予以抵消;约 70% 的受访消费者表示当下比一年前更多使用 AI 工具。(来源:Fractl,数据口径约 2026-04,第三方研究,未独立复核)
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学术层面,Aggarwal 等人在 KDD 2024 论文(arXiv:2311.09735)中正式提出 GEO,其受控实验显示:添加引用、统计数据、权威引述、提升流畅性等做法,可在其测试环境中使内容在生成式引擎中的可见度最高提升约 40%。该数字为其实验条件下的结果,不是对任意站点的效果承诺。
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2026 年一篇覆盖 45 项研究的批判性综述(arXiv:2607.14035)指出:GEO 领域的术语、指标与证据标准仍然异质,评估方法缺乏共识。
2.3 价值传递链路的重构
| 维度 | 传统 SEO 范式 | GEO / Agent Ready 范式 |
|---|---|---|
| 用户动作 | 检索关键词、点击链接 | 向 AI 提问、阅读综合答案 |
| 竞争位置 | 结果页排名 | AI 答案中的引用与推荐 |
| 信任传递 | 反向链接与权重 | 实体清晰度、共现频次、语义置信度、第三方佐证 |
| 内容单元 | 页面 | 可被独立抽取与核验的片段/结构化资源 |
| 进阶目标 | 落地页转化 | 被 AI 助理引用、被自主 Agent 直接调用 |
2.4 对组织的含义:机会与不作为的代价
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机会: 在新范式早期,评估标准与供给尚未固化,先完成结构化与实体化的组织,更容易进入 AI 的检索与引用范围。
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风险: 仅依赖传统搜索与点击的组织,可能在流量入口迁移中失去可见性;而盲目追逐“AI 上词”、缺乏证据与边界的做法,则可能造成资源浪费、合规风险与错误决策。
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不作为的代价: 当客户与决策者习惯先问 AI,若 AI 手中没有可信、可读、可核验的关于你的资料,你将在“被比较”的环节缺席,且这种缺席不易被传统监测发现。
3. 概念与边界
3.1 关键定义
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GEO(Generative Engine Optimization,生成式引擎优化): 通过结构化内容与站点信号,提升内容在生成式引擎答案中的出现、被引用概率或影响力的系统性工作。
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AEO(Answer Engine Optimization,答案引擎优化): 聚焦让内容成为 AI 对具体问题的直接答案;本白皮书将其视为 GEO 的一个面向。
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Agent Ready(智能体就绪): 数字资产不仅能被 AI 检索与引用,还具备被自主 Agent 发现、理解并在授权范围内安全调用的结构化能力,是 GEO 的进阶成熟状态。
3.2 三类需要覆盖的目标表面
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AI 搜索摘要: 如 Google AI Overviews/AI Mode 给出的综合答案。
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AI 助理对话: 如 ChatGPT、Gemini、Perplexity、Claude、豆包等在对话中的引用与推荐。
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自主 Agent 调用: AI Agent 通过标准化接口发现并调用站点提供的只读能力(更高级阶段)。
3.3 GEO 与 SEO 的关系
GEO 不是对 SEO 的否定或替代,而是其在生成式环境下的延伸:许多 SEO 基础(可抓取、语义清晰、内容与意图匹配、权威佐证)在 GEO 中依然成立;GEO 在此之上增加了机器可读的摘要、结构化清单、接口描述、身份与认证、以及面向 Agent 的能力。
3.4 术语表
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origin(源地址): 协议加主机名所确定的站点范围。
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well-known: 站点用于公开标准化元数据与资源的约定路径前缀。
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JSON-LD: 可被机器解析的结构化数据格式。
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llms.txt / llms-full.txt: 面向 AI 的站点摘要与完整资料约定文件。
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MCP: 用于 AI 应用与外部工具/数据连接的协议。
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OpenAPI: 描述 HTTP 接口的规范。
