令牌技能

SKILLAI 工具
AI 工具 未标注权限 许可证不明确
安装这个技能

省50-80%AI token用量,不牺牲响应质量。省50-80%AI token用量,不牺牲响应质量。Code blocks (always kept intact)。Error mess

一句话选型结论

按资料字段整理

令牌技能是AI 工具类的 Agent Skill,安装方式未标注,权限级别未标注,许可证不明确,有可直接复制的安装命令。技能本质是给模型读的说明书,效果取决于模型与客户端;装完先用一个小任务验证,再放进正式流程。

分类AI 工具 安装方式未标注 权限级别未标注权限 许可证许可证不明确
累计安装 未标注 本周 未标注
GitHub 星标 未标注 仓库 未标注
下载量 未标注 技能市场下载数
权限级别 未标注权限 安装前请确认风险范围
许可证 许可证不明确 未说明

安装

未标注
在支持 Skills 的客户端里执行,装完问一句「你有哪些技能」验证
skillhub install zcwl/token-saver-skill
  1. 1
    确认客户端支持

    确认你的客户端支持 Skills(Claude、Codex、Cursor 等),并升级到较新的版本。

  2. 2
    拿到技能文件

    执行上面的命令安装;也可以把仓库里的 SKILL.md 手动放进客户端的 skills 目录。

  3. 3
    刷新技能列表

    重新打开客户端,或刷新技能列表,让新放进来的技能被加载。

  4. 4
    验证是否生效

    问一句「你有哪些技能」,确认列表里出现它,再拿一个小任务试跑。

技能档案

AI 工具
字段来自技能仓库与公开索引,中文说明由本站标注
作者 / 维护方zcwl仓库 未标注 · 版本 未标注
宿主客户端未标注技能可被这些客户端加载
安装方式未标注安装包 enterprise
权限级别未标注权限安装前请确认风险范围
许可证未说明许可证不明确,商用前请自行确认条款
仓库可用性available技能仓库当前是否可访问
最近核验未标注上游最后核验时间 · 数据更新 1791665328
正文体量8.7 KBSKILL.md 原文大小,越大说明说明书写得越细

典型使用场景

3 类
按分类整理的常见用法,具体以 SKILL.md 为准
组合多个 AI 能力

把写作、检索、总结串成一条流程。

搭工作流

把固定流程做成可重复调用的步骤。

做提示词与评测

整理提示词并对比输出效果。

选择建议

装之前先看这几条
按条目字段给出的判断,不替你决定用不用

值得选的地方

  • 有可直接复制的安装命令,不用自己翻文档找入口。

先想清楚的地方

  • 没有标注权限级别,实际能做什么要装好后确认。
  • 许可证不明确,商用或二次分发前先到仓库确认条款。
  • 没有做站内核验,内容以作者仓库为准。
  • 技能只是说明书,实际效果取决于模型与客户端,装完先用小任务试一遍。

更适合

  • 需要在AI 工具场景里给模型补一套做法的人。
  • 愿意先读一遍 SKILL.md、确认它会让模型做什么的人。

不太适合

  • 对许可证要求很严、必须明确可商用的人。
  • 只想装完就自动变强、不打算看说明书的人。

同类对比

6 个同类
同分类里挑最接近的几条,字段以各自详情页为准
技能权限级别安装方式累计安装宿主客户端安装命令
令牌技能当前 未标注权限 未标注 未标注 未标注 有
执行计划 只读 GitHub 仓库 151.5 万 Codex、Claude、Cursor 有
Playwright MCP 测试 浏览器控制 ClawHub 市场 136.5 万 OpenClaw 有
完成前的验证 未标注权限 GitHub 仓库 131.9 万 Codex、Claude、Cursor 有
子 Agent 驱动开发 只读 GitHub 仓库 131.2 万 Codex、Claude、Cursor 有
前端设计 执行命令 GitHub 仓库 125.6 万 Codex、Claude、Cursor 有

SKILL.md 全文

8.7 KB
模型实际读到的说明书原文,来自技能仓库

TokenSaver

A token cost optimization skill that helps you save 50-80% on AI token usage without sacrificing response quality.

When to Use

Use TokenSaver when:
- You have long conversations that consume many tokens
- You want to reduce AI API costs
- You're working with technical discussions that accumulate context
- You notice token usage growing rapidly in long sessions

Core Capabilities

1. Smart Context Compression

Automatically compresses conversation history based on message importance.

