Shapes turns action-first: action leads, steps numbered, state restated. Use for ADHD-friendly output. Do not use to trim tokens; use response-compression.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Compresses verbose responses by removing filler and framing to save 200-400 tokens. Use when responses feel bloated or context is filling fast.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
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:pr-review - Review feedback아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.
Shapes turns action-first: action leads, steps numbered, state restated. Use for ADHD-friendly output. Do not use to trim tokens; use response-compression.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Inverts burden of proof for code additions. Use when reviewing PRs, planning refactors, or running unbloat to challenge every addition's necessity.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Tracks per-agent token usage and flags waste in parallel dispatch. Use when evaluating parallel agent efficiency or after a multi-agent run.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Coordinates Claude agent teams via filesystem protocol. Use when orchestrating parallel agents with task dependencies. Do not use for single-agent tasks.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Evaluates API surface design, consistency, and exemplar alignment. Use when reviewing public API changes or before releasing a new API surface.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Selects architecture paradigm via research before scaffolding. Use when architecture is undecided and the choice needs justification and documentation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.