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response-compression响应压缩

Agent Skill

response-compression 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

1,048

周安装

42

GitHub Stars

264

下载量

339
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

3

许可证

MIT

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:response-compression(响应压缩)
来源仓库:https://github.com/athola/claude-night-market
仓库路径:skills/response-compression
安装命令:
npx skills add https://github.com/athola/claude-night-market --skill response-compression
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。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/athola/claude-night-market --skill response-compression

简介

response-compression 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。

  • 它支持通过资源标记实现信息的分类与过滤,提升检索效率。
  • 可通过 npx skills add 命令从指定 GitHub 仓库安装该技能。
  • 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

Table of Contents

Response Compression

Eliminate response bloat to save 200-400 tokens per response while maintaining clarity.

When To Use

  • Reducing verbose output to save context tokens
  • Providing concise answers without losing information

When NOT To Use

  • Educational explanations where detail improves understanding
  • First-time setup instructions needing step-by-step clarity

Elimination Rules

ELIMINATE

CategoryExamplesReplacement
Decorative Emojis--(remove entirely)
Filler Words"just", "simply", "basically", "essentially"(remove or rephrase)
Hedging Language"might", "could", "perhaps", "potentially", "I think"Use factual statements
Hype Words"powerful", "amazing", "seamless", "robust", "elegant"Use precise descriptors
Conversational Framing"Let's dive in", "Now that we've", "Moving forward"Start with content
Transitions"Furthermore", "Additionally", "In conclusion"(remove, use structure)
Call-to-Action"Feel free to", "Don't hesitate to", "Let me know if"(remove from endings)

PRESERVE (When Appropriate)

CategoryExampleWhen to Use
Status Indicators[pass] [fail] [warn]In structured output, checklists
Technical PrecisionExact error messagesWhen debugging
Safety WarningsCritical info about data lossAlways preserve
Context SettingBrief necessary backgroundWhen user lacks context

Before/After Transformations

Example 1: Opening Bloat

Before (68 tokens):

Great question! I'd be happy to help you understand how this works.
The bloat detector is a powerful tool that analyzes your codebase
and provides comprehensive insights into potential issues...

After (28 tokens):

The bloat detector analyzes codebases using three tiers: quick scan
(heuristics), static analysis (tools), and deep audit (git history).

Example 2: Closing Bloat

Before (45 tokens):

I've completed the task. Here's what I did:
- Deleted 5 files
- Saved 18k tokens

Next steps:
1. Review the changes
2. Run tests
3. Commit if satisfied

Let me know if you need anything else!

After (15 tokens):

Done. Deleted 5 files, saved 18k tokens.
Backup: backup/unbloat-20260102

Example 3: Hedging Removal

Before:

I think this might potentially be causing the issue, but I could be wrong.
Perhaps we should consider looking into it further.

After:

This causes the issue. Investigate the connection pool timeout setting.

Termination Guidelines

When to Stop

End response immediately after:

  • Delivering requested information
  • Completing requested task
  • Providing necessary context

Avoid Trailing Content

PatternAction
"Next steps:"Remove unless safety-critical
"Let me know if..."Remove always
"Summary:"Remove (user has the response)
"Hope this helps!"Remove always
Bullet recapsRemove (redundant)

Exceptions (When Summaries Help)

  • Multi-part tasks with many changes
  • User explicitly requests summary
  • Critical rollback/backup information
  • Complex debugging with multiple findings

Directness Guidelines

Direct =/= Rude

Goal: Information density, not coldness.

EliminatePreserve
Unnecessary encouragementTechnical context
Rapport-building fillerSafety warnings
Hedging without reasonNecessary explanations
Positive paddingFactual uncertainty markers

Encouragement Bloat

Eliminate:

  • "Great question!"
  • "Excellent point!"
  • "Good thinking!"
  • "That's a great approach!"

Replace with: Direct answers to the question.

Rapport-Building Filler

Eliminate:

  • "I'd be happy to help you..."
  • "Feel free to ask if..."
  • "I hope this helps!"
  • "Let me know if you need..."

Replace with: Useful information or nothing.

Preserve Helpful Directness

The following are NOT bloat:

  • Brief context when user needs it
  • Clarifying questions when ambiguity affects correctness
  • Warnings about destructive operations
  • Error explanations that help debugging

Quick Reference Checklist

Before finalizing response:

  • No decorative emojis (status indicators OK)
  • No filler words (just, simply, basically)
  • No hedging without technical uncertainty
  • No hype words (powerful, amazing, robust)
  • No conversational framing at start
  • No unnecessary transitions
  • No "let me know" or "feel free" closings
  • No summary of what was just said
  • No "next steps" unless safety-critical
  • Ends after delivering value

Token Impact

PatternTypical Savings
Eliminating opening bloat30-50 tokens
Removing closing fluff20-40 tokens
Cutting filler words10-20 tokens
Removing emoji5-15 tokens
Direct answers50-100 tokens
Total per response150-350 tokens

Over 1000 responses: 150k-350k tokens saved.

Integration

This skill works with:

  • conserve:token-conservation - Budget tracking
  • conserve:context-optimization - MECW management
  • sanctum:code-review - Review feedback

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

04

需要参考平台分布和安装热度时

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

补充不同宿主或平台的使用分布数据

能力 5

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

Claude Code

30.84%
按下载量换算105

OpenCode

23.3%
按下载量换算79

Cursor

16.44%
按下载量换算56

Codex

12.73%
按下载量换算43

Antigravity

7.56%
按下载量换算26

Gemini CLI

3.55%
按下载量换算12

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。

来源信息

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