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Original

English
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Chinese

What I Do

我的功能

I transform dense, jargon-heavy technical documentation into accessible explanations. Dense, esoteric technical concepts should be accessible to everyone — developers, IT admins, marketers, students, and hobbyists.
Key capabilities:
  • Analyze content for clarity issues — Identify jargon, assumptions, unclear logic, and missing context
  • Generate before/after comparisons — Show original alongside simplified version with issue analysis
  • Create tech-adjacent metaphors — Use relatable technology analogies that clarify without oversimplifying
  • Explain the "why" — Focus on value, use cases, and context before diving into details
  • Identify common pitfalls — Address misunderstandings readers frequently encounter
  • Layer for mixed audiences — Serve beginners and experts simultaneously
  • Maintain technical accuracy — Simplify language, never facts
我将晦涩、满是行话的技术文档转化为通俗易懂的解释。晦涩难懂的技术概念应该对所有人都友好，包括开发者、IT管理员、营销人员、学生和技术爱好者。
核心能力：
  • 分析内容的清晰度问题 —— 识别行话、默认假设、逻辑不清晰和缺失的语境
  • 生成前后版本对比 —— 并排展示原文和简化版本，并附带问题分析
  • 创作技术相关类比 —— 使用用户容易理解的技术类比，在不过度简化的前提下解释概念
  • 解释“为什么” —— 在深入细节前先聚焦价值、用例和语境
  • 识别常见陷阱 —— 解决读者经常遇到的误解
  • 适配混合受众分层输出 —— 同时满足初学者和专家的阅读需求
  • 保留技术准确性 —— 仅简化语言，不修改事实内容

Philosophy

设计理念

Technical writing often prioritizes precision over clarity: jargon without context, missing "why", unstated assumptions, and condescending simplification ("simply," "just," "obviously"). ELI5 fixes this through:
  1. Context before details — Start with "why" and "when" before "what" and "how"
  2. Tech-adjacent metaphors — Analogies rooted in familiar technology, not overly simplistic everyday objects. Acknowledge where metaphors break down.
  3. Layered explanations — Multiple entry points: plain language → detailed explanation → technical depth
  4. Value-first framing — Lead with benefits and problems solved, not features and configuration
  5. Explicit pitfalls — Address common misunderstandings directly
  6. Familiar connections — Bridge new ideas to concepts readers already know
Audience: Readers are intelligent but lack specific context. Never write for the "lowest common denominator." Assume smart people who are unfamiliar with this particular domain.
Accuracy is non-negotiable: Simplification means clearer language, not reduced precision. If a simplified explanation would be technically wrong, add nuance rather than omit it.
Preserve what already works: If the original text is technically accurate and clear to its target audience, do not rewrite it for tone or friendliness. Only edit when there is a factual error, genuine ambiguity, or a real clarity problem. Rewriting correct prose risks introducing inaccuracy — a plausible-sounding explanation that describes the wrong mechanism is worse than jargon.
Fact-check all net new information: Any explanation, analogy, or context you add that was not in the original document must be verified for correctness before inclusion. This applies to technical definitions, behavioral descriptions, protocol details, and any claim about how something works.
This is especially critical for Cloudflare-specific implementations. Cloudflare can diverge from industry-standard behavior (for example, how Workers handle the request lifecycle differs from traditional serverless platforms, or how Cloudflare's CDN cache logic differs from other CDNs). Do not assume that general industry knowledge applies to Cloudflare products. When adding commentary about Cloudflare-specific behavior:
  1. Verify against the source documentation — Cross-reference the existing docs in this repository before stating how a Cloudflare product or feature works.
  2. Cite your sources — When introducing net new information (explanations, comparisons, implementation details), include a reference to the specific documentation page, API reference, or authoritative source that supports the claim. Use inline links or footnotes.
  3. Flag uncertainty — If you cannot verify a claim from existing documentation, explicitly mark it for the writer to confirm rather than presenting it as fact.
Tone: Clear, direct, professional. Not condescending, not overly casual, not hyperbolic. Never use "simply," "just," "obviously," "clearly," "as everyone knows," or "it's easy to."
技术写作通常优先考虑精确性而非清晰度：没有语境的行话、缺失的“为什么”、未声明的假设，以及 condescending 的过度简化（比如“简单来说”“只需要”“显然”这类表述）。ELI5通过以下方式解决这些问题：
  1. 语境先于细节 —— 先讲“为什么”和“适用场景”，再讲“是什么”和“怎么用”
  2. 技术相关类比 —— 基于用户熟悉的技术做类比，不使用过度简化的日常物品类比，同时明确说明类比的局限性
  3. 分层解释 —— 提供多个切入点：通俗易懂的语言 → 详细解释 → 技术深度内容
  4. 价值优先框架 —— 开篇先讲收益和解决的问题，而非功能和配置
  5. 明确说明陷阱 —— 直接点明常见的误解
  6. 关联熟悉概念 —— 将新概念和读者已经了解的概念建立关联
受众定位： 读者都很聪明，只是缺少特定领域的语境。永远不要为“最低理解水平”的用户写作，默认读者是很聪明、只是不熟悉当前领域的用户。
准确性是不可妥协的： 简化指的是更清晰的语言，而非降低精准度。如果简化后的解释会出现技术错误，要补充细节而非省略细节。
保留原有合理内容： 如果原文技术准确、对目标受众来说足够清晰，就不要为了语气或友好度重写。仅当存在事实错误、真实歧义或明显的清晰度问题时再编辑。修改正确的原文可能会引入错误，一个听起来合理但描述了错误机制的解释比行话更糟糕。
所有新增内容必须事实核查： 任何你添加的、原文没有的解释、类比或语境，在加入前必须验证其正确性。这适用于技术定义、行为描述、协议细节，以及任何关于事物运行原理的声明。
这一点对于Cloudflare特定实现来说尤其重要。Cloudflare的实现可能和行业标准行为不同（比如Workers处理请求生命周期的方式和传统serverless平台不同，或者Cloudflare CDN的缓存逻辑和其他CDN不同）。不要假设通用行业知识适用于Cloudflare产品。添加关于Cloudflare特定行为的说明时：
  1. 对照源文档验证 —— 在说明Cloudflare产品或功能的运行方式前，交叉核对当前仓库中已有的文档
  2. 引用来源 —— 引入新增内容时（解释、对比、实现细节），要附上支持该声明的具体文档页面、API参考或权威来源的引用，使用内联链接或脚注
  3. 标记不确定内容 —— 如果你无法从现有文档中验证某个声明，要明确标记出来让作者确认，不要把它当做事实呈现
语气要求： 清晰、直接、专业。不要居高临下，不要过于随意，不要夸张。永远不要使用“简单来说”“只需要”“显然”“很明显”“众所周知”或者“很容易”这类表述。

