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Nm Conserve Response Compression

by athola · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ Security Clean
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Install in OpenClaw
/install nm-conserve-response-compression
Description
Compress verbose responses by removing filler, hype, and unnecessary framing. Directness and termination guidelines
README (SKILL.md)

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

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
Usage Guidance
This skill is coherent with its stated purpose and is low-risk from an installation/credential perspective. Before enabling broadly, test it on non-critical prompts to ensure it doesn't remove essential nuance, safety warnings, or uncertainty where those matter. If you rely on hedged language (e.g., diagnostics, legal, safety guidance), instruct the skill to preserve those sections or avoid using it for those domains. Also verify the plugin source/version if provenance matters (SKILL.md and registry versions differ).
Capability Analysis
Type: OpenClaw Skill Name: nm-conserve-response-compression Version: 1.0.0 The skill bundle 'nm-conserve-response-compression' (SKILL.md) contains purely instructional markdown designed to guide an AI agent toward more concise communication. It provides rules for eliminating filler words, hedging, and conversational bloat to optimize token usage. There is no executable code, no network activity, and no evidence of malicious intent or prompt injection aimed at compromising the system.
Capability Assessment
Purpose & Capability
The name/description (compress verbose responses) matches the SKILL.md content (rules for removing filler, hedging, framing, termination). No unrelated binaries, env vars, or config paths are requested. Minor note: SKILL.md reports a different internal version (1.8.2) than registry metadata (1.0.0) — likely harmless but worth verifying release provenance.
Instruction Scope
Instructions stay within the stated purpose and do not read files, access environment variables, or call external endpoints. However, the rules explicitly instruct removing hedging and turning tentative statements into definitive claims; this can change meaning and may be harmful in safety- or uncertainty-sensitive contexts. Recommend applying the skill only where concise, definitive language is appropriate and configuring explicit exceptions for safety-critical outputs.
Install Mechanism
No install spec and no code files — instruction-only skill. This minimizes risk because nothing will be written to disk or fetched at install time.
Credentials
No environment variables, credentials, or config paths requested. The skill does not ask for unrelated secrets or system access.
Persistence & Privilege
always:false (default), user-invocable:true, and model invocation allowed (normal). The skill does not request permanent or elevated presence or modify other skills/configs.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install nm-conserve-response-compression
  3. After installation, invoke the skill by name or use /nm-conserve-response-compression
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Summary: First release of the response-compression skill focused on concise, direct answers and token efficiency. - Eliminates filler, hype, emojis, and unnecessary framing from responses. - Provides clear guidelines and before/after examples for compressing responses. - Includes a checklist to ensure directness and token savings. - Outlines when to avoid compression (e.g., educational content). - Supports structured outputs and technical precision when necessary.
Metadata
Slug nm-conserve-response-compression
Version 1.0.0
License MIT-0
All-time Installs 1
Active Installs 1
Total Versions 1
Frequently Asked Questions

What is Nm Conserve Response Compression?

Compress verbose responses by removing filler, hype, and unnecessary framing. Directness and termination guidelines. It is an AI Agent Skill for Claude Code / OpenClaw, with 98 downloads so far.

How do I install Nm Conserve Response Compression?

Run "/install nm-conserve-response-compression" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Nm Conserve Response Compression free?

Yes, Nm Conserve Response Compression is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Nm Conserve Response Compression support?

Nm Conserve Response Compression is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Nm Conserve Response Compression?

It is built and maintained by athola (@athola); the current version is v1.0.0.

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