How it works:
- Recent messages (last 3-5) kept fully intact
- Older messages summarized based on importance score
- Code blocks and critical decisions never compressed

Savings: 50-70% reduction in context tokens

2. Semantic Cache

Caches responses to similar queries to avoid reprocessing.

How it works:
- L1: Exact query match → 100% savings
- L2: Semantic similarity > 85% → 80% savings
- L3: Pattern match → 50% savings

3. Adaptive Optimization

Automatically adjusts compression based on token pressure.

Stages:
- < 3K tokens: No compression
- 3-6K tokens: Light compression
- 6-10K tokens: Medium compression
- > 10K tokens: Heavy compression + suggest new chat

Natural Language Commands

When user asks about TokenSaver in natural language, interpret and execute:

Settings & Configuration

User says: "Configure TokenSaver" / "TokenSaver settings" / "Setup TokenSaver"
Action: Show current configuration and available options

Current TokenSaver Settings:
- Mode: Adaptive (auto-adjust based on token pressure)
- Compression: Balanced
- Cache: Enabled
- Quality Threshold: 85%

You can change mode:
- /tokensave - Aggressive mode (max savings)
- /tokenbalance - Balanced mode (default)
- /tokenquality - Quality priority (min compression)

User says: "Use aggressive mode" / "Maximize savings" / "Set to save mode"
Action: Execute /tokensave command
Response: "✅ TokenSaver switched to aggressive save mode. This provides maximum token savings (up to 80%) with slight quality trade-off."

User says: "Use balanced mode" / "Default settings" / "Set to balanced"
Action: Execute /tokenbalance command
Response: "✅ TokenSaver switched to balanced mode. Good savings (50-70%) with quality preserved."

User says: "Prioritize quality" / "Keep full context" / "Set to quality mode"
Action: Execute /tokenquality command
Response: "✅ TokenSaver switched to quality priority mode. Minimal compression, maximum context retention for precision-critical tasks."

User says: "Disable TokenSaver" / "Turn off token saving" / "Stop TokenSaver"
Action: Execute /tokenoff command
Response: "⏸️ TokenSaver temporarily disabled. Re-enable anytime with /tokens or by asking me to turn it back on."

Usage Reports & Analytics

User says: "Show me token usage report" / "TokenSaver statistics" / "Token report"
Action: Execute /tokenreport command
Response format:

📈 TokenSaver Usage Report
━━━━━━━━━━━━━━━━━━━━━━━━━━━━
This Session:
• Total Original Tokens: 12,450
• Total Optimized Tokens: 4,358
• Tokens Saved: 8,092 (65%)
• Estimated Cost Saved: $0.016
• Cache Hits: 5
• Compressions Applied: 12

Top Savings Sources:
1. Context Compression: -6,200 tokens
2. Semantic Cache: -1,500 tokens
3. Response Optimization: -392 tokens

User says: "TokenSaver status" / "How much am I saving?" / "Token status"
Action: Execute /tokens command
Response format:

💰 TokenSaver Status
━━━━━━━━━━━━━━━━━━━━
Current Session:
• Savings: 65%
• Original Tokens: 12,450
• Optimized Tokens: 4,358
• Cache Hits: 5

Current Mode: Balanced (Adaptive)
• Stage 3 compression active
• Quality Score: 95%

User says: "Clear token cache" / "Reset cache" / "Clear TokenSaver cache"
Action: Execute /tokencache clear command
Response: "✅ TokenSaver cache cleared. Fresh responses will be generated for subsequent queries."

Explanation & Help

User says: "What is TokenSaver" / "How does token saving work?" / "Explain TokenSaver"
Response:

TokenSaver is an intelligent token cost optimization skill that helps you save 50-80% on AI token usage.

Key Features:
1. Smart Context Compression - Summarizes old conversation while keeping recent messages intact
2. Semantic Cache - Reuses responses for similar questions
3. Adaptive Optimization - Automatically adjusts based on conversation length
4. Quality Guard - Never compresses code, errors, or important messages

Benefits:
• Save 50-80% on token costs
• Faster responses with caching
• No quality loss for critical content
• Automatic - works in background

User says: "TokenSaver not working" / "Why no savings?" / "TokenSaver not saving"
Response:

TokenSaver may not show savings if:
1. Conversation is too short (&lt; 3000 tokens) - compression starts at 3K
2. Currently in quality priority mode - switch with /tokensave
3. First-time queries - cache builds up over time
4. Code-heavy conversation - code blocks are never compressed

Try starting a longer technical discussion to see significant savings!