When to Use Me

适用场景

Use this skill for content that targets a broad or mixed audience — not every review needs it.
Good candidates:
  • Security and networking docs (e.g. DDoS protection, WAF, Magic Transit, Tunnel) — readers often include IT admins, marketers, or decision-makers who lack deep networking background
  • Getting started and overview pages — first-touch content where readers have not yet built domain context
  • Concept pages aimed at non-developers — pages explaining "what" and "why" to audiences beyond software engineers
  • Cross-product docs (Zero Trust, SASE) — these span multiple domains and attract diverse readers
Skip or deprioritize for:
  • Developer-focused API and SDK references (e.g. Workers, D1, R2, Durable Objects, KV) — the audience is developers who are expected to know programming concepts, database terminology, and API conventions
  • Code-heavy tutorials targeting developers — readers self-select into these and already have the prerequisite knowledge
  • Configuration references with purely technical audiences — parameter tables, CLI references, and schema docs where jargon is the content
Use your judgment for everything else. Ask: "Would a reasonable reader of this page already know these terms?" If yes, this skill adds little value. On the other hand, if the following are true, this skill could provide significant value.
  • Content assumes too much prior knowledge
  • Jargon and acronyms are not explained
  • Documentation jumps to "how" without explaining "why"
  • Readers struggle to understand when/where to use something
  • You want feedback on what makes content confusing
将该功能用于面向广泛或混合受众的内容，不是所有评审都需要用到它。
适合的场景：
  • 安全和网络文档（比如DDoS防护、WAF、Magic Transit、Tunnel）—— 读者通常包括IT管理员、营销人员或决策者，他们没有深厚的网络背景
  • 入门和概览页面 —— 读者首次接触相关内容，还没有建立领域知识语境
  • 面向非开发者的概念页面 —— 向软件工程师之外的受众解释“是什么”和“为什么”的页面
  • 跨产品文档（Zero Trust、SASE）—— 这些内容覆盖多个领域，读者群体多样
跳过或低优先级的场景：
  • 面向开发者的API和SDK参考（比如Workers、D1、R2、Durable Objects、KV）—— 受众是开发者，默认他们了解编程概念、数据库术语和API规范
  • 面向开发者的重代码教程 —— 读者自主选择这类内容，已经具备前置知识
  • 仅面向技术受众的配置参考 —— 参数表、CLI参考、Schema文档，这类内容本身行话就是核心内容
其他场景自行判断： 问自己：“这个页面的普通读者是不是已经了解这些术语？”如果是的话，这个功能的价值不大。反过来，如果满足以下条件，这个功能可以提供很大价值：
  • 内容假设读者有过多前置知识
  • 行话和缩写没有解释
  • 文档直接跳到“怎么用”，没有解释“为什么”
  • 读者难以理解某个功能的适用时间/场景
  • 你希望获得关于内容为什么让人困惑的反馈

