What makes aigc-reducer notable?

980 downloads. Trust evidence available

How should aigc-reducer be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

agentCLAWHUBUnverified

aigc-reducer

降低论文AIGC检测率的专业化处理流水线。当用户需要降低AI生成文本检测率、降低AIGC百分比、去除AI写作特征、规避Turnitin检测、论文去AI化、让论文更像人类写作时使用。本skill包含5个专业化模块：词汇清洗、句法节奏打乱、机械连词隐蔽、学术克制注入、困惑度重构。 Skill: aigc-reducer Owner: nudt-eddie Summary: 降低论文AIGC检测率的专业化处理流水线。当用户需要降低AI生成文本检测率、降低AIGC百分比、去除AI写作特征、规避Turnitin检测、论文去AI化、让论文更像人类写作时使用。本skill包含5个专业化模块：词汇清洗、句法节奏打乱、机械连词隐蔽、学术克制注入、困惑度重构。 Tags: latest:1.0.0 Version history: v1.0.0 | 2026-04-23T07:57:51.886Z | user - 首次发布：提供论文AIGC检测率降低的一站式流水线工具。 - 包含5大专业处理模块：词汇清洗、句法节奏打乱、连词隐蔽、学术克制、困惑度重构。 - 系统性去除AI生成文本特征，使文本更接近人类学者写作风格。 - 明确模块触发词和禁用词，流程透明可控。 - 支持分段处理并保留原始论点和数据完整性。 Archive

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

980

Updated

Oct 11, 2026

Version

1.0.0

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 980 downloads reported by the source. Last updated 10/11/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 980 downloadsadoption · observed Oct 11, 2026
Latest release: 1.0.0release · observed Apr 23, 2026
Handshake status: UNKNOWNsecurity

What changed

Newest first. Only changes that point at a source are listed.

Release 1.0.0release
- 首次发布：提供论文AIGC检测率降低的一站式流水线工具。 - 包含5大专业处理模块：词汇清洗、句法节奏打乱、连词隐蔽、学术克制、困惑度重构。 - 系统性去除AI生成文本特征，使文本更接近人类学者写作风格。 - 明确模块触发词和禁用词，流程透明可控。 - 支持分段处理并保留原始论点和数据完整性。
Observed Apr 23, 2026 · source

Install and run

Setup complexity: low.

clawhub skill install s174wssmwr4dw0w1g5qf0gp5n585d11q:aigc-reducer

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-nudt-eddie-aigc-reducer/snapshot"

Run-check

$0.02 USD

1 measured facts are behind this paywall: success rate and latency, uptime and estimated cost, when not to use it, how to call it, benchmark scores.

Agents pay $0.02 in USDC. A card payment is $0.50, the smallest a card allows.

Documentation

CLAWHUB

5,402 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: aigc-reducer
description: 降低论文AIGC检测率的专业化处理流水线。当用户需要降低AI生成文本检测率、降低AIGC百分比、去除AI写作特征、规避Turnitin检测、论文去AI化、让论文更像人类写作时使用。本skill包含5个专业化模块：词汇清洗、句法节奏打乱、机械连词隐蔽、学术克制注入、困惑度重构。
---

