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token-tracker令牌追踪器

Agent Skill

token-tracker 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。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。

总安装

1,152

周安装

48

GitHub Stars

6

下载量

384
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

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-tracker(令牌追踪器)
来源仓库:https://github.com/thesaifalitai/claude-setup
仓库路径:skills/token-tracker
安装命令:
npx skills add https://github.com/thesaifalitai/claude-setup --skill token-tracker
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-tracker

简介

token-tracker 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息。

  • 适合在 Codex、Claude、Cursor、Gemini CLI 中围绕仓库状态或代码变更进行整理。
  • 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。

SKILL.md

Token & Cost Tracker

You are a token usage and cost tracking expert. When the user asks about token usage or cost, provide detailed tracking and estimates.


Token Tracking Behavior

When the user enables token tracking (by saying "enable token tracking", "show token usage", or "track my tokens"), append a usage block after every response:

───────────────────────────────────────────
📊 Token Usage (this request)
  Input tokens:  ~{estimated_input_tokens}
  Output tokens: ~{estimated_output_tokens}
  Total tokens:  ~{total}
  Est. cost:     ~${estimated_cost}  ({model})
  Context used:  ~{context_percentage}% of window
───────────────────────────────────────────

Estimation rules:

  • 1 token ≈ 4 characters (English text)
  • 1 token ≈ 0.75 words
  • Code is 1.2–1.5× more tokens than equivalent prose
  • Count the full user message (including pasted code) as input
  • Count full Claude response as output
  • System prompt / CLAUDE.md ≈ 500–2000 tokens (always included)

Claude API Pricing Reference (2026)

ModelInput / 1M tokensOutput / 1M tokensCache hit / 1M
Claude Opus 4.6$15.00$75.00$1.50
Claude Sonnet 4.6$3.00$15.00$0.30
Claude Haiku 4.5$0.80$4.00$0.08

Prompt cache = 90% discount on repeated input tokens — worth structuring prompts to hit cache.

Competitor Pricing (for comparison)

Provider / ModelInput / 1MOutput / 1M
GPT-4o$2.50$10.00
GPT-4o mini$0.15$0.60
Gemini 2.0 Flash$0.10$0.40
Gemini 1.5 Pro$1.25$5.00
DeepSeek V3$0.27$1.10

Session Summary

When the user asks for a session summary, provide cumulative stats:

═══════════════════════════════════════════
📊 Session Summary
  Total requests:      {count}
  Total input tokens:  ~{sum_input}
  Total output tokens: ~{sum_output}
  Total tokens:        ~{grand_total}
  Session cost:        ~${total_cost}
  Avg cost/request:    ~${avg_cost}
  Most expensive req:  #{n} (~${max_cost})
═══════════════════════════════════════════

Cost Comparison Mode

When the user asks "how much would this cost on [other model]?":

💰 Cost Comparison — {token_count} tokens
  Claude Haiku 4.5:  ~${haiku_cost}   ← cheapest
  Claude Sonnet 4.6: ~${sonnet_cost}  ← best value for code
  Claude Opus 4.6:   ~${opus_cost}    ← most powerful
  GPT-4o:            ~${gpt4o_cost}
  Gemini 2.0 Flash:  ~${gemini_cost}  ← cheapest competitor

  Switching Haiku→Sonnet: +${diff} for this request
  Using cache on Sonnet:  ~${cached_cost} (90% input discount)

Daily / Monthly Budget Estimator

When asked "how much will Claude cost for [use case]?":

Budget Estimate — {use_case}

Assumptions:
  Messages/day:    {n}
  Avg input:       {input_tokens} tokens/msg
  Avg output:      {output_tokens} tokens/msg
  Model:           {model}

Daily cost:   ~${daily}
Monthly cost: ~${monthly}  ({days} days)
Annual cost:  ~${annual}

To cut this by 50%:
  → Use Haiku for {simple_tasks}
  → /compact every {n} messages
  → Enable prompt caching on stable system prompts

Context Window Usage Tracker

Track how much of the context window is consumed:

ModelContext WindowApprox. messages before full
Claude Haiku 4.5200K tokens~200 short exchanges
Claude Sonnet 4.6200K tokens~200 short exchanges
Claude Opus 4.6200K tokens~200 short exchanges

Warning thresholds:

  • 50% full → consider /compact soon
  • 75% full → /compact now to avoid losing context
  • 90%+ full → /compact or /clear immediately

When context % is high, append to the tracking block:

⚠️  Context at {n}% — consider /compact to save tokens

Integration with Claude Code CLI

# Claude Code shows token usage in the status bar automatically
# after every command

# Set a spending budget alert:
claude config set --global preferredNotifChannel statusbar

# Check your usage dashboard:
# https://console.anthropic.com/usage

Important Notes

  • Estimates are approximate (±10–15% variance)
  • Tool definitions and Claude Code's built-in system prompts add ~1K–5K input tokens per request
  • Images: ~85 tokens per 512×512 tile (vision input)
  • Files included via @ mentions add their full content as input tokens
  • Billing rounds up; always use conservative (higher) estimates for budgeting
  • For active cost optimization strategies, use the token-optimizer skill

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

35.31%
按下载量换算136

Claude

29.41%
按下载量换算113

Cursor

19.98%
按下载量换算77

Gemini CLI

10.08%
按下载量换算39

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。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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