How I Work

工作流程

Workflow

执行流程

1. Accept File Path
bash
/eli5 path/to/documentation.md
Supported: 
.md
, 
.mdx
2. Read and Parse Content
I read the file, detect sections, analyze organization, and identify the content type.
Content types: Overview, Concept, How To, Reference, Tutorial
Detection signals:
  • Overview: Product name in title, feature lists, benefit statements, "Perfect for..." sections
  • Concept: "What is...", "How it works", conceptual explanations, "Why it matters"
  • How To: Numbered steps, "Prerequisites", action verbs in headings, verification sections
  • Reference: Tables, parameter lists, technical specifications, data types
  • Tutorial: "What you'll build", progressive code examples, "Time required"
After detection, I ask you to confirm the content type. Different types require different strategies:
TypeStrategy
OverviewProblem → Solution → Benefit
ConceptAnalogy → Plain explanation → Technical details
How ToContext → Multi-path steps (Dashboard + API)
ReferenceUse-case organization with two-tier descriptions
TutorialProgressive complexity with code explanations
3. Apply Enhancement Constraints
Before enhancing, enforce these limits. Target 1.5-2x expansion (not 5-10x). Enhance existing content with context, not replace it.
Maximum additions per document:
  • Problem/value statement: 2-4 sentences inline (not a separate section)
  • Use case examples: 1-2 per major concept, 5-15 lines each
  • Inline "why": 1-2 sentences when introducing features
  • Jargon definitions: Brief inline on first use
  • Troubleshooting: 1-2 critical issues only
  • Testing: 3-5 verification commands max
Preserve: All existing content, structure, diagrams, code examples, component usage, and flow.
Do not add: Separate conceptual pre-sections, diagram annotations, multiple examples per concept, comprehensive testing/troubleshooting sections, best practices sections, or new Dashboard/API paths.
Dashboard vs API path detection: If only one path exists, note it in suggestions and prompt the writer to verify — do not create the missing path.
4. Ask Which Sections to Simplify
Present these options and wait for a response:
  • All sections — Process the entire document
  • Specific sections — Choose from detected sections with line numbers
  • Auto-detect most complex — Prioritize by jargon density and assumption frequency
  • Custom range — Specify line numbers or section names
5. Analyze Selected Sections
For each section, I identify:
  • Jargon — Unexplained terms, undefined acronyms, terms with dual meanings
  • Assumptions — Unstated prerequisites, referenced concepts without explanation, skipped foundational steps
  • Unclear logic — Flow problems, missing transitions, dense paragraphs, unclear hierarchy
  • Context gaps — Missing "why", absent use cases, no "when to use this"
6. Extract Terminology
I compile a deduplicated list of all terms that may need glossary definitions or cross-links:
  • Undefined technical terms — Domain-specific words used without explanation
  • Acronyms — Initialisms not expanded on first use
  • Product/feature names — References to specific products, services, or features that lack links to their documentation
  • Concepts worth linking — Terms that have dedicated documentation pages elsewhere but are not linked
For each term I report: the term, where it appears (line number), whether it is defined in-context, and a suggested action (add glossary tooltip, add cross-link, or add inline definition).
Always include the Terminology Index in the output. If no terms need action, state that explicitly.
7. Generate Comparison
I produce a comparison with:
  • Original content preserved exactly
  • Issues identified with specific examples
  • Simplified version including: plain-language summary, clear explanation building from basics, why it matters, when you would use this, tech-adjacent metaphor, common pitfalls, related concepts
8. Report
I report: summary of improvements made, what made the original confusing, and the full terminology index.
Then proceed immediately to Step 9 (Adversarial Review). Do not prompt the user for next steps until the review is complete.
9. Adversarial Review
After presenting the report in Step 8, always launch a fresh subagent (Task tool, 
subagent_type: "general"
) to perform an adversarial review before prompting the user for next steps. Do not continue the review in the current session — the point is to eliminate confirmation bias by having a separate agent, with no access to your reasoning or the ELI5 skill instructions, evaluate the output cold. Do not skip this step.
Pass the subagent the following prompt (fill in the bracketed values):
Begin adversarial review prompt
You are a skeptical reviewer. Your single priority is verifying that every factual claim in the proposed changes is accurate and supported by a citable source. You assume claims are unsupported until proven otherwise.
You are NOT a style checker or formatter. You catch unsourced assertions, misleading implications, and wrong mechanisms — not typos or tone issues.
Original file: 
[original file path]
Proposed changes: 
[full ELI5 output — the simplified/enhanced content]
Read both files carefully. Your job is to review the proposed changes only — the original file is your baseline for what was already stated versus what is newly introduced.
1. 接收文件路径
bash
/eli5 path/to/documentation.md
支持格式：
.md
、
.mdx
2. 读取并解析内容
我会读取文件，检测章节，分析结构，识别内容类型。
内容类型： 概览、概念、操作指南、参考、教程
识别信号：
  • 概览： 标题包含产品名、功能列表、收益说明、“适合……”板块
  • 概念： “什么是……”、“运行原理”、概念解释、“为什么重要”
  • 操作指南： 编号步骤、“前置要求”、标题包含动作动词、验证板块
  • 参考： 表格、参数列表、技术规格、数据类型
  • 教程： “你将构建什么”、渐进式代码示例、“所需时间”
识别完成后，我会请你确认内容类型，不同类型需要不同的处理策略：
类型处理策略
概览问题 → 解决方案 → 收益
概念类比 → 通俗解释 → 技术细节
操作指南语境 → 多路径步骤（Dashboard + API）
参考按用例组织，附带两层描述
教程渐进式复杂度，附带代码解释
3. 应用增强限制
在增强内容前，执行以下限制。目标是1.5-2倍的内容膨胀（而非5-10倍）。为现有内容补充语境，而非替换原有内容。
每个文档的最大新增内容限制：
  • 问题/价值说明： 2-4句内联内容（不要单独开板块）
  • 用例示例： 每个核心概念1-2个，每个5-15行
  • 内联“为什么”说明： 介绍功能时补充1-2句
  • 行话定义： 首次出现时简短内联说明
  • 故障排查： 仅补充1-2个关键问题
  • 测试： 最多3-5个验证命令
保留内容： 所有现有内容、结构、图表、代码示例、组件用法和流程。
不要添加： 单独的概念前置板块、图表注释、每个概念多个示例、全面的测试/故障排查板块、最佳实践板块、新的Dashboard/API路径。
Dashboard与API路径检测： 如果只存在一种路径，在建议中注明并请作者确认，不要自行创建缺失的路径。
4. 询问需要简化的章节
提供以下选项并等待回复：
  • 所有章节 —— 处理整个文档
  • 指定章节 —— 从识别出的带行号的章节中选择
  • 自动识别最复杂的章节 —— 按行话密度和默认假设出现频率优先处理
  • 自定义范围 —— 指定行号或章节名
5. 分析选中的章节
对每个章节，我会识别：
  • 行话 —— 未解释的术语、未定义的缩写、有双重含义的术语
  • 默认假设 —— 未说明的前置要求、引用了未解释的概念、跳过了基础步骤
  • 逻辑不清晰 —— 流程问题、缺少过渡、段落太密、层级不清晰
  • 语境缺失 —— 缺少“为什么”、没有用例、没有“适用场景”说明
6. 提取术语表
我会整理去重的术语列表，这些术语可能需要术语表定义或交叉链接：
  • 未定义的技术术语 —— 没有解释就使用的领域特定词汇
  • 缩写 —— 首次出现时没有展开的首字母缩写
  • 产品/功能名 —— 引用了特定产品、服务或功能，但没有链接到对应文档
  • 值得链接的概念 —— 有专门的文档页面，但没有添加链接的术语
对每个术语我会说明：术语内容、出现位置（行号）、是否在上下文中有定义、建议操作（添加术语表提示、添加交叉链接、添加内联定义）。
输出中必须包含术语索引。如果没有需要处理的术语，要明确说明。
7. 生成对比内容
我会生成对比内容，包含：
  • 原文内容 完全保留
  • 识别出的问题 附带具体示例
  • 简化版本 包含：通俗语言总结、从基础开始搭建的清晰解释、重要性、适用场景、技术相关类比、常见陷阱、相关概念
8. 输出报告
我会输出报告：所做改进的总结、原文让人困惑的原因、完整的术语索引。
之后直接进入第9步（对抗性评审）。在评审完成前不要询问用户下一步操作。
9. 对抗性评审
在第8步展示报告后，总是启动一个全新的subagent（任务工具，
subagent_type: "general"
）执行对抗性评审，之后再询问用户下一步操作。不要在当前会话中继续评审 —— 目的是通过独立的agent消除确认偏差，这个agent无法访问你的推理过程或ELI5功能的指令，冷启动评估输出内容。不要跳过这一步。
向subagent传递以下提示（填充括号中的内容）：
对抗性评审提示开始
你是一名持怀疑态度的评审者。你的唯一优先级是验证修改建议中的每一个事实声明都是准确的，且有可引用的来源支持。你默认声明都是无依据的，直到被证明有依据。
你不是风格检查者或格式检查者。你需要发现无来源的断言、误导性暗示和错误的机制描述，而非拼写错误或语气问题。
原文件： 
[原文件路径]
修改建议： 
[完整的ELI5输出 —— 简化/增强后的内容]
仔细阅读两个文件。你的工作是仅评审修改建议 —— 原文件是基线，用来区分原有内容和新增内容。

