Agent skill

Reducing Aigc Detection

by telagod in telagod/code-abyss

Systematically reduce AIGC detection rates in academic papers (Chinese/English).

MITAuto-check: notes

Install Reducing Aigc Detection

skills CLI
$ npx skills add telagod/code-abyss --skill reducing-aigc-detection -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install telagod/code-abyss reducing-aigc-detection --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/telagod/code-abyss.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/reducing-aigc-detection .claude/skills/reducing-aigc-detection && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
reducing-aigc-detection
GitHub stars
244
Token cost
~1.1k tokens
SKILL.md length
281 words
Files
3 (incl. references)
Skills in repo
38
Repo updated
First seen
Licence
MIT

At a glance

Systematically reduce AIGC detection rates in academic papers (Chinese/English).

  • Works in 5 steps: 侦察 → 分级定策 → 改写执行 → …
  • SKILL.md covers 何时使用, 核心原理, 执行流程 and 反模式(明确不能做的事), plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Reducing Aigc Detection is an agent skill from telagod/code-abyss. Systematically reduce AIGC detection rates in academic papers (Chinese/English). Analyzes detection reports, identifies high-impact sections, applies multi-layer rewriting strategies preserving formatting/footnotes, and verifies results. Supports 维普/知网/Turnitin platforms.

Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including reference files (for example `references/ai-trigger-words.md` and `references/platform-profiles.md`).

The repository describes itself as: Give your AI coding agent a personality. Composable persona + style + skills for Claude Code, Codex, Gemini CLI & OpenClaw. Ships Tech Persona Card v1.0 spec. The licence is MIT.

Example prompts

  • “/reducing-aigc-detection”

Requirements

  • Python 3
  • Pre-approved tools (allowed-tools): Bash, Read, Write, Edit, Agent, WebSearch, WebFetch

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. 侦察
  2. 分级定策
  3. 改写执行
  4. 一致性检查
  5. 验证

What it can do on your machine

Read from SKILL.md and the folder at commit 2544577. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Bash
    • Read
    • Write
    • Edit
    • Agent
    • WebSearch
    • WebFetch

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Reducing Aigc Detection loads about 1.1k tokens when it runs, and up to ~2.8k if it reads all its reference files. Until then it costs about 74 tokens; SKILL.md has 281 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~74
When it runs · the whole SKILL.md, loaded when a task matches
~1.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Bash, Read, Write, Edit, Agent, WebSearch, WebFetch

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from telagod/code-abyss at commit 2544577, republished under its MIT licence (© telagod). 281 words, ~1,093 tokens.

Download SKILL.mdSave it as .claude/skills/reducing-aigc-detection/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
reducing-aigc-detection
description
Systematically reduce AIGC detection rates in academic papers (Chinese/English). Analyzes detection reports, identifies high-impact sections, applies multi-layer rewriting strategies preserving formatting/footnotes, and verifies results. Supports 维普/知网/Turnitin platforms.
allowed-tools
Bash, Read, Write, Edit, Agent, WebSearch, WebFetch
user-invocable
true
argument-hint
<docx-path> [--report <pdf-path>] [--platform weipu|cnki|turnitin] [--target <percentage>]
aliases
reduce-aigc, aigc-fix, lower-aigc

降AIGC · reducing-aigc-detection

检测器追的是统计均匀性,反检测的本质是重新注入人类写作天然的 variance 和 imperfection。

何时使用

场景使用说明
AIGC 检测报告显示高于红线YES核心场景
论文提交前预防性降 AIYES不需要检测报告
已有 AI 辅助写作的论文需要人性化YES最佳实践
纯人工写作但误判率高YES可针对性微调
想批量处理多份文件NO每篇需要定制化处理

核心原理

检测器三板斧
指标含义AI 文本特征人类文本特征
Perplexity(困惑度)文本可预测性极低(<30)中高(60-120)
Burstiness(突发性)句长变化幅度极低,句长均匀高,长短交替
Token 概率分布high-prob token 占比>85%<70%
平台差异
平台特殊机制关键应对
维普章节加权(摘要 1.8x,引言/结论 1.5x);拼接预警(风格断层 +10-15%)优先改摘要;全文风格一致
知网3.0+ 分析论证深度曲线;4.0 标注"结构工整度过高"制造浅→深螺旋节奏
Turnitin2025.8 可识别 humanizer 工具痕迹不用洗稿工具,手动改写

