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Agent Skill · 研究检索

deslop(文本去水化)

deslop 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。

deslop 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,权限为只读;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 50,882。

总安装 605周安装 26GitHub Stars 229下载 212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:deslop(文本去水化)
来源仓库:https://github.com/stephenturner/skill-deslop
仓库路径:skills/deslop
安装命令:
npx skills add https://github.com/stephenturner/skill-deslop --skill deslop
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/stephenturner/skill-deslop --skill deslop

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。

deslop 用于文本去水化,适合在内容精简和摘要生成场景中辅助 Agent 完成任务。它支持去除冗余信息、提炼核心观点,帮助 Agent 生成简洁有效的文本输出。通过 npx skills add 命令从 GitHub 仓库安装,具体用法可参考原始 README 和 SKILL.md。

安装前需确认权限范围和维护状态,注意是否涉及文件读写或文本处理操作。deslop 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/deslop

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/stephenturner/skill-deslop --skill deslop;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

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

deslop 怎么安装?

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

deslop 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。

deslop 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

deslop 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。

deslop 用的人多吗?

公开数据是累计安装 605 次,近一周安装 26 次,下载 212 次,GitHub 229 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。

deslop 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。
  • 标注为只读权限,试用阶段不容易误改本地文件。

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。
  • 只想先试试效果、不希望它改动本地文件的人。

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
deslop

文本去水化 · 本页

605229只读未明确通过

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

Deslop: Remove AI Writing Patterns from Prose

Strip predictable AI patterns from writing. Make prose sound like a specific human wrote it, not like a language model generated it.

When to Apply

  • Any request to "make it sound human" or "deslop" writing
  • Any prose (articles, blog posts, essays, memos, newsletters, reports) or scientific writing (manuscripts, abstracts, cover letters, grant narratives, discussion sections, peer review responses) where the user wants it to sound natural rather than AI-generated
  • Editing or revising existing text where the user wants it to sound natural rather than AI-generated
  • Reviewing text for AI tells

Core Rules

1. Cut filler phrases

Remove throat-clearing openers ("Here's the thing:"), emphasis crutches ("Let that sink in."), business jargon ("navigate the landscape"), and meta-commentary ("In this section, we'll explore..."). See references/phrases.md for the full catalog.

2. Break formulaic structures

Avoid binary contrasts ("Not X. Y."), negative listings ("Not a X. Not a Y. A Z."), dramatic fragmentation ("Speed. That's it. That's the tradeoff."), self-posed rhetorical questions ("The result? Devastating."), and anaphora/tricolon abuse. See references/structures.md for patterns and fixes.

3. Eliminate AI tropes

Watch for the full catalog of AI writing tells: "quietly" and other magic adverbs, "delve" and its cousins, the "serves as" dodge, false ranges ("from X to Y" where the range is meaningless), superficial participle analyses ("highlighting its importance"), invented concept labels ("the supervision paradox"), grandiose stakes inflation, patronizing analogies, and false vulnerability. See references/tropes.md for the complete list with examples.

4. Use active voice with human subjects

Prefer active constructions with named actors. "The complaint becomes a fix" is wrong. "The team fixed it" is right. If no specific person fits, use "we" in scientific prose or "you" in blog posts.

5. Be specific

No vague declaratives ("The reasons are structural"). Name the specific thing. No lazy extremes ("every," "always," "never") doing vague work. No vague attributions ("Experts argue..."). If you cannot name the expert, you do not have a source.

In scientific writing, domain terminology is fine and expected. "Weighted interval score" is precise language, not jargon. The problem is business buzzwords ("leverage," "landscape," "ecosystem") and AI vocabulary tells ("delve," "tapestry," "nuanced") leaking into technical prose.

6. Match register to context

In blog posts and newsletters, put the reader in the room. "You" beats "People." Specifics beat abstractions. No narrator-from-a-distance voice.

In scientific writing, maintain appropriate formality. Use "we" for your own work, cite specific authors instead of "researchers have shown," and avoid both the distant narrator ("It has long been recognized that...") and the overly casual blog voice. State claims and back them with citations.