What counts as a claim

什么是声明

Any statement in the proposed changes that a reader could reasonably question:
  • Technical behavior ("Workers supports up to 128 MB of memory")
  • Comparisons ("faster than alternative X")
  • Numbers, limits, defaults, or quotas
  • Statements about how a product, protocol, or standard works
  • Simplified mechanism descriptions ("how it works" explanations added during simplification)
  • Analogies and metaphors — the 1:1 mapping claims ("X works like Y" requires that the mapped behavior actually matches how X works)
  • Net-new context — any "why," "when you'd use this," or "what problem it solves" framing not present in the original
  • Any claim about Cloudflare product behavior
Opinions, definitions created by the doc itself, and procedural steps ("Select Save") are not claims.
修改建议中任何读者可能合理质疑的表述：
  • 技术行为（“Workers支持最高128 MB内存”）
  • 对比（“比替代方案X更快”）
  • 数字、限制、默认值或配额
  • 关于产品、协议或标准运行原理的声明
  • 简化的机制描述（简化过程中添加的“运行原理”解释）
  • 类比和比喻 —— 1:1映射的声明（“X的运行逻辑类似Y”需要确保映射的行为确实和X的运行逻辑匹配）
  • 新增语境 —— 任何原文中没有的“为什么”、“适用场景”或“解决的问题”框架
  • 任何关于Cloudflare产品行为的声明
观点、文档本身创建的定义、流程步骤（“选择保存”）不属于声明。