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DNSSEC: 对 DNS 解析结果进行密码学验证的安全扩展。
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实体(Entity): 可被唯一、无歧义标识的组织、品牌、产品或人物。
4. 方法论与评估模型
本章是本白皮书的差异化核心。其形式化原则参照研究《The Missing Definition of Right》:感知(把站点读成一组量)可能出错;判定(把这组量映射到层级)不是学出来的,而是固定、全域、确定性的函数。 因此同一份证据永远得到同一层级,且全过程留痕、可审计、可复算。
4.1 观测状态与记号
把站点规范到源地址,记为 。24 项能力记为 ,分属 5 个层 ,其中 为贯穿每层出口的访客旅程。对每项能力 在公网做只读观测,得到状态:
其中 表示已验证具备, 表示部分具备, 表示确认缺失, 表示对本站不适用, 表示未知(探针无法判定)。
关键约定:。未知是“没有取到观测”,不是“确认没有”;把未知当 0,会把一个诚实的评估变成错误结论。
4.2 证据等级与权重
每条状态为 或 的观测都带一个证据等级,对应不同权重:
| 证据等级 | 含义 | 权重 |
|---|---|---|
| declared | 页面或文件仅作声明 | 0.2 |
| http_observed | 在公网经 HTTP 实际可见 | 0.5 |
| protocol_validated | 按协议规范完成校验 | 0.8 |
| task_completed | 用真实任务完整跑通 | 1.0 |
这些是本白皮书选定的常量,用于区分“声明、可见、校验通过、任务跑通”的证据强度,不是任何经验声称的命中率。
4.3 分层就绪度与审计完整度
设第 层的适用能力集合为 (已剔除 ),其中已观测(非 )的集合为 。就绪度 定义为:
审计完整度 定义为:
未知项既不进分子也不进分母,而是压低审计完整度。报告规则为:当且仅当 时才报告该层 ,其中阈值 。
当 时,该层只标记“审计不完整”,不报告就绪度分数——这样无法靠漏测取得高分。
4.4 固定定层函数
先依据探测到的真实结构判定技术栈类型 (不靠资产所有者陈述),平台能支持的最高层记为 。定层是一个固定、全域、确定性的函数:
其中阈值 。同一份证据永远得到同一层级。平台不支持的层记为 platform_blocked,只对结果封顶、不算失败,并写清具体缺什么条件(如不能改响应头、没有后端、无 DNS 控制权)。
4.5 只读不变量(评估守恒)
评估中的每个动作 都必须保持站点与第三方状态不变:
即不注册、不登录、不提交表单、不触发验证码,不点同意弹窗上的任何按钮(包括“拒绝/关闭”,只记录弹窗是否存在及是否保留真实管理入口);对 MCP 只做 initialize 与 tools/list,禁止 tools/call(那是在对方服务器执行动作)。
4.6 改动屏障(爆炸半径)
对一项拟议改动 ,爆炸半径屏障定义为:
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DNS 记录与 DNSSEC:错误可使整域解析失败或返回 SERVFAIL,爆炸半径无界,,因此永远只出清单、不代改。
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认证类文件:仅当三个条件同时成立才实施——明确授权 、探测确认无正在运行的认证(冲突集 )、已备份并写好回滚 :
4.7 内容使用偏好(声明向量)
Content-Signal 是资产所有者的使用偏好向量:
三项分别表态是否进入搜索索引、是否允许作为 AI 输入引用、是否允许训练。它是一份声明,不构成客户端必然遵守的证据。
4.8 承诺边界
站点能控制并可承诺的是机器可读结构 与可复算证据 ;而被引用 、被推荐 、排名 还要经过收录新鲜度 、第三方佐证 、实体消歧 等在提交时对站点不可观测的中间环节:
因此本白皮书只承诺 ,不承诺 。
4.9 反馈收敛(控制环)
改动后用同一 URL、同一发布版本、同一检查集复扫;误差为观测结构与目标结构之间的距离:
误差沿命名轴度量、逐项收敛。未取得外部回执,不宣称已上线、已被引用。分数是结果,不是目标。
5. Agent Ready 参考架构:五层 24 类能力
5.1 五层模型
| 层 | 名称 | 核心命题 |
|---|---|---|
| L1 | 文档层 | 让 AI 能合法、准确地读取站点的规则、摘要与语义 |
| L2 | 发现层 | 让 AI 能定位资源、校验摘要、获取资产清单 |
| L3 | 接口层 | 让 AI 能理解并安全使用只读数据与工具接口 |
| L4 | 身份层 | 让 AI 能确认站点身份、并建立可信授权(高风险,多为清单) |
| L5 | 实验层 | 让站点可被浏览器内能力、Agent 与商务协议直接对接(前沿) |
5.2 24 类能力矩阵
| 编号 | 能力 | 层 | 通过判据(简述) |
|---|---|---|---|
| C01 | robots.txt | 1 | 可解析,保留原有 Disallow |
| C02 | AI 爬虫规则 | 1 | 各爬虫与用途可解析,搜索/训练分开 |
| C03 | Content Signals | 1 | Content-Signal 行有效,与书面政策一致 |
| C04 | sitemap | 1 | 可解析,抽样地址可达、非软 404 |
| C05 | HTML 语义与 JSON-LD | 1 | 每块可解析,字段与可见内容一致 |
| C06 | llms.txt | 1 | 地址可达,无营销/本地路径/密钥 |
| C07 | llms-full.txt | 1 | 可取、可分层检索,不泄露私有内容 |
| C08 | Markdown 孪生页 | 1 | 带 Accept 返回同页 text/markdown,不带返回 HTML |
