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Nm Conserve Response Compression

Compresses verbose responses by removing filler and framing to save 200-400 tokens
通过删除冗余和框架内容来压缩冗长回复,节省200-400个token
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未分类 clawhub v1.9.12 4 版本 100000 Key: 无需
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概述

> Night Market Skill — ported from claude-night-market/conserve. For the full experience with agents, hooks, and commands, install the Claude Code plugin.

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

版本历史

共 4 个版本

  • v1.9.12 当前
    2026-06-19 19:45 安全 安全
  • v1.0.3
    2026-06-09 17:42 安全 安全
  • v1.0.2
    2026-05-09 16:36 安全 安全
  • v1.0.1
    2026-05-07 08:14 安全 安全

安全检测

腾讯云安全 (Keen)

安全,无风险
查看报告

腾讯云安全 (Sanbu)

安全,无风险
查看报告

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