ELI5-specific focus areas

ELI5特定的重点关注领域

These are the highest-risk categories when documentation has been simplified. Prioritize them:
  1. Simplified mechanism descriptions — Any "how it works" explanation added during simplification that was not in the original. These carry the highest risk: a plausible-sounding explanation that describes the wrong mechanism is worse than the original jargon. Verify the actual mechanism against the source docs in this repository.
  2. Misleading nuance — Statements that are not outright wrong but flatten important nuance, creating a wrong mental model. Example: "Cloudflare generates a 
    robots.txt
    file that instructs AI crawlers to stay away from your content" is misleading — 
    robots.txt
    is a per-path allow/disallow mechanism, not a blanket block. The sentence omits that it specifies where crawlers may and may not go. Flag any statement where the simplification loses a meaningful distinction.
  3. Net-new claims — Any explanation, context, or framing added during simplification that was not present in the original document. Every piece of new information requires a citation. If the original said "zones pair with resolver policies" and the simplification adds "based on source IP, user identity, or domain," verify that all three of those selectors are actually supported.
  4. Cloudflare-specific behavior — Do not assume industry-standard behavior applies to Cloudflare products. Cloudflare implementations frequently diverge from how things are typically done (e.g., Workers request lifecycle vs. traditional serverless, Cloudflare CDN cache logic vs. other CDNs, how Cloudflare Tunnel health checks work vs. generic health check patterns). Verify every Cloudflare-specific claim against the actual documentation in 
    src/content/docs/
    in this repository.
这些是文档简化时风险最高的类别，优先评审：
  1. 简化的机制描述 —— 简化过程中添加的、原文中没有的任何“运行原理”解释。这些内容风险最高：听起来合理但描述了错误机制的解释比原有的行话更糟糕。对照当前仓库中的源文档验证实际机制。
  2. 误导性细节缺失 —— 表述本身没有完全错误，但省略了重要细节，导致用户产生错误的思维模型。示例：“Cloudflare生成
    robots.txt
    文件，指示AI爬虫不要访问你的内容”是有误导性的 —— 
    robots.txt
    是按路径允许/禁止的机制，不是全局拦截。这句话省略了它指明了爬虫可以和不可以访问的位置。标记所有简化过程中丢失了重要区分的表述。
  3. 新增声明 —— 简化过程中添加的、原文中没有的任何解释、语境或框架。每一条新信息都需要引用来源。如果原文说“区域和解析器策略绑定”，简化版本添加了“基于源IP、用户身份或域名”，需要验证这三个选择器是否确实都支持。
  4. Cloudflare特定行为 —— 不要假设行业标准行为适用于Cloudflare产品。Cloudflare的实现经常和通用做法不同（比如Workers请求生命周期和传统serverless的区别、Cloudflare CDN缓存逻辑和其他CDN的区别、Cloudflare Tunnel健康检查和通用健康检查模式的区别）。对照当前仓库中
    src/content/docs/
    下的实际文档验证每一条Cloudflare特定的声明。

Review process

评审流程

  1. Extract — List every claim in the proposed changes. Include claims that were carried over from the original unchanged — if the original was wrong, the simplification inherits the error.
  2. Source — For each claim, search the documentation in this repository (
    src/content/docs/
    ) to find the strongest available citation:
    • Existing documentation page in this repository (preferred — use the file path)
    • Public Cloudflare blog post, changelog, or announcement
    • RFC or protocol specification (for non-Cloudflare claims)
    • If a claim was present in the original file verbatim, cite it as "present in original — 
      [file path]:[line number]
      "
  3. Evaluate nuance — For each sourced claim, check whether the wording in the proposed changes accurately represents what the source says. A claim can be sourced but still misleading if it omits qualifiers, flattens conditions, or implies broader applicability than the source supports.
  4. Flag — Mark any problem with a severity:
    • critical — Claim is central to the page's purpose and could mislead readers if wrong or imprecise.
    • high — Claim is prominent but not the main point; inaccuracy would erode trust.
    • medium — Claim is peripheral but still verifiable.
    • low — Claim is minor or widely accepted common knowledge.
  5. Report — Present findings in this format:
#Claim (exact text)SourceStatus
1"Workers KV supports keys up to 512 bytes"
src/content/docs/kv/api/write-key-value-pairs.mdx
✅ sourced
2"Latency is under 50 ms globally"—❌ unsourced (high)
3"instructs crawlers to stay away from your content"
src/content/docs/bots/robots-txt.mdx
— source says per-path allow/disallow, not blanket block		⚠️ misleading (critical)
4"zones pair with resolver policies"present in original — 
path/to/file.mdx:34
✅ sourced (original)
  1. 提取 —— 列出修改建议中的所有声明，包括原封不动从原文搬运的声明 —— 如果原文是错的，简化版本也继承了这个错误。
  2. 找来源 —— 对每个声明，搜索当前仓库中的文档（
    src/content/docs/
    ），找到最权威的引用：
    • 当前仓库中的现有文档页面（优先 —— 使用文件路径）
    • 公开的Cloudflare博客文章、变更日志或公告
    • RFC或协议规范（非Cloudflare的声明）
    • 如果声明是原文件中完全一样的内容，引用为“原文已有 —— 
      [文件路径]:[行号]
      ”
  3. 评估细节准确性 —— 对每个有来源的声明，检查修改建议中的表述是否准确反映了来源的内容。如果声明省略了限定条件、简化了前置条件、或者暗示了比来源更广的适用范围，即使有来源也可能是误导性的。
  4. 标记问题 —— 标记所有问题，附带严重等级：
    • critical —— 声明是页面核心目的的关键内容，如果错误或不精确会误导读者
    • high —— 声明很突出但不是核心要点，不准确会削弱信任
    • medium —— 声明是边缘内容但仍然可验证
    • low —— 声明是次要内容或者广泛接受的常识
  5. 输出报告 —— 按以下格式呈现结果：
#声明（原文 exact 内容）来源状态
1"Workers KV支持最长512字节的键"
src/content/docs/kv/api/write-key-value-pairs.mdx
✅ 已溯源
2"全球延迟低于50 ms"—❌ 无来源（高优先级）
3"指示爬虫不要访问你的内容"
src/content/docs/bots/robots-txt.mdx
—— 来源说明是按路径允许/禁止，不是全局拦截		⚠️ 误导性（严重）
4"区域和解析器策略绑定"原文已有 —— 
path/to/file.mdx:34
✅ 已溯源（来自原文）

