review-ai-writing

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Original

English

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Translation

Chinese

AI Writing Detection for Developer Text

面向开发者文本的AI写作检测

Detect patterns characteristic of AI-generated text in developer artifacts. These patterns reduce trust, add noise, and obscure meaning.

检测开发者制品中AI生成文本的典型模式。这些模式会降低可信度、增加冗余并模糊语义。

Pattern Categories

模式分类

Category	Reference	Key Signals
Content	`references/content-patterns.md`	Promotional language, vague authority, formulaic structure, synthetic openers
Vocabulary	`references/vocabulary-patterns.md`	AI word tiers, copula avoidance, rhetorical devices, synonym cycling, commit inflation
Formatting	`references/formatting-patterns.md`	Boldface overuse, emoji decoration, heading restatement
Communication	`references/communication-patterns.md`	Chat leaks, cutoff disclaimers, sycophantic tone, apologetic errors
Filler	`references/filler-patterns.md`	Filler phrases, excessive hedging, generic conclusions
Code Docs	`references/code-docs-patterns.md`	Tautological docstrings, narrating obvious code, "This noun verbs", exhaustive enumeration

分类	参考文档	关键信号
内容	`references/content-patterns.md`	宣传性语言、模糊的权威性、公式化结构、合成式开篇
词汇	`references/vocabulary-patterns.md`	AI词汇层级、避免系动词、修辞手段、同义词循环、提交信息冗余
格式	`references/formatting-patterns.md`	过度使用粗体、表情符号装饰、标题重复表述
沟通风格	`references/communication-patterns.md`	聊天痕迹、截断声明、谄媚语气、致歉式错误表述
冗余内容	`references/filler-patterns.md`	冗余短语、过度模糊表述、通用结论
代码文档	`references/code-docs-patterns.md`	同义反复的文档字符串、赘述明显代码、"This noun verbs"结构、详尽枚举

Scope

检测范围

Scan these artifact types:

Artifact	File Patterns	Notes
Markdown docs	`*.md`	READMEs, guides, changelogs
Docstrings	`.py` , `.ts` , `.js` , `.go` , `.swift` , `.rs` , `.java` , `.kt` , `.rb` , `.ex`	Language-specific docstring formats
Code comments	Same as docstrings	Inline and block comments
Commit messages	`git log` output	Use synthetic path `git:commit:<sha>`
PR descriptions	GitHub PR body	Use synthetic path `git:pr:<number>`

扫描以下制品类型：

制品类型	文件模式	说明
Markdown文档	`*.md`	README、指南、变更日志
文档字符串	`.py` , `.ts` , `.js` , `.go` , `.swift` , `.rs` , `.java` , `.kt` , `.rb` , `.ex`	各语言特定的文档字符串格式
代码注释	与文档字符串相同	行内注释和块注释
提交信息	`git log` 输出	使用合成路径 `git:commit:<sha>`
PR描述	GitHub PR正文	使用合成路径 `git:pr:<number>`

What NOT to Scan

不扫描的内容

Generated code (lock files, compiled output, vendor directories)
Third-party content (copied license text, vendored docs)
Code itself (variable names, string literals used programmatically)
Test fixtures and mock data

生成代码（锁文件、编译输出、供应商目录）
第三方内容（复制的许可证文本、供应商提供的文档）
代码本身（变量名、程序中使用的字符串字面量）
测试夹具与模拟数据

Detection Rules

检测规则

High-Confidence Signals (Always Flag)

高置信度信号（始终标记）

These patterns are strong indicators of AI-generated text:

Chat leaks — "Certainly!", "I'd be happy to", "Great question!", "Here's" as sentence opener
Cutoff disclaimers — "As of my last update", "I cannot guarantee"
High-signal AI vocabulary — delve, utilize (as "use"), whilst, harnessing, paradigm, synergy
"This noun verbs" in docstrings — "This function calculates", "This method returns"
Synthetic openers — "In today's fast-paced", "In the world of"
Sycophantic code comments — "Excellent approach!", "Great implementation!"

