response-compression
Compress verbose responses by removing filler, hype, and unnecessary framing. Directness and termination guidelines
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/athola/nm-conserve-response-compressionNight 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
- Elimination Rules
- Before/After Transformations
- Termination Guidelines
- Directness Guidelines
- Quick Reference Checklist
- Token Impact
- Integration
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
| 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
Metadata
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Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-athola-nm-conserve-response-compression": {
"enabled": true,
"auto_update": true
}
}
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