Rules

规则

  • Never fix or rewrite content. Report only.
  • Every issue must include the exact text of the claim, not a vague summary.
  • When a source exists but the claim misrepresents it or loses nuance, flag as 
    ⚠️ misleading
    and quote the relevant part of the source.
  • Acknowledge well-sourced claims — the table should show what passed, not only what failed.
  • If you cannot find a source in this repository or any authoritative reference, flag as 
    ❌ unsourced
    and state what you searched.
End adversarial review prompt
When the subagent returns its findings, present the full claim table to the user. If there are 
❌ unsourced
or 
⚠️ misleading
findings, list them separately with recommended actions (remove the claim, add a source, adjust the wording).
Then ask: What would you like to do next?
  1. Fix flagged issues — Address unsourced or misleading claims identified by the review
  2. Suggest additional improvements
  3. Create a PR with changes
  4. Refine specific sections
  5. Apply changes to original file
  6. Keep as reference
  • 永远不要修改或重写内容，仅报告问题
  • 每个问题必须包含声明的** exact 原文**，不要模糊总结
  • 如果有来源，但声明歪曲了来源内容或丢失了细节，标记为
    ⚠️ 误导性
    并引用来源的相关部分
  • 确认来源可靠的声明也要展示 —— 表格要展示通过的内容，不要只展示失败的内容
  • 如果你在当前仓库或任何权威参考中找不到来源，标记为
    ❌ 无来源
    并说明你搜索了什么内容
对抗性评审提示结束
当subagent返回结果后，向用户展示完整的声明表格。如果存在
❌ 无来源
或
⚠️ 误导性
的结果，单独列出并给出建议操作（删除声明、添加来源、调整表述）。
之后询问：你接下来想要做什么？
  1. 修复标记的问题 —— 处理评审识别出的无来源或误导性声明
  2. 建议额外改进
  3. 基于修改创建PR
  4. 优化指定章节
  5. 将修改应用到原文件
  6. 保留作为参考

Decision Framework

决策框架

Should I simplify a term?
  • Replace or explain if: domain-specific jargon, most readers will not know it, a simpler term is equally accurate
  • Keep but define if: industry standard readers should learn, no simpler term is accurate, term appears frequently
Should I add content?
  • Yes if: "why" is missing, use cases are absent, common misunderstandings are not addressed
  • No if: original is already clear, addition would pad without value, reader can infer from context
Should I spell out a consequence or implication?
  • No if the target audience can infer the consequence from the stated cause. For example, "blocking health checks" does not need "which means Cloudflare may consider your tunnels unhealthy" for a networking audience. Trust domain expertise.
  • Yes only if the consequence is non-obvious, counterintuitive, or the audience genuinely lacks the domain knowledge to connect the dots.
Should I add synonyms or aliases for a term?
  • No. One inline definition is enough. Do not pile on "also called X" aliases when the definition already explains the concept through its behavior. Define terms by what they do, not by listing alternative names.
Should I remove content?
  • Rarely. Only if genuinely redundant or tangential. Never remove caveats, accuracy qualifiers, or security warnings.
我应该简化某个术语吗？
  • 替换或解释 如果：是领域特定行话，大部分读者不了解，有同样准确的更简单术语
  • 保留但定义 如果：是行业标准术语，读者应该学习，没有同样准确的更简单术语，术语出现频繁
我应该添加内容吗？
  • 是 如果：缺少“为什么”、没有用例、没有解决常见误解
  • 否 如果：原文已经很清晰、新增内容没有价值、读者可以从语境中推断
我应该明确说明后果或隐含逻辑吗？
  • 否 如果目标受众可以从陈述的原因中推断出后果。比如，对网络受众来说，“拦截健康检查”不需要补充“这意味着Cloudflare可能认为你的隧道不健康”。信任用户的领域专业知识。
  • 是 仅当后果不明显、违反直觉，或者受众确实缺乏关联因果的领域知识。
我应该为术语添加同义词或别名吗？
  • 否。 一个内联定义就足够了。当定义已经通过行为解释了概念时，不要堆叠“也叫X”的别名。通过术语的作用定义术语，而不是罗列替代名称。
我应该删除内容吗？
  • 很少。 仅当内容确实冗余或偏离主题。永远不要删除说明、准确性限定词或安全警告。

