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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.
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/athola/claude-night-market --skill response-compression명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
로컬 사본을 원하시나요? SkillsMP에서 현재 제공할 수 있는 파일을 다운로드하세요.
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Generates and self-executes a diff-derived test plan for a PR. Use when validating PR changes before merge. Do not use for code review; use sanctum:pr-review.
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SKILL.md 표시 중
SOC 직업 분류 기준
| 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