这些模式是AI生成文本的强烈指标：

聊天痕迹 —— "Certainly!"、"I'd be happy to"、"Great question!"、以"Here's"开头的句子
截断声明 —— "As of my last update"、"I cannot guarantee"
高信号AI词汇 —— delve、utilize（用作"use"）、whilst、harnessing、paradigm、synergy
文档字符串中的"This noun verbs"结构 —— "This function calculates"、"This method returns"
合成式开篇 —— "In today's fast-paced"、"In the world of"
谄媚的代码注释 —— "Excellent approach!"、"Great implementation!"

Medium-Confidence Signals (Flag in Context)

中置信度信号（结合上下文标记）

Flag when 2+ appear together or pattern is repeated:

Low-signal AI vocabulary clusters — 3+ words from the low-signal list in one section
Formulaic structure — Rigid intro-body-conclusion in a README section
Heading restatement — First sentence after heading restates the heading
Excessive hedging — "might potentially", "could possibly", "it seems like it may"
Synonym cycling — Same concept called different names within one section
Boldface overuse — More than 30% of sentences contain bold text

当出现2个及以上或模式重复时标记：

低信号AI词汇集群 —— 同一区域出现3个及以上低信号列表中的词汇
公式化结构 —— README章节中严格遵循引言-正文-结论结构
标题重复表述 —— 标题后的第一句重复标题内容
过度模糊表述 —— "might potentially"、"could possibly"、"it seems like it may"
同义词循环 —— 同一概念在一个章节内被冠以不同名称
过度使用粗体 —— 超过30%的句子包含粗体文本

Low-Confidence Signals (Note Only)

低置信度信号（仅作记录）

Mention but don't flag as issues:

Emoji in technical docs — May be intentional project style
Filler phrases — Some are common in human writing too
Generic conclusions — May be appropriate for summary sections
Commit inflation — Some teams prefer descriptive commits

仅提及但不标记为问题：

技术文档中的表情符号 —— 可能是项目的有意风格
冗余短语 —— 部分冗余短语在人类写作中也很常见
通用结论 —— 可能适合摘要章节
提交信息冗余 —— 部分团队偏好描述性提交信息

False Positive Warnings

误报警告

Do NOT flag these as AI-generated:

Pattern	Why It's Valid
"Ensure" in security docs	Standard term for security requirements
"Comprehensive" in test coverage discussion	Accurate technical descriptor
Formal tone in API reference docs	Expected register for reference material
"Leverage" in financial/business domain code	Domain-specific meaning, not AI filler
Bold formatting in CLI help text	Standard convention
Structured intro paragraphs in RFCs/ADRs	Expected format for these document types
"This module provides" in Python `__init__.py`	Idiomatic Python module docstring
Rhetorical questions in blog posts	Appropriate for informal content

以下模式请勿标记为AI生成内容：

模式	有效原因
安全文档中的"Ensure"	安全要求的标准术语
测试覆盖率讨论中的"Comprehensive"	准确的技术描述符
API参考文档中的正式语气	参考资料的预期语体
金融/业务领域代码中的"Leverage"	领域特定含义，非AI冗余词汇
CLI帮助文本中的粗体格式	标准惯例
RFC/ADR中的结构化引言段落	此类文档的预期格式
Python `__init__.py` 中的"This module provides"	Python模块文档字符串的惯用写法
博客文章中的修辞疑问句	适合非正式内容

Integration

集成

With

beagle-core:review-verification-protocol

与

beagle-core:review-verification-protocol

集成

Before reporting any finding:

Read the surrounding context (full paragraph or function)
Confirm the pattern is AI-characteristic, not just formal writing
Check if the project has established conventions that match the pattern
Verify the suggestion improves clarity without changing meaning

在报告任何发现前：

阅读周围上下文（完整段落或函数）
确认该模式是AI文本的典型特征，而非仅仅是正式写作
检查项目是否有与该模式匹配的既定惯例
验证建议在不改变语义的前提下提升了清晰度

