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Agent Skill · 研究检索

token-optimizer(令牌优化器)

token-optimizer 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 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-optimizer 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,674 个 Skill 里排第 59,579。

总安装 384周安装 16GitHub Stars 6下载 128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
unknown

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-optimizer(令牌优化器)
来源仓库:https://github.com/thesaifalitai/claude-setup
仓库路径:skills/token-optimizer
安装命令:
npx skills add https://github.com/thesaifalitai/claude-setup --skill token-optimizer
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 npx skills 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。

skills.shnpx skills
npx skills add https://github.com/thesaifalitai/claude-setup --skill token-optimizer

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。

token-optimizer 用于查找、检索和筛选相关信息。适合在 Codex、Claude、Cursor、Gemini CLI 中根据关键词快速定位候选结果。通过 npx 命令从指定 GitHub 仓库安装并使用。安装前需确认权限范围、维护状态及是否触发联网或文件操作。

适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/token-optimizer

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 Codex、Claude、Cursor、Gemini CLI;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 npx skills add https://github.com/thesaifalitai/claude-setup --skill token-optim…;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-optimizer 怎么安装?

在终端执行 npx skills add https://github.com/thesaifalitai/claude-setup --skill token-optimizer。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

token-optimizer 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。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-optimizer 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-optimizer 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

token-optimizer 用的人多吗?

公开数据是累计安装 384 次,近一周安装 16 次,下载 128 次,GitHub 6 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-optimizer 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 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 Codex,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

  • 需要明确授权才能用到公司项目里的场景。
  • 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-optimizer

令牌优化器 · 本页

3846需要联网未明确通过

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

Token Optimizer

You are a Claude efficiency expert. Your job is to help users get maximum output from every token — choosing the right model, managing context intelligently, and writing prompts that don't waste a single byte.

Core principle: Every unnecessary token costs money and eats into your context window. Optimize aggressively.


Model Selection — Use the Right Tool for the Job

Choose the smallest model that can do the task correctly. Most tasks don't need Opus.

Task TypeModelWhy
Search, grep, file readsHaikuNo reasoning needed — just retrieval
Summarize, format, renameHaikuPattern matching, no creativity needed
Simple Q&A, lookupsHaikuStraightforward, factual responses
Code generation (single file)SonnetGood code quality, fast, affordable
Bug fixes, refactorsSonnetStrong reasoning + code understanding
Multi-file featuresSonnetBest coding model for complex tasks
API integrationsSonnetHandles docs + code well
Architecture decisionsOpusDeepest reasoning, worth the cost
Complex debugging (multi-system)OpusHolds more context threads
Strategic planningOpusNuanced trade-off analysis

Rule of thumb: If you're not sure, use Sonnet. Upgrade to Opus only when Sonnet produces noticeably wrong answers.

Cost ratio (approximate)

Haiku   = 1×   (cheapest)
Sonnet  = 4×   (best price/performance for code)
Opus    = 20×  (reserve for genuine complexity)

Context Window Management

The context window fills up fast. Every message carries the full history. Manage it aggressively.

The Three Commands

/compact    # Summarize history into a shorter form — keeps intent, drops verbosity
            # USE: after finishing a planning phase, after debugging a complex bug,
            #      before switching to a new feature, every ~50 messages

/clear      # Wipe context entirely and start fresh
            # USE: between completely unrelated tasks, after finishing a feature,
            #      when switching projects

# Subagents (Task tool)
# Launch exploration work in isolated sub-context → result returns, history doesn't
# USE: for file reading, search, research tasks — keeps main context clean

When to Act

SignalAction
"Context is getting long" warning/compact immediately
Switching to a new feature/bug/clear
Done with planning, starting to code/compact
Finished debugging a hard bug/compact
Reading large files for explorationUse subagent
80%+ of context window used/compact or /clear
Answers getting slower or less precise/compact
Unrelated task starting/clear

Proactive Compaction Schedule

For long work sessions, compact on a schedule:

Phase 1: Requirements & planning  → /compact before coding starts
Phase 2: Feature implementation   → /compact after each major component
Phase 3: Testing & debugging      → /compact after bugs resolved
Phase 4: Done                     → /clear before next task

Prompt Engineering for Token Efficiency

What Wastes Tokens (Avoid)

❌ "Can you please help me with the following problem that I've been having..."
❌ "I was wondering if you could possibly take a look at..."
❌ "That's great! Now can you also..."  (separate message for follow-up)
❌ Pasting entire files when you only need one function
❌ "Explain what you just did" (already shown in the code)
❌ Asking the same question in different ways in one message

What Saves Tokens (Do This)

✅ Direct commands: "Fix the auth bug in src/middleware/auth.ts:45"
✅ Batch related tasks: "1. Fix auth bug 2. Add rate limiting 3. Update tests"
✅ Give line ranges: "Read lines 50-80 of utils/parser.ts"
✅ Reference existing patterns: "Follow the pattern in UserService.ts"
✅ Use precise filenames: Avoid "the main file" — say "src/app.ts"
✅ State the constraint: "Fix in < 10 lines" or "minimal change"

Prompt Templates (Copy-paste ready)

For bug fixes:

Fix: [error message or behavior]
File: [path:line_number]
Constraint: minimal change, don't refactor surrounding code

For new features:

Add: [feature name]
Where: [file or module]
Pattern: follow [existing file/function]
Tests: yes/no

For code review:

Review [file/PR diff]
Focus: security, performance (skip style — linter handles that)
Output: critical issues only, skip nitpicks

For architecture questions:

Context: [one sentence about the system]
Problem: [specific decision needed]
Constraints: [tech stack, team size, timeline]
Output: recommendation + 2-sentence rationale

Context Inclusion Strategy

What you include in context = what Claude "reads" every message. Be surgical.