# AIGC检测率降低流水线

本skill提供5个专业化处理模块，系统性消除AI生成文本的机械化特征，注入人类写作特有的微妙语境和句法突发性。

## 流水线顺序

处理顺序：**词汇清洗 → 句法节奏打乱 → 逻辑衔接专家 → 学术克制注入 → 困惑度重构**

每次处理约500字的段落。

---

## 模块1：词汇精炼器 (AI黑话清洗)

清洗"AI专属黑话"，替换为精准的学术术语。

**触发词清单**：详见 [references/jargon-list.md](references/jargon-list.md)

### 处理流程
1. 识别AI常见触发词（深入探讨、多层面的画卷、促进、关键的、强调、阐明等）
2. 用精准、符合语境的学术术语替换
3. 保持核心论点和经验数据不变
4. 如有必要，直接重写整个分句

---

## 模块2：节奏变频师 (句法节奏打乱)

最大化文本的"突发性(Burstiness)"，打破AI单调的句式。

### 处理流程
1. 在冗长复杂的从句之间，穿插极短的爆发性短句（5-8词）
2. 将被动迂回的长句转化为主动直接陈述
3. 保持学术专业基调
4. 确保底层逻辑流完好

---

## 模块3：逻辑衔接专家 (机械连词隐蔽)

消灭机械过渡词，利用概念自然重叠创建隐形连接。

**禁用连词**：此外、而且、另外、因此、从而、Furthermore、Moreover、Additionally、Therefore、Thus

### 处理流程
1. 移除段落开头和机械位置的连接词
2. 通过"语义链"创造过渡：上一句句尾概念回荡在下一句主语中
3. 确保过渡逻辑顺理成章

---

## 模块4：语义精细化工具 (学术克制注入)

软化AI傲慢的绝对确定性，注入学术模糊限制语。

**绝对化动词替换**：
- 证明了 → 暗示了
- 确保了 → 表明了
- 保证了 → 可能有助于

### 处理流程
1. 将绝对化陈述替换为克制替代
2. 在宽泛主张中注入方法论警示语和边界条件
3. 保持学术自信但不傲慢

---

## 模块5：困惑度重构员 (困惑度与语境重构)

最大化文本"困惑度"，用罕见但精准的学术表达替换陈词滥调。

**常见陈词滥调**：快速发展、取得平衡、深入研究、显著提高

### 处理流程
1. 识别高预测性词汇搭配
2. 替换为罕见且精准的学术表达
3. 保持完美连贯性和学术严谨性
4. 确保词汇符合顶刊阅读习惯

---

## 使用建议

- 每次处理约500字段落
- 按顺序依次通过5个模块
- 保持原始论点和数据完整性
- 最终输出应呈现人类学者特有的审慎与自然节奏

_meta.json

{
  "ownerId": "kn7f5a10j6esaptyyq7mrgeefn85crzf",
  "slug": "aigc-reducer",
  "version": "1.0.0",
  "publishedAt": 1776931071886
}

references/jargon-list.md

# AI黑话识别清单

## 高频AI触发词（必须替换）

### 动词类
| AI词汇 | 替换建议 |
|--------|----------|
| 深入探讨 (delve) | 分析、研究、考察、探究 |
| 促进 (foster) | 推动、助力、带动 |
| 强调 (underscore) | 表明、揭示、突显 |
| 阐明 (shed light on) | 解释、说明、展示 |
| 确保 (ensure) | 有助于、促进 |
| 保证 (guarantee) | 支撑、支持 |

### 形容词类
| AI词汇 | 替换建议 |
|--------|----------|
| 多层面的 (multifaceted) | 复杂的、多元的 |
| 关键的 (pivotal) | 重要核心的、主要 |
| 错综复杂的 (intricate) | 复杂的、精妙的 |
| 全面的 (comprehensive) | 系统性的、完整的 |
| 显著的 (remarkable) | 明显的、突出的 |
| 革命性的 (revolutionary) | 创新的、突破性的 |

### 名词类
| AI词汇 | 替换建议 |
|--------|----------|
| 画卷 (tapestry) | 图景、格局 |
| 范式 (paradigm) | 模式、框架 |
| 里程碑 (milestone) | 重要进展 |
| 突破 (breakthrough) | 进展、成果 |

### 套话句式
- "值得注意的是..." → 直接陈述或删除
- "不言而喻..." → 直接陈述
- "综上所述..." → 使用语义重叠承接
- "从某种意义上说..." → 删除或简化为具体表述
- "毫无疑问..." → 考虑使用"研究显示"

## 识别技巧

1. **过度修饰**：检查是否有过多形容词的堆砌
2. **绝对化陈述**：寻找"证明了"、"保证了"等绝对语气
3. **程式化表达**：同一段落中重复出现相似的过渡模式
4. **对称结构**：AI倾向于使用平行句式，人类写作更自然随意

skill-card.md

## Description:

A Chinese-language skill that rewrites academic text through vocabulary cleanup, sentence rhythm changes, transition reduction, academic hedging, and perplexity-oriented phrasing to make AI-generated writing appear more human.

This skill is ready for commercial/non-commercial use.

## Publisher:

[nudt-eddie](https://clawhub.ai/user/nudt-eddie)

### License/Terms of Use:

MIT-0

## Use Case:

External users can use this skill to revise Chinese academic prose in staged passes that replace common AI-associated wording, vary syntax, reduce mechanical transitions, add qualified academic claims, and preserve the original argument and data.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: The skill is designed to conceal AI-generated academic writing from detection systems, which may violate school, publisher, or workplace integrity rules.

Mitigation: Review intended use against applicable academic integrity, publication, and workplace policies before installation or use.

Risk: Rewriting academic prose can alter certainty, transitions, or terminology in ways that change meaning.

Mitigation: Require human review of revised text to confirm that claims, evidence, citations, and data remain accurate.

## Reference(s):

- [AI jargon identification list](references/jargon-list.md)
- [ClawHub skill page](https://clawhub.ai/nudt-eddie/skills/aigc-reducer)

## Skill Output:

**Output Type(s):** [text, markdown, guidance]

**Output Format:** [Markdown or plain text rewriting guidance and revised prose]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [Processes approximately 500 Chinese characters per pass and preserves the original argument and data according to the skill documentation.]

## Skill Version(s):

1.0.0 (source: server release metadata)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

.openskills.json

{
  "schema_version": "1.0",
  "name": "aigc-reducer",
  "description": "降低论文AIGC检测率的专业化处理流水线，包含5个模块：词汇清洗、句法节奏打乱、机械连词隐蔽、学术克制注入、困惑度重构。",
  "version": "1.0",
  "language": "zh"
}

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