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Agent Skill · 研究检索

token-saver-skill(令牌保存技能)

token-saver-skill 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

token-saver-skill 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为需要联网;许可证为 MIT-0,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 6,521。

总安装 21,921周安装 932GitHub Stars 1下载 7,680核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.2

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:token-saver-skill(令牌保存技能)
来源仓库:https://github.com/jzming9/token-saver-skill
安装命令:
openclaw skills install token-saver-skill
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install token-saver-skill

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

智能优化 OpenClaw 的令牌成本达 50-80%。通过上下文压缩和语义缓存实现高效管理。自动减少 AI 令牌消耗同时保持服务质量。安装前需确认是否会修改提示结构或缓存策略。建议先测试小规模会话再全面部署。token-saver-skill 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install token-saver-skill;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

token-saver-skill 怎么安装?

在终端执行 openclaw skills install token-saver-skill。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

token-saver-skill 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

token-saver-skill 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

token-saver-skill 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

token-saver-skill 用的人多吗?

公开数据是累计安装 21,921 次,近一周安装 932 次,下载 7,680 次,GitHub 1 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

token-saver-skill 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
token-saver-skill

令牌保存技能 · 本页

21,9211需要联网MIT-0有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
tokensaver
description
Smart token cost optimization for OpenClaw. Automatically reduces AI token consumption by 50-80% through intelligent context compression, semantic caching, and adaptive optimization while maintaining response quality.
metadata
emoji
💰
requires
{}

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

Example 3: Topic switching

User: Switching from discussing Python to JavaScript...
TokenSaver: "Detected topic change. Start new chat to keep context clean?"
[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:

{
  "mode": "adaptive",
  "compression": "balanced",
  "cache": true,
  "qualityThreshold": 0.85
}

Expected Results

Conversation TypeTokens SavedQuality Impact
Technical discussion (50 rounds)70%Minimal
Code review80%None
Casual chat75%None
Quick Q&A30-50%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

平台分布

OpenClaw

85.76%
按下载量换算6,586

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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