Nm Conserve Response Compression

Compress verbose responses by removing filler, hype, and unnecessary framing. Directness and termination guidelines

Security Scan

Instruction-only skill whose requirements and instructions match its stated goal of trimming verbose responses; nothing requested appears disproportionate or unrelated to that purpose.

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.

Assessment

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).

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

• 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

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