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Compresses verbose responses by removing filler and framing to save 200-400 tokens. Use when responses feel bloated or context is filling fast.
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npx skills add https://github.com/athola/claude-night-market --skill response-compressionコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
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SKILL.md を表示中
| name | response-compression |
| description | Compresses verbose responses by removing filler and framing to save 200-400 tokens. Use when responses feel bloated or context is filling fast. |
| alwaysApply | true |
| category | optimization |
| tags | ["tokens","efficiency","communication","directness"] |
| tools | [] |
| complexity | low |
| model_hint | fast |
| estimated_tokens | 500 |
Eliminate response bloat to save 200-400 tokens per response while maintaining clarity.
| 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) |
| 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 (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).
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
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.
End response immediately after:
| 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) |
When Skill(conserve:action-first-output) is active, two rows above
are overridden:
See the Precedence table in that skill for the full resolution.
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 |
Eliminate:
Replace with: Direct answers to the question.
Eliminate:
Replace with: Useful information or nothing.
The following are NOT bloat:
Before finalizing response:
| 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.
This skill works with:
conserve:token-conservation - Budget trackingconserve:context-optimization - MECW managementsanctum:code-review - Review feedback