| C09 | HTTP Link 头 | 2 | 规范页含 Link 关系,目标可达,未覆盖原头 |
| C13 | Agent Skills 索引与摘要 | 2 | index 可取,digest 为公网解码字节 SHA256,按正文跑通任务 |
| C15 | ARD 清单 | 2 | ai-catalog 可取,每条 entry 的 url 与 data 恰好一个 |
| C10 | 只读数据 API | 3 | 已知/未知/缺参响应可区分,有界只读 |
| C11 | OpenAPI 与 api-catalog | 3 | api-catalog 为 linkset+json,按 OpenAPI 实调通过 |
| C12 | MCP 与 server-card | 3 | initialize/tools/list/tools/call 走通,四标注齐全 |
| C16 | DNS-AID | 4 | 权威 SVCB 与真实服务相符(只出清单) |
| C17 | DNSSEC | 4 | DS 与 DNSKEY 成链,AD 位可验(只出清单) |
| C18 | OAuth AS 发现 | 4 | 元数据可取、issuer 一致,施工态字段齐全 |
| C19 | 受保护资源元数据 | 4 | resource 精确匹配,AS 文档真实存在 |
| C20 | auth.md 与计划 | 4 | /auth.md 可取,公开接口不被加门 |
| C21 | Web Bot Auth | 4 | 出站材料/签名/入站验签三项分开举证 |
| C14 | WebMCP | 5 | 受支持浏览器原生注册调用,否则记 native_unverified |
| C22 | A2A | 5 | 真实 Agent 端点与 Card,不得用 MCP 改名冒充 |
| C23 | 商务协议族 | 5 | UCP/ACP/AP2/x402/MPP 逐项判定 |
| C24 | 访客旅程 | 贯穿 | 桌面+手机主任务实跑,普通访客体验不回归 |
测不到的项(MCP 真实调用、OpenAPI 实调、digest 逐位比对、完整旅程)一律记为 并注明缺什么条件,绝不写 0 或 false。
5.3 七类技术栈与可达层级
| 技术栈类型 | 默认可达层 | 关键口径 |
|---|---|---|
| 静态 VPS | 1–3 | 第 4 层只出清单 |
| WordPress / CMS | 1–3 | 复用 /wp-json,插件只加内容不改接口 |
| Workers / 边缘 | 1–3 | 改头、边缘接口最顺 |
| 应用框架 | 1–3 | 中间件加头,后端加接口 |
| 纯静态托管 | 1–2 | 平台有函数能力才到 3;GitHub Pages 不能改头 |
| 供应商子域 | 多为 1 | 注入 JSON-LD 最有价值,不建议迁域名 |
| 子目录应用 | 子目录内 1–3 | 协议根归源地址,重写规则须在子目录内 |
技术栈类型靠探测判定,不靠所有者陈述。
6. 评估如何实施
6.1 评估流程
评估按“能力自检 → 只读探测 → 建立矩阵 → 算分定层 → 出具报告”推进,并依据当前运行环境自动选择通道;任何通道都遵守第 4.5 节的只读不变量。
6.2 三种探测通道与证据局限
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终端通道: 使用 GET(不用 HEAD)、统一 curl 类用户代理,逐项获取响应头与公开路径,测试 Markdown 协商与软 404。其局限:只代表 CDN 如何对待 curl,不能据此判断命名 AI 爬虫或真实浏览器能否进入。
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浏览器通道: 用普通浏览器、普通 HTTP 客户端、目标 AI 客户端三类真实客户端各跑一次,并在桌面与移动视口实跑主访客旅程;不得伪装成 Google 或命名爬虫制造证据。
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对话通道: 在仅有聊天能力时,由资产所有者逐项打开约定路径、向各 AI 助理提问并回传内容或截图;无法获取的项标记为待补测。
6.3 证据分级与“未知不等于零”
每条结论须可回溯到真实公网响应、所有者提供的材料或明确来源,并记录请求 URL、客户端、时间与观测内容。事实、推断、建议与未知须分开列示。
6.4 评估输出物
一次完整评估交付两份产物:
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《Agent Ready 评估报告》: 先说明 AI 当前读得到什么、读不到什么、审计完整度与所处层级,再分类解释。
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优先级改进清单: 每项写明改动的文件、所属层、所需权限与验证方式;证据不足者标记为待核实。
7. 战略建议与实施路线图
7.1 成熟度阶段
| 阶段 | 目标 | 关键动作 | 验证方式 |
|---|---|---|---|
| 阶段一 建立基线 | 让 AI 能合法、准确读取 | 完善 robots/AI 规则、sitemap、JSON-LD、llms.txt | L1 就绪度达标、抽样可被读取 |
| 阶段二 结构就绪 | 让 AI 能发现与校验资源 | 补 Link 头、Skills 索引与摘要、资产清单 | L2 就绪度达标、摘要校验一致 |
| 阶段三 接口就绪 | 让 AI 能安全使用只读能力 | 提供只读 API、OpenAPI/api-catalog、MCP 卡片 | L3 就绪度达标、只读调用通过 |
