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prose-distill散文精华

Agent Skill

prose-distill 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。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

1,032

周安装

43

GitHub Stars

28

下载量

344
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:prose-distill(散文精华)
来源仓库:https://github.com/laurigates/claude-plugins
仓库路径:skills/prose-distill
安装命令:
npx skills add https://github.com/laurigates/claude-plugins --skill prose-distill
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/laurigates/claude-plugins --skill prose-distill

简介

prose-distill 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。

  • 适用于代码审查、项目进度跟踪和协作事项梳理等开发协作场景。
  • 支持从指定仓库提取 Issue、PR 等协作数据,并生成结构化摘要。
  • 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。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

/prose:distill

Distill verbose text to its concentrated essence — the art of compression without loss.

When to Use This Skill

Use this skill when...Use something else when...
Text is wordy and needs tighteningYou need to change tone or register (use prose-tone)
Redundant phrases need removingYou need to restructure document flow (use prose-structure)
User says "condense", "tighten", "shorten"You need to adapt for a specific audience (use prose-audience)
Preserving all meaning while reducing lengthSummarizing (lossy) rather than distilling (lossless)

Core Principles

Distillation is lossless compression of natural language. Every sentence in the output must preserve the information content of the input. The goal is approaching the Shannon limit of the message — boiling off redundancy to leave concentrated meaning.

The Hierarchy of Cuts

Apply in this order. Each level removes less essential material:

  1. Redundant phrases — saying the same thing twice in different words
  2. Filler words — "actually", "basically", "essentially", "really", "very", "quite", "rather"
  3. Hedge words — "somewhat", "arguably", "it could be said that", "in a sense"
  4. Throat-clearing — opening phrases that delay the point ("It is worth noting that", "It should be mentioned that")
  5. Nominalizations — noun forms where verbs are stronger ("make a decision" → "decide", "perform an analysis" → "analyze")
  6. Passive constructions — where active is clearer and shorter
  7. Prepositional chains — "the result of the analysis of the data" → "the data analysis result"
  8. Weak verbs + adverbs — replace with a single precise verb ("moved quickly" → "darted")

What to Preserve

  • Technical precision and domain terminology
  • Necessary qualifications and nuance
  • Logical structure and argument flow
  • Voice and character (distill the style, don't flatten it)
  • Specific details, numbers, names, references

Parameters

Parse $ARGUMENTS:

  • If text is provided inline, distill it directly
  • If a file path is provided, read and distill the file contents
  • If no arguments, ask the user for text to distill

Execution

Execute this distillation workflow:

Step 1: Assess the input

Read the provided text. Identify:

  • Approximate word count
  • Density of redundancy (light, moderate, heavy)
  • Whether the text has a distinctive voice worth preserving

Step 2: Apply the hierarchy of cuts

Work through the text applying cuts in order from the hierarchy above. For each sentence:

  1. Can two sentences merge into one without losing meaning?
  2. Are there redundant phrases?
  3. Can filler/hedge words be removed?
  4. Can nominalizations become verbs?
  5. Can passive become active without changing emphasis?
  6. Can prepositional chains compress?

Step 3: Verify lossless compression

Compare the distilled version against the original. Confirm:

  • No information was lost
  • No meaning was altered
  • Qualifications and nuance survived
  • The logical flow is intact

Step 4: Present the result

Output the distilled text. Follow with a brief summary:

---
Original: ~N words
Distilled: ~N words
Reduction: ~N%

If any meaning was ambiguous and required interpretation, note it.

Examples

Filler and hedge removal

Before: "It is essentially worth noting that the system actually performs quite well in basically all of the scenarios that were tested."

After: "The system performs well in all tested scenarios."

Nominalization to verb

Before: "We performed an investigation into the cause of the failure and made a determination that the configuration was incorrect."

After: "We investigated the failure and determined the configuration was incorrect."

Redundancy elimination

Before: "The end result of this process is that each and every individual component is tested and verified to ensure and confirm that it meets the required specifications and standards."

After: "This process verifies each component meets the required specifications."

Preserving necessary nuance

Before: "While the approach generally works well in most common scenarios, there are some edge cases, particularly those involving concurrent access patterns, where the current implementation may exhibit degraded performance characteristics."

After: "The approach works well in common scenarios but may degrade under concurrent access patterns."

Note: "may" is preserved — it's a genuine qualification, not a hedge.

Agentic Optimizations

ContextApproach
Short text (< 100 words)Distill inline, show before/after
Medium text (100-500 words)Distill in sections, show word count reduction
Long text or file (> 500 words)Read file, distill, write result, show stats
Preserving technical accuracyFlag any cuts that might alter technical meaning

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

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

按任务关键词查找相关 Skills

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

36.29%
按下载量换算125

Claude

27.64%
按下载量换算95

Cursor

18.91%
按下载量换算65

Gemini CLI

8.37%
按下载量换算29

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。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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