Best Practices

User says: "TokenSaver best practices" / "How to use TokenSaver effectively?" / "TokenSaver tips"
Response:

TokenSaver Best Practices:

1. For Debugging/Code Review:
   Use /tokenquality mode to keep full context

2. For Research/Exploration:
   Use /tokensave mode for maximum savings (up to 80%)

3. For General Chat:
   Balanced mode (default) works best

4. When to Start New Chat:
   If switching major topics, start fresh for better compression

5. Monitor with:
   /tokens - Quick status check
   /tokenreport - Detailed analytics

Slash Commands

For direct command access:

/tokens

Show current status and statistics

Session Savings: 65%
Original Tokens: 12,450
Optimized Tokens: 4,358
Cache Hits: 3

/tokensave

Enable aggressive save mode
- Maximum compression
- Best for very long technical discussions
- Slight quality trade-off possible

/tokenbalance

Balanced mode (default)
- Good savings with quality preserved
- Recommended for most use cases

/tokenquality

Quality priority mode
- Minimal compression
- Maximum context retention
- Use when precision is critical

/tokenreport

Generate detailed usage report

Total Tokens Saved: 8,092
Estimated Cost Saved: $0.016
Compressions Applied: 12
Cache Hits: 5

/tokencache clear

Clear all cached responses

/tokenoff

Temporarily disable optimization

Usage Examples

Example 1: Long coding session

User: [20 rounds of Python discussion]
TokenSaver: Optimized 15K → 4.5K tokens (70% saved)

Example 2: Repeated questions

User: &quot;How do I write to a file in Python?&quot;
User: &quot;Python file write method?&quot;
TokenSaver: L2 cache hit - instant response, 0 tokens used

Example 3: Topic switching

User: Switching from discussing Python to JavaScript...
TokenSaver: &quot;Detected topic change. Start new chat to keep context clean?&quot;
[Yes] [No]

Safety Features

TokenSaver never compresses:
- Code blocks (always kept intact)
- Error messages and stack traces
- User-marked important messages
- Messages with high cross-references

Quality Guard:
- Auto-rollback if quality drops > 15%
- One-click restore to uncompressed version
- Snapshots for every compression

Configuration

Default configuration:

{
  &quot;mode&quot;: &quot;adaptive&quot;,
  &quot;compression&quot;: &quot;balanced&quot;,
  &quot;cache&quot;: true,
  &quot;qualityThreshold&quot;: 0.85
}

Expected Results

Conversation Type Tokens Saved Quality Impact
Technical discussion (50 rounds) 70% Minimal
Code review 80% None
Casual chat 75% None
Quick Q&A 30-50% None

Limitations

  • Requires conversation to exceed 3K tokens before compression starts
  • First-time queries cannot be cached
  • Very short conversations (< 10 messages) see minimal benefit
  • Code-heavy conversations benefit most from smart referencing

Related Skills

  • shieldclaw: For security scanning
  • browser_visible: For web browsing
  • file_reader: For reading local files

常见问题

5 问
答案由本站依据条目字段整理
令牌技能怎么安装?

在支持 Skills 的客户端里执行:skillhub install zcwl/token-saver-skill。安装方式为未标注。装完刷新技能列表或重开客户端。

令牌技能支持哪些客户端?

资料没有标注宿主客户端,但只要客户端支持 Skills(Claude、Codex、Cursor 等),一般都能加载。

令牌技能权限高吗,安全吗?

权限级别为未标注权限。安装前先看它会读取或写入哪些内容。

令牌技能收费吗?

本站不代收费用,也没有标注具体价格。是否收费取决于技能本身和所用的模型或客户端,正式使用前按作者说明确认。

令牌技能用什么许可证?

许可证标注为 未说明(许可证不明确)。商用或二次分发前请到仓库确认条款原文。

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技能内容以作者仓库为准;分类、权限级别与许可证状态的中文说明为中文整理,最近核验 未标注。