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deslop文本去水化

Agent Skill

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。

总安装

605

周安装

26

GitHub Stars

229

下载量

212
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

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

简介

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。

SKILL.md

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.

适合场景

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02

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需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

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能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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

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