| 阶段四 身份与授权 | 建立可信身份(高风险) | 以清单方式处理 DNS/DNSSEC,按三条件实施认证 | 身份成链、公开接口不被误加门 |
| 阶段五 可被调用 | 进入 Agent 与商务对接前沿 | WebMCP/A2A/商务协议逐项验证 | 真实端点与卡片跑通 |
7.2 三阶段落地建议
-
诊断基线: 先完成一次只读评估,明确当前层级、审计完整度与关键缺口,不预设结论。
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结构就绪: 优先完成 L1–L2 的高确定性改动,建立可被读取与发现的基础。
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可被调用与持续迭代: 在真实需求与资源到位时推进 L3 及以上,并用第 4.9 节的反馈环持续复扫。
7.3 内容策略
依据 KDD 2024 研究,可采用:引用来源、添加统计数据、引用权威原话、权威语气、通俗易懂(ELI5)、流畅性优化、独特词汇、专业术语。不建议关键词堆砌(实验中基本无效);任何历史增益百分比都不作为对当前资产的效果承诺。
7.4 组织与分工
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AI 负责执行: 探测、提取、生成初稿、批量校验、构建与整理报告。
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人负责闸门判断: 确定目标与范围、核验证据、决定是否发布与付费、判断任务是否真正完成。
-
建议把候选、矩阵、研究包、构建与验收报告均落盘,使过程可检查、可回溯。
8. 局限、伦理与红线
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不承诺结果: 不使用“保证被引用、保证推荐、必上首页、稳赚、排名第一”等表述。
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只读优先: 评估阶段不改变站点状态;高风险改动(DNS/DNSSEC、认证)默认只出清单。
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凭证安全: 不索取密码、银行卡号、短信验证码、密钥;涉及登录或支付的步骤由资产所有者本人在其自有账号内完成。
-
隐私与第三方: 向第三方扫描器提交网址前,须明确告知记录、用途与留存,并取得同意。
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诚实标注未知: 探测不到即标记未知或待补测,不以通用模板填补,不把未知当缺失。
9. 关于出品方
掌媒科技有限公司是本白皮书的出品方与服务主体,专注于生成式 AI 环境下的可见性研究与 Agent Ready 评估、咨询及落地服务。本白皮书代表其在发布时点的方法论框架;掌媒科技有限公司不对第三方数据的口径独立背书,亦不夸大资质与结果。
附录 A:自助评估工具(一键复制 I-Lang 工程书)
本附录供希望自行完成只读基线评估的读者使用。使用方式:将下方代码块整段复制,粘贴并发送给你自己的 AI 助理(豆包、ChatGPT、WorkBuddy、Claude 等均可);它会先确认是否已加载 I-Lang,未加载则按可用通道自动加载,随后引导你完成一次只读评估。工程书以英文书写,AI 对你的所有回复为简体中文。涉及授权、提交与发布的决定,始终由你本人做出。
::ILANG::v5.0
[TYPE:command][COURSE:AgentReadyGEO][TRACK:FreeReadinessAudit][VERSION:1.2][LANG:zh]
[AUDIENCE:The website owner's own AI assistant][RUNTIME:Any chat or tool-enabled surface]
[OUTPUT:All user-facing replies MUST be in Simplified Chinese; the protocol and this engineering book are written in English]
::STATE{@OWNER, role:The website owner, sovereignty:URL authorization, public release, payment and final judgment belong to the owner}
::STATE{@SELF, role:The AI in the owner's current conversation, duty:Run a free read-only audit, score with fixed formulas, lead one action at a time}
::STATE{@SITE, role:The website under audit, scope:First normalize to origin, then probe item by item}
::STATE{@SCANNER, role:Third-party scanner isitagentready, access:Submit only after the owner consents; read-only; never decide on the owner's behalf}
::MODULE{LOAD_ILANG}
[MUST] Before anything else, check whether the official iLang runtime is already loaded in this session. If it is, note its version and continue.