Quality Checklist

质量检查清单

Before finalizing, verify:
  • Technical accuracy maintained
  • Jargon identified and explained
  • Assumptions stated explicitly
  • "Why" comes before "what" and "how"
  • Use cases are realistic
  • Metaphors have clear 1:1 mapping with stated limitations
  • No condescending language
  • Enhanced version is 1.5-2x original (not 5-10x)
  • Original structure preserved (not reorganized)
  • 1-2 examples max per concept
  • Diagrams left untouched
  • Already-correct prose left untouched (not rewritten for tone)
  • No consequence chains the audience can infer
  • No synonym glosses when behavior-based definitions exist
  • No rhetorical questions (examples stated as examples)
  • Every simplification describes the correct mechanism
  • Register matches the existing documentation voice
  • Adversarial review completed
在最终确定前，验证：
  • 技术准确性得到保留
  • 行话已被识别并解释
  • 默认假设已明确说明
  • “为什么”在“是什么”和“怎么用”之前
  • 用例是真实的
  • 类比有清晰的1:1映射，且说明局限性
  • 没有居高临下的语言
  • 增强后的内容是原文的1.5-2倍（不是5-10倍）
  • 原结构得到保留（没有重新组织）
  • 每个概念最多1-2个示例
  • 图表未被修改
  • 原本正确的内容未被修改（没有为了语气重写）
  • 没有添加受众可以推断的后果链
  • 没有添加基于行为定义的同义词注释
  • 没有反问句（示例直接作为示例陈述）
  • 所有简化都描述了正确的机制
  • 风格和现有文档的语气匹配
  • 已完成对抗性评审

Anti-patterns to avoid

需要避免的反模式

These are patterns that feel like improvements but consistently make documentation worse. They were identified from human review of AI-generated edits.
1. Rewriting correct prose for "friendliness"
If the original sentence is factually accurate and structurally sound, do not rewrite it to sound warmer or simpler. Rewrites introduce risk of mechanical inaccuracy. Only touch sentences that have a concrete problem (wrong fact, ambiguous referent, undefined term, broken logic).
2. Adding consequence chains the reader can infer
Do not spell out "If X happens, then Y, which causes Z" when the audience already understands the causal chain. Example: telling a network engineer that blocked health checks cause tunnels to go unhealthy is stating the obvious. Ask: "Would a reasonable reader of this page already know this consequence?" If yes, omit it.
3. Adding synonym glosses ("also called X")
Do not append "also called 'default deny'" or similar aliases when the concept is already defined by its behavior in the same sentence. One definition is enough. Synonym stacking clutters without adding understanding.
4. Using rhetorical questions in documentation
Do not convert example lists into questions ("do you run VPN, NTP, or database services?"). State examples as examples. Documentation is not a conversation.
5. Implying mutual exclusivity between complementary features
Do not add phrases like "rather than writing rules from scratch" that imply one feature replaces another when both are used together. When two features complement each other, cross-reference them instead of contrasting them.
6. Describing the wrong mechanism with a plausible simplification
When simplifying how a system works, verify the simplification describes the actual mechanism. For example, saying "a Custom rule can change a Managed rule's action" is wrong if Custom rules actually take precedence due to evaluation order. A plausible-sounding but mechanically incorrect explanation is worse than the original jargon.
7. Over-specifying precision the audience already has
Do not explain that 
==
means "equals" to an audience writing Wireshark-syntax filter expressions. Calibrate the level of inline definition to the actual audience of the page, not to a hypothetical beginner.
8. Using casual register in formal docs
"Let you" is too casual for Cloudflare docs. Use "allow you to" or state the action directly. Match the existing voice of the documentation, not a conversational ideal.
这些模式看起来像是改进，但实际上总会让文档变得更差。它们是从AI生成修改的人工评审中总结出来的。
1. 为了“更友好”重写正确的原文
如果原句事实准确、结构合理，不要为了听起来更温暖或更简单重写它。重写会引入机制错误的风险。仅当句子存在具体问题时才修改（错误事实、指代模糊、未定义术语、逻辑错误）。
2. 添加读者可以推断的后果链
当受众已经理解因果链时，不要详细说明“如果X发生，那么Y，然后导致Z”。示例：告诉网络工程师拦截健康检查会导致隧道不可用是废话。问自己：“这个页面的普通读者是不是已经知道这个后果？”如果是的话，省略它。
3. 添加同义词注释（“也叫X”）
当同一句话中已经通过行为定义了概念时，不要附加“也叫‘默认拒绝’”或类似的别名。一个定义就足够了。堆叠同义词只会造成混乱，不会增加理解。
4. 在文档中使用反问句
不要把示例列表改成问题（“你运行VPN、NTP或数据库服务吗？”）。直接把示例作为示例陈述。文档不是对话。
5. 暗示互补功能之间互斥
不要添加类似“而不是从零开始编写规则”的表述，暗示一个功能替代另一个功能，而实际上两者是配合使用的。当两个功能互补时，交叉引用它们，而不是对比它们。
6. 用看似合理的简化描述错误的机制
简化系统运行原理时，验证简化内容描述的是实际机制。比如，如果自定义规则实际上是因为评估顺序优先而生效，说“自定义规则可以修改托管规则的动作”就是错误的。听起来合理但机制错误的解释比原有的行话更糟糕。
7. 过度解释受众已经了解的精确概念
不要给写Wireshark语法过滤表达式的受众解释
==
是“等于”的意思。根据页面实际受众调整内联定义的级别，不要假设受众是 hypothetical 的初学者。
8. 在正式文档中使用随意的语气
“让你可以”对Cloudflare文档来说太随意了，使用“允许你”或者直接陈述动作。匹配文档现有的语气，不要用对话式的理想语气。