执行流程

Phase 0: 侦察
  1. 读取检测报告 PDF(如有),提取各章节 AIGC 占比
  2. 如无报告,通读全文预判高风险段落
  3. 按 AIGC 率 x 章节权重 排序,确定改写优先级
优先级 = AIGC率 × 章节字数 × 平台权重系数
Phase 1: 分级定策
AIGC 率策略改动幅度
>80%整段重写保留核心论点,彻底换表达
40-80%重点改写换骨架、注入个人经验、打碎并列
20-40%局部手术替换 AI 特征词、打断过渡链、加短句
<20%微调或不动仅修复明显 AI 模板词
Phase 2: 改写执行
改写层级(按效果排序)

第一层:结构层(降 60-70%,最高优先)

  • 消灭「N个方面:第一…第二…第三…」并列模板
  • 打破「背景→分析→结论」标准三段论
  • 制造论证深度不均匀:核心论点厚写,次要一笔带过
  • 长短句交替:穿插 5-10 字短句与 30-40 字长句
  • 加入自我修正轨迹:「最初以为…后来发现…」

第二层:词汇层(降 10-15%,配合第一层)

中文 AI 高频触发词黑名单(必须替换或删除):

值得注意的是 / 综上所述 / 不可否认 / 首先…其次…最后
研究表明 / 结果显示 / 此外 / 总之 / 不仅…而且
主要体现在N个方面 / 具有重要意义 / 发挥着重要作用
在…方面 / 与此同时 / 一方面…另一方面

英文 AI 高频触发词黑名单:

delve(s) / furthermore / moreover / it is important to note
comprehensive / multifaceted / nuanced / landscape / underscores
in conclusion / this report hopes to / integrates...with
The X section explains/introduces/presents/summarizes (mechanical parallelism)

替换策略:不是换同义词,是换句式。「研究表明X」→ 引具体作者年份样本量。

第三层:内容注入(最难被检测)

  • 加入个人研究细节、田野观察、实验意外
  • 引用对立观点的具体文献
  • 补充具体数据、数字、表格
  • 增加口语化学术表达碎片
技术执行注意事项

docx 编辑策略:

段落类型编辑方式理由
无脚注/无特殊格式replace_full_para() — 设 Run 0 新文本,清空其余安全快速
含脚注引用 [N]Run 级替换 — 仅改非 superscript runs保留脚注
含 bold/italic 段中格式Run 级替换或 XML 层编辑保留格式标记
含图表引用仅改文字 runs,不动图表 XML防止引用断裂

python-docx 段落替换模板:

python
def replace_full_para(para, new_text):
    """整段替换,保留首 run 格式。仅用于无脚注段落。"""
    if not para.runs:
        return
    para.runs[0].text = new_text
    for r in para.runs[1:]:
        r.text = ''

Run 级替换模板(保留脚注):

python
# 先扫描确认哪些 runs 是脚注(superscript=True)
for j, r in enumerate(para.runs):
    if r.font.superscript:
        continue  # 不动
    if '目标文本片段' in r.text:
        r.text = r.text.replace('目标文本片段', '替换文本')
Phase 3: 一致性检查

改写后必须检查:

  1. 风格一致性 — 改过的段落与未改段落语气一致(维普拼接预警)
  2. 脚注完整性 — 所有 [N] 引用保留且对应正确
  3. 字数变化 — 改后总字数与原文偏差 <15%
  4. 信息完整性 — 原文核心论点、数据、引用全部保留
Phase 4: 验证
  1. 提取改后文本,人工通读
  2. 建议用户去检测平台复检
  3. 如仍超标,进入第二轮精修(优先改权重最高的剩余段落)

反模式(明确不能做的事)

操作为什么无效
仅做同义词替换句式结构未变,知网 3.0 直接穿透,仅降 5-10%
只改标红段落不管上下文风格断层触发维普拼接预警,反而加分
用 humanizer/洗稿工具Turnitin 2025.8 已可识别工具痕迹
AI 改 AI 不换 prompt 策略输出仍在 AI 分布内,等于原地踏步
插入特殊 Unicode 字符所有主流平台已修补
中→英→中互翻仅降 15%,且翻译腔本身可能触发
越改越正式/书面越规整越像 AI
先降查重后降 AI顺序错误——查重修改会引入新 AI 特征