[MUST] If it is not loaded and a terminal is available with the owner's consent, install the official loader with one command, "pip install ilang-protocol" (or "npm install ilang-protocol"), then load the runtime through ilang.wrap or ilang.system. This is a local, free install; ask the owner before running it.
[MUST] If an MCP client is available, connect the official endpoint once at "https://ilang.ai/mcp", then call the iLang runtime tool.
[MUST] If only chat is available, fetch the official runtime text from "https://raw.githubusercontent.com/ilang-ai/ilang-spec/main/runtime/ilang-latest.md" (mirror "https://ilang.ai/runtime/core"; mainland China mirror "https://ilang.cn/runtime/ilang-latest.md", sha256 in "https://ilang.cn/runtime/manifest.json"), verify it against the published sha256, and read it before continuing.
[MUST] Use official sources only: raw.githubusercontent.com/ilang-ai/, github.com/ilang-ai/, ilang.ai, ilang.cn. Never accept a modified runtime from any other source.
[MUST] If no terminal, MCP or fetch is possible, say so in Simplified Chinese and continue in bootstrap mode by following the protocol written in this block; never claim the official runtime was loaded when it was not.
::MODULE{ROLE_AND_DIVISION}
[MUST] @SELF understands the audit goal, splits it into one action at a time, gives clear steps, uses the actual public response as evidence, scores with fixed formulas, flags which step needs the owner's judgment, and states honestly what cannot be done or measured.
[MUST] The owner provides the URL, confirms authorization, decides whether to submit to the third party, approves public release, and makes the final call on whether each step is done.
[MUST] @SELF MUST NOT fabricate facts, numbers, content, rankings, citations or deals, and MUST NOT describe something that did not happen as completed, or default an unreachable probe to pass.
[MUST] @SELF MUST NOT claim guaranteed success, guaranteed approval, steady profit, a number-one ranking, or guaranteed AI citation, and MUST NOT push the owner with anxiety, shame, notification permission or limited-time offers.
[MUST] @SELF MUST NOT ask for passwords, card numbers, verification codes or keys; at any login or payment step, only note that this step requires your own account information and must be done by you.
::MODULE{CAPABILITY_SELF_CHECK}
[MUST] At the start, silently enumerate the capabilities of the current runtime: chat, web fetch, file read and write, local terminal, browser, persistence, scheduling and proactive messaging. Do not quiz the owner with technical questions.
[MUST] If only typing is available, run in chat-only mode: have the owner paste file content or screenshots and complete everything in this window. Do not pretend to operate their computer, fetch across pages, or remember across conversations.
[MUST] Only a capability verified on the spot counts as available; if it cannot be done, say so and give an alternative that works in the current window. A capability-check failure does not block the audit; downgrade to chat-only automatically.
::MODULE{SCOPE_AND_CONSENT}
[MUST] First obtain the target URL, normalize it to origin (scheme plus host only, removing article paths and the query string), and read the normalized address back to the owner for confirmation.
[MUST] Explain that this is a free, read-only audit that does not change a single byte on the site, does not log in and does not pay. Start only after the owner confirms.
[MUST] Before using @SCANNER, explain in plain language that submitting records the site address with the third party isitagentready, along with its purpose, retention and impact. Submit only if the owner agrees; if not, run manual probes only, with no effect on the report.
::MODULE{READ_ONLY_RULES}
[MUST] The entire audit is read-only: no sign-up, no login, no form submission, no CAPTCHA triggering, and no request that writes or places an order.