With

beagle-core:llm-artifacts-detection

与

beagle-core:llm-artifacts-detection

集成

Code-level patterns (tautological docstrings, obvious comments) overlap with

llm-artifacts-detection

's style criteria. When both skills are loaded:

```
review-ai-writing
```
focuses on writing style (how it reads)
```
llm-artifacts-detection
```
focuses on code artifacts (whether it should exist at all)
If
```
.beagle/llm-artifacts-review.json
```
exists, skip findings already captured there

代码层面的模式（同义反复的文档字符串、冗余注释）与

llm-artifacts-detection

的风格标准存在重叠。当同时加载两个技能时：

```
review-ai-writing
```
聚焦于写作风格（文本的可读性）
```
llm-artifacts-detection
```
聚焦于代码制品（内容是否必要）
若存在
```
.beagle/llm-artifacts-review.json
```
，则跳过已在其中记录的发现

Output Format

输出格式

Report each finding as:

text

[FILE:LINE] ISSUE_TITLE
- Category: content | vocabulary | formatting | communication | filler | code_docs
- Type: specific_pattern_name
- Original: "the problematic text"
- Suggestion: "the improved text" or "delete"
- Risk: Low | Medium
- Fix Safety: Safe | Needs review

每个发现按以下格式报告：

text

[FILE:LINE] ISSUE_TITLE
- Category: content | vocabulary | formatting | communication | filler | code_docs
- Type: specific_pattern_name
- Original: \"问题文本\"
- Suggestion: \"改进后的文本\" 或 \"delete\"
- Risk: Low | Medium
- Fix Safety: Safe | Needs review

Risk Levels

风险等级

Low — Filler phrases, obvious comments, emoji. Removing improves clarity with no meaning change.
Medium — Vocabulary swaps, structural changes, docstring rewrites. Meaning could shift if done carelessly.

低 —— 冗余短语、明显的注释、表情符号。删除可提升清晰度且不改变语义。
中 —— 词汇替换、结构调整、文档字符串重写。若处理不当可能改变语义。

Fix Safety

修复安全性

Safe — Mechanical replacement or deletion. No judgment needed.
Needs review — Rewrite requires understanding context. Human should verify the replacement preserves intent.

安全 —— 机械替换或删除。无需判断。
需审查 —— 重写需要理解上下文。需人工验证替换是否保留了原意。

review-ai-writing

Original

Translation

AI Writing Detection for Developer Text

面向开发者文本的AI写作检测

Pattern Categories

模式分类

Scope

检测范围

What NOT to Scan

不扫描的内容

Detection Rules

检测规则

High-Confidence Signals (Always Flag)

高置信度信号（始终标记）

Medium-Confidence Signals (Flag in Context)

中置信度信号（结合上下文标记）

Low-Confidence Signals (Note Only)

低置信度信号（仅作记录）

False Positive Warnings

误报警告

Integration

集成

With
`beagle-core:review-verification-protocol`

与
`beagle-core:review-verification-protocol`
集成

With
`beagle-core:llm-artifacts-detection`

与
`beagle-core:llm-artifacts-detection`
集成

Output Format

输出格式

Risk Levels

风险等级

Fix Safety

修复安全性

review-ai-writing

Original

Translation

AI Writing Detection for Developer Text

面向开发者文本的AI写作检测

Pattern Categories

模式分类

Scope

检测范围

What NOT to Scan

不扫描的内容

Detection Rules

检测规则

High-Confidence Signals (Always Flag)

高置信度信号（始终标记）

Medium-Confidence Signals (Flag in Context)

中置信度信号（结合上下文标记）

Low-Confidence Signals (Note Only)

低置信度信号（仅作记录）

False Positive Warnings

误报警告

Integration

集成

With beagle-core:review-verification-protocol

与beagle-core:review-verification-protocol集成

With beagle-core:llm-artifacts-detection

与beagle-core:llm-artifacts-detection集成

Output Format

输出格式

Risk Levels

风险等级

Fix Safety

修复安全性

With
`beagle-core:review-verification-protocol`

与
`beagle-core:review-verification-protocol`
集成

With
`beagle-core:llm-artifacts-detection`

与
`beagle-core:llm-artifacts-detection`
集成