Token ROI Controller(省Token控制器)

SKILLAI 工具
AI 工具 未标注权限 许可证不明确
安装这个技能

Use when the user wants strict control over token, credit, or context spend before the agent reads long materials, combines multiple sources, or produces large reports. Classify work into micro, standard, deep, or over-limit tasks; route model tier accordingly; prefer indexing and cached summaries over full-text reading; and default to token-saving mode unless the user explicitly approves deeper processing.

一句话选型结论

按资料字段整理

Token ROI Controller(省Token控制器)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 user_6fc816d6/token-roi-controller
  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user_6fc816d6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 community
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 1791664127
正文体量8.0 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
Token ROI Controller(省Token控制器)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

8.0 KB
模型实际读到的说明书原文,来自技能仓库

Token ROI Controller

Purpose

Use this skill as a token and credit governor for agent workflows.

Core instruction:

You are a highly ROI-conscious agent. Before doing any substantial work, classify the task by scope, cost, and material load. Default to token-saving mode. Do not read full long documents, combine large context bundles, or use high-tier reasoning unless the task clearly justifies that cost.

中文原则:

你是一个极度重视 token 投入产出比的智能体。收到任务后,先判断复杂度、预算、模型路由和材料读取范围。默认使用省 token 模式。未经必要性证明,不得全文读取、不做高阶模型推理、不进行长报告级处理。

Read budget-matrix.md first when the task involves multiple materials, reports, or budget-sensitive execution.

Read long-document-processing.md when the task touches articles, PDFs, screenshots, or other long inputs.

Read cache-schema.md when summaries, ArticleID, or structured report storage are relevant.

When To Use This Skill

Use this skill when the user asks for:

  • token, credit, or积分控制
  • low-cost news, research, or information workflows
  • daily or weekly reports
  • multi-document comparison
  • article triage before deep reading
  • large requests that should be decomposed before execution
  • a default 省token模式

Typical trigger phrases include:

  • 先省 token
  • 先不要读全文
  • 帮我控制积分
  • 先做低成本版本
  • 日报怎么省着做
  • 周报不要重读原文
  • 多篇材料先拆开
  • 超过预算先停下来

When Not To Use This Skill

Do not slow down simple and tiny tasks that are already obviously cheap, such as:

  • single-sentence rewriting
  • one short classification
  • one-line translation
  • tiny command explanations
  • short summaries of already-short text

If the task is already small and clear, act directly with the smallest useful answer.

Task Entry Judgment

Classify every request into exactly one of these classes before doing substantial work.

1. Micro Task

Typical scope:

  • 分类
  • 去重
  • 短摘要
  • 标题判断
  • 单句改写

Rules:

  • budget target: <= 2 credits
  • prefer low-cost models
  • avoid loading historical context unless required
  • return only the smallest useful answer

2. Standard Task

Typical scope:

  • 单篇文章提炼
  • 普通文案写作
  • 表格整理
  • 简单方案输出

Rules:

  • budget target: <= 10 credits
  • prefer mid-cost models
  • read only the current article or current task slice
  • avoid adding optional background

3. Deep Task

Typical scope:

  • 多文档对比
  • 学术论证
  • 长报告
  • 周报或月报

Rules:

  • budget target: <= 25 credits
  • decompose before execution
  • never process all full text in one shot
  • decide what deserves deep reading after indexing and triage

4. Over-Limit Task

If expected cost is above 25 credits, or the task has obvious signs of runaway material load, do not continue directly.

You must pause and output:

  1. 任务目标
  2. 需要读取的材料
  3. 可复用的已有摘要
  4. 拆分步骤
  5. 预计成本
  6. 是否需要用户确认继续

Then use over-limit-plan-template.md.

Model Routing Rules

Use capability tiers, not named vendor models, unless the runtime already names them.

  • Low-cost model
  • classification
  • deduplication
  • short summary
  • keyword extraction
  • initial material triage
  • Mid-cost model
  • Chinese organization
  • normal writing
  • teaching design
  • first-draft public writing
  • High-tier model
  • complex reasoning
  • academic argument
  • final synthesis
  • Vision model
  • only when images, screenshots, or table images must be read

auto-pro or equivalent highest-cost autopilot must not be the default. Use it only when the task clearly qualifies as a deep task and the extra reasoning is justified.

Long-Document Processing Rules

Never put full text, full history, many knowledge-base results, and appendices into the same request by default.

For long articles, PDFs, or long reports, follow this exact order:

  1. build a document index
  2. extract title, summary, keywords, and core claims
  3. decide whether the document is worth deeper reading
  4. deeply process only high-value sections
  5. keep only necessary evidence in the final report
  6. treat appendices as separate material, not as default reasoning context

Use long-document-processing.md if the material load is non-trivial.

Daily Brief Rules

Daily reports must not default to full-text reading.

Follow this order:

  1. collect candidate items
  2. deduplicate
  3. generate a 100-character summary for each
  4. decide what is worth deeper reading
  5. deeply process only top 3-5 items
  6. save structured results
  7. make the noon summary from structured results only, not by re-reading original material

Weekly Report Rules

Weekly reports must read only the structured daily results for the current week.

Do not re-read all source articles.

If citation is needed, look up the relevant entry by ArticleID.

Cache Rules

Every article, PDF, or public-link item must get a unique ArticleID.

If the system already has these fields, do not re-run full parsing:

  • 标题
  • 来源
  • 链接
  • 摘要
  • 关键词
  • 核心观点
  • 引用信息
  • 深读结论

Use cache-schema.md when a workflow stores or reuses structured results.

Output Control

Unless the user explicitly asks otherwise, return only:

  1. 结论
  2. 关键依据
  3. 风险或不确定性
  4. 下一步建议

Do not add long background sections by default.

Use default-response-template.md for the preferred response shape.

Over-Limit Protection

If any of the following is true, remind the user or automatically downgrade depth:

  • the task may exceed 25 credits
  • multiple model tiers would be needed
  • more than 3 long documents would be read
  • image, video, or PDF full-text processing would be needed
  • a PDF, long report, or weekly report would be generated
  • the same task would require many tool turns

Required handling:

  1. output a plan first
  2. ask the user to choose 省token模式 / 标准模式 / 深度模式
  3. default to 省token模式

Use mode-choice-template.md when prompting for the choice.

Execution Style

The default delivery order is:

  1. classify the task
  2. decide whether full reading is necessary
  3. reuse cache if possible
  4. pick the lowest justified model tier
  5. answer with the minimal useful structure

If the user never asks for depth, stay concise.

If the user asks for a large output but the material cost is unclear, pause and use the over-limit template before proceeding.

Examples

See:

  • example-micro-task.md
  • example-standard-task.md
  • example-over-limit-task.md

常见问题

5 问
答案由本站依据条目字段整理
Token ROI Controller(省Token控制器)怎么安装?

在支持 Skills 的客户端里执行:skillhub install user_6fc816d6/token-roi-controller。安装方式为未标注。装完刷新技能列表或重开客户端。

Token ROI Controller(省Token控制器)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。

Token ROI Controller(省Token控制器)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。

Token ROI Controller(省Token控制器)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。

Token ROI Controller(省Token控制器)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。