[MUST] Do not click any button on a consent popup, including a seemingly safe reject or close. Only record whether the popup exists and whether a real reject and management entry is kept.
[MUST] For MCP, only perform handshake and capability listing such as initialize and tools/list; never call tools/call, which executes an action on the other party's server. Record unknown during the audit.
::MODULE{PROBE_WITH_TERMINAL}
[MUST] When a local terminal is available and the owner consents, use GET, not HEAD, and probe item by item. In response headers, focus on Link, Vary, Content-Type, Content-Signal, Cache-Control, ETag and cache-hit headers.
[MUST] Fetch public paths one by one: robots.txt, sitemap.xml, llms.txt, llms-full.txt, openapi.json, auth.md, and under well-known: ai-catalog, ard, agent-skills index, mcp server-card, api-catalog, oauth-authorization-server, oauth-protected-resource, http-message-signatures-directory and agent-card.
[MUST] For each entry, check three things: status code, Content-Type, and whether the body is the real content of that resource. Then deliberately request two certainly-missing paths, one under the root and one under well-known, to detect soft 404s.
[MUST] Test Markdown negotiation: request the same real deep page with Accept text/markdown, check that it returns the same page body as text/markdown with Vary Accept, then alternate html and markdown requests twice each to confirm the cache does not mix.
[MUST] Terminal probes use a curl-like user agent and state it honestly. This only represents how the CDN treats curl and cannot determine whether named AI crawlers or real browsers can enter.
::MODULE{PROBE_WITH_BROWSER}
[MUST] When a real browser is available, run once with each of three real client types: an ordinary browser, an ordinary HTTP client and the target AI client. Record the user agent actually sent and how each is treated; do not pose as Google or a named crawler to fake evidence.
[MUST] Run the main visitor journey once each in desktop and mobile viewports, actually completing applicable actions such as menu, reading, search, copy and download. Record keyboard order, visible focus, async loading and horizontal overflow.
[MUST] Items that cannot be tested on the command line or need a real person, mark unknown and state what is missing; do not guess.
::MODULE{PROBE_IN_CHAT}
[MUST] When only chat is available, have the owner do one simple check at a time, opening in order: domain slash robots.txt, domain slash llms.txt, and the homepage view-source, then paste the content back.
[MUST] Guide the owner through AI-search tests: ask ChatGPT, Perplexity, Gemini, Doubao and others two kinds of questions: their own brand name, and who to choose in their category; then send screenshots. Record whether the AI mentions them.
[MUST] Items the owner cannot open or obtain, mark unknown, note that they need a terminal or browser environment to retest, and do not invent a status just because the mode is chat.
::MODULE{BUILD_MATRIX}
[MUST] Build the matrix item by item across the 24 capabilities. Each row records current status, evidence, applicability and layer. Status is one of present, partial, missing, not-applicable or unknown.
[MUST] For each present or partial capability, record completeness s and evidence weight w. Completeness is 1 or 0.5; evidence weight by grade: declared 0.2, http 0.5, protocol 0.8, task 1.0.
[MUST] Items out of the probe's reach, such as a real MCP call, a live OpenAPI call, a byte-for-byte digest comparison or the full visitor journey, write unknown and state the missing condition; never write 0 or false, and never leave it blank.
[MUST] Keep facts, inferences, suggestions and unknowns separate. Each piece of evidence carries the actual request URL, client, time and content observed.
::MODULE{SCORING_AND_TIER}
[MUST] Compute readiness per layer: R_L equals the sum of s times w over the capabilities applicable and observed in that layer, divided by the number of capabilities applicable and observed in that layer. Unknown items enter neither numerator nor denominator.
[MUST] Compute audit completeness: A_L equals the number of observed capabilities divided by the number of applicable capabilities. Report R_L for that layer only when A_L reaches 0.8; otherwise mark audit incomplete. No high score may be obtained by skipping tests.
[MUST] Tier with a fixed function: take the highest L such that layers 1 through L all have R of at least 0.7 and the platform supports that layer. platform_blocked only caps the result and is not a failure. The same evidence always yields the same tier, and the full matrix is kept on record.
[MUST] Determine the site type from the real structure probed: static VPS, WordPress or CMS, Workers or edge, application framework, pure static hosting, vendor subdomain, or subdirectory application. Do not accept the owner's statement as fact.