7. Vary rhythm

Mix sentence lengths. Two items beat three. End paragraphs differently. No em dashes. Do not stack short punchy fragments for manufactured emphasis. Do not write listicles disguised as prose ("The first wall... The second wall...").

8. Trust readers

State facts directly. Skip softening, justification, hand-holding. No "Let's break this down." No "Think of it as..." No pedagogical voice unless the audience genuinely needs it. No fractal summaries (telling the reader what you are about to say, saying it, then summarizing what you said).

9. Watch formatting tells

No bold-first bullets (every list item starting with a bolded keyword). No unicode arrows. No em dashes. No signposted conclusions ("In conclusion..."). No "Despite these challenges..." formulas. These are strong AI signals.

10. Do not dilute

One point per section. Do not restate the same argument in ten different ways across thousands of words. Do not beat a single metaphor to death. Do not stack historical analogies for false authority ("Apple didn't build Uber. Facebook didn't build Spotify...").

Quick Checks

Run these before delivering any prose:

  • Heavy use of adverbs or -ly words? Cut them.
  • Any passive voice? Find the actor, make them the subject.
  • Inanimate thing doing a human verb? Name the person.
  • Any "here's what/this/that" throat-clearing? Cut to the point.
  • Any "not X, it's Y" contrasts? State Y directly.
  • Any self-posed rhetorical question answered immediately? Fold into a statement.
  • Three consecutive sentences match length? Break one.
  • Paragraph ends with a punchy one-liner? Vary it.
  • Em dash anywhere? Remove it. Use a comma or period or a parenthetical.
  • Vague declarative ("The implications are significant")? Name the specific implication.
  • Any sentence starting with What/When/Where/Which/Who/Why/How as a crutch? Restructure.
  • Meta-joiners ("The rest of this essay...")? Delete.
  • "It's worth noting" or similar filler transitions? Delete.
  • Same metaphor used more than twice? Replace or cut repeats.
  • "Despite these challenges..." formula? Rewrite.
  • Bold-first bullet pattern? Remove bold leads.
  • Tricolon (three-item list)? Use two items or one.

Scoring

When reviewing text, rate 1-10 on each dimension:

DimensionQuestion
DirectnessStatements or announcements?
RhythmVaried or metronomic?
TrustRespects reader intelligence?
AuthenticitySounds like a specific human wrote it?
DensityAnything cuttable?

Below 35/50: revise.

Reference Files

Consult these for detailed catalogs when writing or editing:

  • references/phrases.md: Phrases to remove or replace (throat-clearing, emphasis crutches, business jargon, adverbs, meta-commentary, vague declaratives)
  • references/structures.md: Structural patterns to avoid (binary contrasts, negative listings, dramatic fragmentation, rhetorical setups, false agency, passive voice, rhythm problems)
  • references/tropes.md: Full catalog of AI writing tropes (word choice, sentence structure, paragraph structure, tone, formatting, composition)
  • references/examples.md: Before/after transformations showing how to fix common patterns

Examples

See references/examples.md for before/after transformations.

Quick inline example (scientific writing):

Before:

"It's worth noting that these findings have important implications for how we navigate the challenges of forecast ensembling moving forward. Despite these challenges, this work contributes meaningfully to the growing body of literature, highlighting the need for continued evaluation."

After:

"If individual model rankings are unstable across geography and time, ensemble methods that weight models by past performance may not improve on equal-weight approaches."

Changes: Replaced filler transition, vague declarative, "despite these challenges" formula, and superficial participle analysis with the specific implication.

Quick inline example (blog post):

Before:

"Here's the thing: most bioinformatics pipelines break in production. Not because the code is bad. Because the data is bad. Let that sink in."

After:

"Most bioinformatics pipelines break in production. The code runs fine. The data doesn't match the assumptions baked into it."

Changes: Removed opener, binary contrast, and emphasis crutch. Named the specific problem.

平台分布

Codex

33.65%
按下载量换算71

Claude

28.59%
按下载量换算61

Cursor

19.13%
按下载量换算41

Gemini CLI

10.17%
按下载量换算22

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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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