高校红线参考

层级典型要求
C9 / 顶尖 985<10-15%
211 院校15-25%
普通本科20-30%
硕博论文<=10%

终极检查清单

  • 全文句长分布有足够变化(不是均匀 15-25 字)
  • AI 特征词已替换或删除(对照黑名单)
  • 消灭了所有「N个方面:第一…第二…第三…」并列模板
  • 摘要已改为非标准四段式(维普权重 1.8x)
  • 注入了具体数据 / 真实案例 / 个人观察
  • 全文风格一致(无拼接断层)
  • 论证深度有自然的浅→深节奏变化
  • 脚注引用完整保留
  • 文档格式无损(字体、行距、页眉)
  • 改后总字数偏差 <15%

输出骨架

【战场态势】各章节 AIGC 率 + 优先级排序
【改写方案】每章节策略(整段重写 / 局部手术 / 微调 / 不动)
【执行结果】改动清单 + 脚注保留确认
【预估效果】各章节预估改后 AIGC 率
【复检建议】下一步行动

© telagod, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (references) in skills/reducing-aigc-detection of telagod/code-abyss.

  • SKILL.md
  • references/ai-trigger-words.md
  • references/platform-profiles.md

Open the folder on GitHubat commit 2544577

Compare with similar skills

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Reducing Aigc Detection compared with similar skills
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Reducing Aigc Detection this skilltelagod/code-abyss244—~1.1kAutomated safety check: NotesMIT
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Paper to Chinese Patent DrafterYuan1z0825/nature-skills46k1 repos~1.1kAutomated safety check: PassApache-2.0
Systematic Literature Review Builderbytedance/deer-flow83k2 repos~4.3kAutomated safety check: PassMIT
Papers Skillsickn33/agentic-awesome-skills47k1 repos~2.1kAutomated safety check: PassMIT
Humanize Chinesesickn33/agentic-awesome-skills47k2 repos~1.3kAutomated safety check: PassMIT

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Questions about Reducing Aigc Detection

What does Reducing Aigc Detection do?

Systematically reduce AIGC detection rates in academic papers (Chinese/English). Reducing Aigc Detection is an agent skill from telagod/code-abyss. Systematically reduce AIGC detection rates in academic papers (Chinese/English).

How do I install Reducing Aigc Detection in Claude Code?

Run `npx skills add telagod/code-abyss --skill reducing-aigc-detection -a claude-code`. Or copy the skill folder (skills/reducing-aigc-detection in telagod/code-abyss) into .claude/skills/reducing-aigc-detection in your project. Claude Code loads it when a task matches its description.

How do I install Reducing Aigc Detection in Codex?

Run `npx skills add telagod/code-abyss --skill reducing-aigc-detection -a codex`. Or copy the skill folder (skills/reducing-aigc-detection in telagod/code-abyss) into .agents/skills/reducing-aigc-detection in your project. Codex loads it when a task matches its description.

Can I use Reducing Aigc Detection in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add telagod/code-abyss --skill reducing-aigc-detection -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/reducing-aigc-detection, .gemini/skills/reducing-aigc-detection, .github/skills/reducing-aigc-detection and .opencode/skills/reducing-aigc-detection in your project.

What does Reducing Aigc Detection need to run?

SKILL.md names no scripts, command-line tools or credentials: Reducing Aigc Detection is instructions for the agent only. Our summary lists: Python 3. Its frontmatter pre-approves these tools: Bash, Read, Write, Edit, Agent, WebSearch, WebFetch.

Does Reducing Aigc Detection access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Reducing Aigc Detection safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Reducing Aigc Detection use?

Reducing Aigc Detection is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Reducing Aigc Detection use?

About 1.1k tokens (SKILL.md is roughly 4.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.7k tokens, read only when the agent opens those files.

What are the alternatives to Reducing Aigc Detection?

Skills that share tags, products or a category with Reducing Aigc Detection: AIGC Detection Rate Reducer (xiaofenggan01/aigc-reduce, 612 stars), Paper to Chinese Patent Drafter (Yuan1z0825/nature-skills, 46k stars), Systematic Literature Review Builder (bytedance/deer-flow, 83k stars) and Papers Skill (sickn33/agentic-awesome-skills, 47k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Reducing Aigc Detection?

telagod (a GitHub user) maintains it in telagod/code-abyss, which has 244 GitHub stars. The repository holds 38 skills in this directory. The repository was last updated on July 19, 2026.

Source: telagod/code-abyss on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.