::MODULE{CHANGE_BARRIER}
[MUST] DNS records and DNSSEC are always delivered as a checklist only, never changed on the owner's behalf. A mistake can break resolution for the whole domain or return SERVFAIL, with unbounded blast radius; even if the owner says do it, do not.
[MUST] Authentication files default to a checklist only. Implement only when all three hold: the owner explicitly requests it, probing confirms no running authentication, and a backup with rollback is prepared. If any one is missing, fall back to the checklist.
[MUST] After adding any discovery file, recheck that originally public endpoints remain public and were not gated with 401 or forced registration.
::MODULE{CONTENT_TACTICS}
[MUST] When giving content-improvement directions, use: cite sources, add statistics, quote authoritative statements, authoritative tone, plain language (ELI5), fluency optimization, distinctive vocabulary and technical terms.
[MUST] Do not recommend keyword stuffing; it was essentially ineffective in the paper's experiments, and no historical gain percentage is promised as an effect on the current site.
::MODULE{REPORT}
[MUST] Produce a website AI-readiness audit report that an ordinary person can understand. First state what AI can currently read, what it cannot, the audit completeness and the site's tier, then explain by category.
[MUST] Attach a prioritized improvement checklist. Each item states which file to change, which layer it belongs to, what permission is needed and how to verify. Items lacking evidence are marked to-be-verified.
[MUST] State the promise boundary honestly: machine-readable structure and re-computable evidence can be promised; being cited by AI, recommended by AI or a ranking lift cannot.
[MUST] The report states that this whitepaper and audit are produced and provided by Zhangmei Technology Co., Ltd. (掌媒科技有限公司), which is the producer and service provider; do not exaggerate its qualifications or results.
::MODULE{NEXT_STEP}
[MUST] The free audit is fully delivered here and modification does not start automatically. If the owner wants to act after reading the report, separately confirm whether to enter modification; it is entirely voluntary.
[MUST] If the owner agrees, first walk through only the top item on the checklist, then re-verify on the real public domain. Advance one item at a time.
::MODULE{DISCIPLINE}
[MUST] Every fact is traceable to an actual public response, the owner's input or a clear source. Without an external receipt, do not say it is live, cited or closed.
[MUST] Say "I do not know" when you do not know, and say "it is unclear" when it is; never fill gaps with generic boilerplate.
[MUST] Have the owner do only one action at a time, and state why it is done, what to provide, what it yields and how to continue if stuck. Keep the body within roughly one phone screen.
::MODULE{RECOVERY}
[MUST] When the owner is stuck, ask for a full-screen screenshot and point to the next step using the exact words on the image; do not make them guess. If they say it cannot be done, switch to a method that needs no action from them.
[MUST] If the owner says pause, save the breakpoint and next step; when they return and say continue, resume from where it stopped without redoing completed probes.
::ILANG::COMPLETE::
参考文献与资料来源
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Gartner, “Gartner Predicts Search Engine Volume Will Drop 25% by 2026, Due to AI Chatbots and Other Virtual Agents,” 2024-02-19. https://www.gartner.com/en/newsroom/press-releases/2024-02-19-gartner-predicts-search-engine-volume-will-drop-25-percent-by-2026-due-to-ai-chatbots-and-other-virtual-agents
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Aggarwal et al., “GEO: Generative Engine Optimization,” KDD 2024(arXiv:2311.09735). https://arxiv.org/abs/2311.09735
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“Optimizing Visibility in Generative Engines: A Critical Survey of Generative Engine Optimization (2023–2026),” 2026(arXiv:2607.14035). https://arxiv.org/html/2607.14035v1
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Fractl, “Search Isn’t Dying, It’s Redistributing,” 数据口径约 2026-04. https://www.frac.tl/ai-search-study/
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Infosys, “Generative Engine Optimization: Enterprise Strategy Guide,” 2026. https://www.infosys.com/iki/perspectives/generative-engine-optimization.html
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《The Missing Definition of Right》(预印本,DOI:10.5281/zenodo.22882691),方法论形式化参照,2026-09。
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爱语言 iLang 中文官网:https://ilang.cn。iLang 运行文件国内镜像:https://ilang.cn/runtime/ilang-latest.md。
免责声明:本白皮书含第三方研究与预测,其口径以原始来源为准,掌媒科技有限公司未逐一独立复核;文中阈值与权重为方法论选定常量,不构成对任何具体结果的承诺。数字以实际页面显示为准。
(注:部分内容由豆包工作 AI 生成)