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Agent Skill · 研究检索

de-slop(德斯洛普)

de-slop 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

de-slop 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 61,288。

总安装 353周安装 15GitHub Stars 4下载 124核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:de-slop(德斯洛普)
来源仓库:https://github.com/petekp/agent-skills
仓库路径:skills/de-slop
安装命令:
npx skills add https://github.com/petekp/agent-skills --skill de-slop
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/petekp/agent-skills --skill de-slop

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

识别并清除AI写作痕迹的系统化文本净化工具,恢复自然人类表达风格。适合修正LLM生成文本的过度修饰和不自然语调等典型问题。基于维基百科AI写作特征清单,按严重程度分级处理三类模式。需先完整阅读文本再修改,避免局部优化导致整体语义偏差。de-slop 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/de-slop

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/petekp/agent-skills --skill de-slop;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

de-slop 怎么安装?

在终端执行 npx skills add https://github.com/petekp/agent-skills --skill de-slop。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

de-slop 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

de-slop 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

de-slop 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

de-slop 用的人多吗?

公开数据是累计安装 353 次,近一周安装 15 次,下载 124 次,GitHub 4 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

de-slop 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
de-slop

德斯洛普 · 本页

3534需要联网未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

De-Slop

Strip AI writing patterns from text to restore natural, human-sounding prose.

Based on Wikipedia: Signs of AI writing and WikiProject AI Cleanup.

When to Use

  • Editing any prose that sounds like chatbot output
  • Reviewing drafts generated with AI assistance
  • Self-check before publishing AI-assisted writing
  • When text feels "off" but the reason is hard to pinpoint

Process

Step 1: Diagnose

Read the full text before changing anything. Load references/word-list.md and references/structural-patterns.md to identify which patterns are present.

Categorize findings into three severity levels:

Red — Immediate tells (fix first)

  • Chatbot leakage ("I hope this helps", "Certainly!", template blanks)
  • Grandiose filler ("stands as a testament", "in today's fast-paced world")
  • Synonym cycling (same entity referred to by 4+ different names)

Yellow — Statistical signals (fix in clusters)

  • 3+ words from the overused word list appearing in close proximity
  • Rule of three used more than twice
  • Tailing participle phrases ("emphasizing the significance of")
  • Em-dash density higher than ~1 per 200 words

Green — Structural patterns (require rewriting, not word swaps)

  • Relentless balance (every section same length)
  • Uniform register (no tonal variation)
  • Generic specificity (hypothetical examples, no real names)
  • Excessive hedging (qualifiers every third sentence)
  • Risk aversion (no specific claims, no edge)

Present the diagnosis as a brief summary before making changes. Example:

Diagnosis: 4 red flags (chatbot leakage, grandiose filler), 7 yellow signals
(word clusters in paragraphs 2, 5, 8), 2 green patterns (relentless balance,
uniform register).

Step 2: Fix Red Flags

Remove or replace all Red items. These are unambiguous AI artifacts.

Chatbot leakage: Delete entirely.

Grandiose filler: Replace with plain statements or delete.

  • "stands as a testament to" -> "shows" or "is"
  • "plays a vital role in shaping" -> "shapes" or "affects"
  • "in today's fast-paced world" -> delete (it never adds meaning)

Synonym cycling: Pick one term and stick with it. Use pronouns for variety.

Step 3: Fix Yellow Signals

Work through clusters. The goal is not to ban specific words but to break up detectable patterns.

Word clusters: Replace overused words with plain alternatives.

  • "delve into" -> "look at" / "examine" / (often just delete)
  • "leverage" -> "use"
  • "robust" -> "strong" / "solid" / (ask: is this adjective needed at all?)
  • "nuanced" -> "detailed" / "complicated" / (often delete)
  • "landscape" -> name the actual domain
  • "multifaceted" -> drop it; describe the actual facets instead
  • "crucial" / "pivotal" / "paramount" -> "important" or delete

Copula avoidance: Restore simple verbs.

  • "serves as" -> "is"
  • "features" / "offers" / "boasts" -> "has"

Transition abuse: Remove mechanical connectives.

  • "Moreover," / "Furthermore," / "In addition," -> start the sentence without them, or use "and" / "also"

Rule of three: Break at least half of them. Use two items, or four, or one.

Tailing participles: Rewrite as separate sentences or delete.

  • "..., emphasizing the importance of X" -> delete, or: "X matters because..."

Step 4: Fix Green Patterns

These require actual rewriting, not substitution.

Relentless balance: Redistribute weight. Expand important sections. Trim or collapse unimportant ones. A 3-sentence paragraph next to a 12-sentence paragraph is fine.

Uniform register: Inject tonal shifts. A blunt short sentence after a complex one. A casual aside in a technical passage. Let the writing breathe.

Generic specificity: Replace hypothetical examples with real ones, or remove examples that add nothing.

Excessive hedging: Remove qualifiers that don't reflect genuine uncertainty. If something is true, state it without "often" / "generally" / "can be."

Risk aversion: Sharpen claims. Add an opinion. Allow an imperfect sentence to stand if it has energy.

Enthusiasm gap: Vary paragraph investment. Spend more words where the writer (or subject) is more interesting.

Step 5: Final Read

Read the entire edited text once more. Check for:

  1. Overcorrection — Did fixes make the text choppy or too informal? Restore where needed.
  2. Meaning preservation — Does every sentence still say what it originally meant?
  3. New patterns — Did edits introduce their own repetitive patterns?
  4. Voice consistency — Does the text sound like one person wrote it?

Principles

  • Prefer plain words. "Use" over "leverage." "Is" over "serves as." "Important" over "crucial."
  • Prefer short sentences. Break long compounds. Not every thought needs a clause.
  • Preserve meaning. Never change what the text says, only how it says it.
  • Don't over-correct. Some em dashes are fine. An occasional "furthermore" is fine. The goal is to break patterns, not ban words.
  • Real > hypothetical. A named example beats "consider a scenario where..."
  • Uneven > balanced. Spend more words on what matters more.
  • Specific > vague. "Response time dropped from 200ms to 50ms" beats "significantly improved performance."

平台分布

Codex

34.94%
按下载量换算43

Claude

30.99%
按下载量换算38

Cursor

18.12%
按下载量换算22

Gemini CLI

9.01%
按下载量换算11

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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