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GitHub Skill

response-compression

Triggers: verbose, bloat, concise, compress, direct, efficient response Eliminates response bloat including emojis, filler words, hedging language, hype, and unnecessary framing. Includes termination and directness guidelines.

athola☆ 157 仓库 Stars19 仓库 Forks

安装与使用

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技能说明

Table of Contents

Response Compression

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

Elimination Rules

ELIMINATE
Category Examples Replacement
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)
Category Example When to Use
Status Indicators [pass] [fail] [warn] In structured output, checklists
Technical Precision Exact error messages When debugging
Safety Warnings Critical info about data loss Always preserve
Context Setting Brief necessary background When 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
Pattern Action
"Next steps:" Remove unless safety-critical
"Let me know if..." Remove always
"Summary:" Remove (user has the response)
"Hope this helps!" Remove always
Bullet recaps Remove (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.

Eliminate Preserve
Unnecessary encouragement Technical context
Rapport-building filler Safety warnings
Hedging without reason Necessary explanations
Positive padding Factual 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

Pattern Typical Savings
Eliminating opening bloat 30-50 tokens
Removing closing fluff 20-40 tokens
Cutting filler words 10-20 tokens
Removing emoji 5-15 tokens
Direct answers 50-100 tokens
Total per response 150-350 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

使用示例

查看来源仓库中的使用示例与配置说明。

查看来源 ↗

来源历史

收录时间
2026-01-20
索引更新时间
2026-01-31
Branch
main

收录与同步日期属于本站记录。源码修改历史请查看来源仓库。

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