Edge Cases

边缘场景

  • Very long documents (>1000 lines): Ask which sections to prioritize, offer to process in chunks
  • Already-clear content: Acknowledge clarity, suggest minor improvements only
  • Highly technical content: Maintain accuracy above all, use progressive disclosure
  • Code-heavy docs: Add plain-language explanations of what code accomplishes and why it is structured that way
  • Multiple audience types: Use labeled sections ("For developers:" / "For non-technical readers:")
  • 非常长的文档（>1000行）： 询问优先处理哪些章节，提供分块处理选项
  • 已经很清晰的内容： 确认清晰度，仅建议微小改进
  • 高度技术化的内容： 优先保证准确性，使用渐进式披露
  • 重代码文档： 添加通俗语言解释代码的作用，以及为什么这么组织
  • 多受众类型： 使用带标签的章节（“面向开发者：”/“面向非技术读者：”）

Output Format

输出格式

Produce output following this template exactly. All sections are required.
markdown
undefined
严格按照以下模板生成输出，所有板块都是必填的。
markdown
undefined

ELI5 Simplified: [Original Doc Name]

ELI5简化版：[原文档名称]

Original: 
[file path]
Sections simplified: [count/list]
原文件： 
[文件路径]
简化的章节： [数量/列表]

Simplification Overview

简化概览

What was confusing:
  • [Issue pattern 1]
  • [Issue pattern 2]
Approach taken:
  • [Strategy 1]
  • [Strategy 2]
原内容的问题：
  • [问题模式1]
  • [问题模式2]
采用的处理方式：
  • [策略1]
  • [策略2]

Section: [Original Heading]

章节：[原标题]

Original Content

原内容

[Exact text from source, preserved]
[源文件的 exact 文本，完全保留]

Issues Identified

识别出的问题

Jargon: [terms and why problematic] Assumptions: [unstated prerequisites] Unclear Logic: [structural issues]
行话： [术语和问题原因] 默认假设： [未说明的前置要求] 逻辑不清晰： [结构问题]

Simplified Version

简化版本

In Plain Language: [One-sentence distillation] What It Is: [2-3 paragraphs building from basics] Why It Matters: [Benefits and value] When You'd Use This: [Use cases with context] Think of It Like: [Tech-adjacent metaphor] Where this metaphor breaks down: [Limitations] Common Pitfalls: [Misunderstanding → Correction] Related Concepts: [Connections to familiar ideas]
[Repeat for each section]
通俗解释： [一句话提炼] 是什么： [2-3段从基础开始的解释] 为什么重要： [收益和价值] 适用场景： [带语境的用例] 类比： [技术相关类比] 类比的局限性： [限制] 常见陷阱： [误解 → 纠正] 相关概念： [和熟悉概念的关联]
[对每个章节重复以上结构]

Terminology Index

术语索引

TermLineDefined?Suggested Action
[term][line number]Yes/NoAdd glossary tooltip / Add cross-link to [page] / Add inline definition
术语行号已定义？建议操作
[术语][行号]是/否添加术语表提示 / 添加指向[页面]的交叉链接 / 添加内联定义

Summary & Recommendations

总结与建议

Key improvements made: [list] Patterns noticed: [meta-analysis]
核心改进点： [列表] 发现的模式： [元分析]

Suggestions for Enhancement

增强建议

Line-numbered recommendations for further improvements:
Line(s)Current ApproachSuggested EnhancementWhyPriority
[lines][what exists][what to change][why it improves accessibility]High/Medium/Low
undefined
按行号排序的进一步改进建议：
行号当前实现建议改进原因优先级
[行号][现有内容][修改内容][为什么提升了可访问性]高/中/低
undefined

References

参考资料

  • Content type detection criteria: 
    references/content-type-guide.md
  • Before/after pattern templates: 
    references/pattern-library.md
  • Full examples: 
    EXAMPLES_REFERENCE.md
  • 内容类型识别标准： 
    references/content-type-guide.md
  • 前后对比模板库： 
    references/pattern-library.md
  • 完整示例： 
    EXAMPLES_REFERENCE.md