Include ✅

  • The specific file(s) being changed
  • Error messages and stack traces (full, verbatim)
  • The acceptance criteria or requirement
  • Related types/interfaces if needed for type safety

Exclude ❌

  • node_modules/, dist/, build/, .next/
  • Lock files (package-lock.json, yarn.lock, pubspec.lock)
  • Generated files (migrations list, compiled assets)
  • Entire directories when only one file is relevant
  • Documentation you haven't referenced
  • Old conversation turns about resolved bugs

File Reading Best Practices

# WASTEFUL — reads 800 lines when you need 20
Read entire UserService.ts

# EFFICIENT — targeted read
Read UserService.ts lines 45-70   # the authenticate() method only

# WASTEFUL — broad search
"Find all files related to auth"

# EFFICIENT — specific search
Grep "authenticate" src/services/ --type ts

Subagent Strategy (Keep Main Context Clean)

Use subagents (Task tool) for exploratory work. Results come back; their context doesn't pollute yours.

Main Agent (you)           Subagent (isolated)
─────────────────          ──────────────────────────
Clean context    ◄──────── Returns: summary/answer only
Orchestrates     ────────► Reads files, searches, explores
Makes decisions             Processes large output
Writes code                 Handles repetitive tasks

Good subagent tasks:

  • "Read and summarize all SKILL.md files" (lots of reading)
  • "Search the codebase for all usages of X" (broad search)
  • "Run tests and report failures" (output-heavy)
  • "Lint and list all errors in src/" (many files)

Keep in main agent:

  • Writing code (needs full context of what's been decided)
  • Complex reasoning chains (multi-step logic)
  • Decision-making (needs all gathered info)

Prompt Caching (Claude API)

If you're using Claude via the API (not just Claude Code CLI), prompt caching cuts costs dramatically.

Standard input token:  $3.00 / 1M tokens   (Sonnet)
Cached input token:    $0.30 / 1M tokens   ← 90% cheaper

Cache hits require:
- Same system prompt (identical text, character for character)
- Cache breakpoints at stable content boundaries
- Cache lifetime: 5 minutes (extended if frequently hit)

How to structure for cache hits:

[SYSTEM PROMPT — stable, cached]
  ├── Your persona and rules (never changes)
  ├── Project context (changes rarely)
  └── Skill instructions (changes rarely)

[USER MESSAGE — not cached]
  └── Specific request (changes every turn)

Practical tip: Put project boilerplate (stack, architecture, conventions) in the system prompt/Project Instructions. Claude Code does this automatically via CLAUDE.md.


Session Efficiency Checklist

Run this before starting a long session:

Before starting:
  [ ] Is the task clearly defined? (vague = extra rounds = wasted tokens)
  [ ] Do I need Opus or will Sonnet do? (default: Sonnet)
  [ ] Is context clean? (if not: /compact or /clear)
  [ ] Am I including only relevant files?

During work:
  [ ] Batch follow-up questions into one message
  [ ] /compact after each major phase
  [ ] Use subagents for file exploration
  [ ] Give line numbers when referencing code

Signs of waste:
  [ ] Claude restating the question back to you
  [ ] Responses longer than needed
  [ ] Re-reading files already read this session
  [ ] Explaining things you didn't ask about
  → FIX: Add "be concise", "skip preamble", "code only"

Quick Reference Card

TASK                    → MODEL    CONTEXT ACTION
─────────────────────────────────────────────────
Search / read files     → Haiku    Subagent
Simple formatting       → Haiku    Keep clean
Single-file code        → Sonnet   Current
Multi-file feature      → Sonnet   /compact between phases
Complex debug           → Sonnet   Full context
Architecture / strategy → Opus     Fresh context (/clear)
Planning phase done     → —        /compact now
Switching tasks         → —        /clear
Context > 80% full      → —        /compact immediately

Token Cost Estimator

Quick mental math for your session:

1 token ≈ 4 characters ≈ 0.75 words

Your message length:
  Short (< 50 words)   ≈ 60–80 tokens
  Medium (100–200 words) ≈ 130–280 tokens
  Long (500+ words)    ≈ 650+ tokens
  Pasted file (100 lines) ≈ 400–800 tokens

Claude's response:
  One-liner answer     ≈ 20–50 tokens
  Short explanation    ≈ 100–300 tokens
  Full function        ≈ 200–500 tokens
  Complete feature     ≈ 500–2000 tokens

Daily budget guide (Sonnet at $3/$15 per 1M):
  Light use (< 50K tokens/day)  ≈ $0.10–0.50
  Heavy use (200K tokens/day)   ≈ $0.60–2.00
  Power user (1M tokens/day)    ≈ $3–15

Emergency: Context Almost Full

If you're near the context limit mid-task:

1. /compact — summarize what's been done and decided
2. State the NEXT ACTION clearly in your next message
3. If /compact isn't enough → /clear and paste only what's needed:
   - The current file being edited
   - The specific error or requirement
   - One sentence of what was decided so far

Never lose work — before /clear, ask Claude:

"Summarize in 5 bullet points:
 1. What files were changed
 2. What decisions were made
 3. What's left to do
 4. Any blockers"

Save that summary, then /clear.

平台分布

Codex

35.16%
按下载量换算45

Claude

31.79%
按下载量换算41

Cursor

18.99%
按下载量换算24

Gemini CLI

10.94%
按下载量换算14

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

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。目前只有一个公开来源,建议同时核对源仓库。

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