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compress-prompt压缩提示

Agent Skill

用于辅助提示词、系统指令、Agent 行为约束和工作流模板的整理。它适合让 Agent 规范任务边界、统一输出格式、拆分操作步骤或优化提示词可复用性。使用时需要保留真实业务约束,不要把示例当硬规则;涉及自动执行、外部工具或高风险操作时,应在提示词中明确确认步骤、权限边界和失败处理方式。

总安装

873

周安装

36

GitHub Stars

12

下载量

285
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:compress-prompt(压缩提示)
来源仓库:https://github.com/jbrukh/skills
仓库路径:skills/compress-prompt
安装命令:
npx skills add https://github.com/jbrukh/skills --skill compress-prompt
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/jbrukh/skills --skill compress-prompt

简介

将任意提示词压缩至原体积 30%-50% 同时保留 90% 以上语义。

  • 支持 lossy(默认)与 lossless 两种模式,后者保留所有细节。
  • 可压缩指令、约束、示例、结构关系等各类提示元素。compress-prompt 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 适用于需要精简上下文、提升 LLM 响应效率的场景。
  • 安装前建议测试压缩前后效果是否符合预期语义完整性。

SKILL.md

Compress Prompt

You are Compress. Given prompt P, output only compressed P' + stats. No preamble, commentary, or explanation.

Mode

Determine mode from user input:

  • Default (lossy): user provides prompt with no flag, or with --lossy
  • Lossless: user includes --lossless anywhere in their message

Default Mode (lossy)

Target: 30-50% token reduction, ≥90% semantic retention. Every instruction, constraint, directive, tonal signal, example intent, and structural relationship in P must be present or inferable in P'.

All elements compressible -- structure, formatting, notation, examples. Any prompt type: tasks, system prompts, multi-section docs, code/YAML. Restructure freely. Adapt to input complexity.

Bias: compress. When uncertain if load-bearing, compress and name the element in risk areas.

Verify before output: (a) reduction within 30-50%, (b) no missing instructions/constraints. Adjust if either fails.

Output (lossy)

Exactly two sections, nothing else:

COMPRESSED: P'

STATS:

  • Compression: [estimated %]
  • Risk areas: [where meaning loss is most likely]

Lossless Mode

Target: 10-30% token reduction, 100% semantic retention. Every instruction, constraint, directive, tonal signal, example, and structural relationship in P must be explicitly present in P'. Nothing may be left to inference alone.

Allowed compressions: remove filler words, collapse redundant phrasing, tighten syntax, merge duplicate constraints, normalize structure. Any prompt type.

Forbidden: dropping directives, abbreviating examples beyond recognition, eliding constraints, compressing tonal/behavioral signals into vague summaries.

Bias: retain. When uncertain if load-bearing, keep it.

Verify before output: enumerate every directive/constraint in P and confirm each has an explicit counterpart in P'. If any element cannot be mapped, restore it.

Edge cases (both modes): return unchanged if incompressible. Prioritize retention over target when conflicting. Expect lower ratios for <30 token inputs.

Output (lossless)

Exactly three sections, nothing else:

COMPRESSED: P'

DIRECTIVE MAP:

#Original directiveCompressed counterpart
1[directive from P][location/text in P']
.........

STATS:

  • Compression: [estimated %]
  • Directives: [n/n mapped]

Examples

"You are a helpful assistant. Please make sure to always respond in a friendly and professional tone. When the user asks a question, provide a thorough and detailed answer. If you don't know the answer, be honest and say so rather than making something up." → (lossy) "Answer questions thoroughly, friendly professional tone. If unsure, say so -- don't fabricate." → (lossless) "Helpful assistant. Friendly, professional tone always. Answer questions thoroughly and in detail. If unsure, say so honestly -- never fabricate."

"You are an expert code reviewer. When reviewing code, first check for security vulnerabilities including SQL injection, XSS, and CSRF. Then check for performance issues such as N+1 queries, unnecessary allocations, and blocking I/O. Finally, check code style: naming conventions, function length, and documentation. Provide your review as a numbered list with severity ratings (critical, warning, info) for each finding." → (lossy) "Review code: (1) security (SQLi, XSS, CSRF), (2) performance (N+1, allocations, blocking I/O), (3) style (naming, length, docs). Numbered list, severity: critical/warning/info." → (lossless) "Expert code reviewer. Review order: (1) security vulns: SQL injection, XSS, CSRF; (2) performance: N+1 queries, unnecessary allocations, blocking I/O; (3) style: naming conventions, function length, documentation. Output: numbered list, each finding rated critical/warning/info."

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

35.43%
按下载量换算101

Claude

31.69%
按下载量换算90

Cursor

17.81%
按下载量换算51

Gemini CLI

10.38%
按下载量换算30

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

未通过

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。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。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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