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Acr Measurement

Community
franklee16
acr-measurement

Use when constructing or auditing accounting measures for 《会计研究》 (Accounting Research) — discretionary accruals (modified Jones), real earnings management (Roychowdhury), conservatism (Basu / C-Score), comparability, disclosure indices, audit-quality and tax-avoidance proxies — so each measure is built transparently and reproducibly. Use before identification.

Overview

Publisherfranklee16
Repositoryacademic-research-skills
Skill nameacr-measurement
Stars
225
Forks
34
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by franklee16 on GitHub. Read the source before you install it.

Installation

Install the Acr Measurement AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/franklee16/academic-research-skills/tree/master/Awesome-Journal-Skills-main/Accounting-Research-Skills/skills/acr-measurement
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/franklee16/academic-research-skills.git /tmp/academic-research-skills
mkdir -p .claude/skills
cp -r /tmp/academic-research-skills/Awesome-Journal-Skills-main/Accounting-Research-Skills/skills/acr-measurement .claude/skills/acr-measurement
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Acr Measurement in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Acr Measurement on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Acr Measurement is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

会计度量构造(acr-measurement)

触发时机

  • 直接用现成指标(如 DA、KV 指数)却不交代如何估计/分年分行业
  • 盈余管理只用单一代理变量,无替代度量
  • 度量的样本、估计窗口、回归设定、缩尾未说明
  • 审稿人质疑"度量不规范 / 不可复现 / 不够会计味"

核心原则:度量要"会计味"且透明可复现

本刊审稿人会逐项检查会计度量的构造。每个度量都要说明:估计模型、估计粒度(分年×分行业)、样本要求、缩尾/标准化、符号方向。不能只丢一个变量名。

常用度量与构造要点

盈余管理 / 应计

  • 可操纵性应计(修正 Jones 模型):分年度×分行业(通常≥10 或 15 个观测)回归总应计对 (ΔREV−ΔREC)、PPE,取残差;说明总应计是资产负债表法还是现金流量表法。
  • Dechow-Dichev / 修正 DD:应计对前后期经营现金流回归,残差波动度量应计质量。
  • 真实盈余管理(Roychowdhury):异常经营现金流、异常生产成本、异常酌量性费用三项及其合成;说明方向(正常 vs 操纵)。
  • 多度量并用,主结果与稳健性互证。

会计稳健性

  • Basu 条件稳健性:盈余对收益(正/负)的非对称及时性回归。
  • C-Score(Khan-Watts):基于 Basu 框架按公司年估计的稳健性得分,说明系数估计与拼装。

信息含量 / 可比性

  • 会计可比性(De Franco et al.):同行业配对的盈余—收益映射差异。
  • 价值相关性 / 盈余反应系数:说明事件窗口与异常收益算法。

披露 / 信息

  • 披露指数:自建指数须给编码手册、条目清单、评分规则、双人编码一致性(如 Kappa);用现成指数(如 KV、年报可读性、MD&A 文本指标)须给来源与算法。

审计 / 税收

  • 审计质量代理:Big4/国内十大、行业专长、任期、是否出具非标意见——说明各自度量的局限。
  • 税收规避:实际税率(ETR)、现金 ETR、会计—税收差异(BTD)、扣除盈余管理后的 BTD(DDBTD)——说明分母处理与异常值。

自检清单

  • 每个度量给出估计模型与估计粒度(分年×分行业)
  • 样本最低观测数、缩尾(如 1%/99%)、标准化说明清楚
  • 符号与方向解释明确(值大代表什么)
  • 主度量 + 至少一个替代度量(为稳健性铺路)
  • 自建指数有编码手册与编码一致性
  • 度量与会计议题、信息机制对得上

反模式

  • "我们用修正 Jones 模型计算 DA"——不说分年分行业、样本要求、总应计口径
  • 盈余管理只用一个 DA,无真实盈余管理或替代
  • 披露指数无条目清单与编码规则,无法复现
  • 用现成数据库指标当黑箱,不交代算法与局限

输出格式

【主度量】<名称 + 模型 + 估计粒度>
【构造透明度】模型□ 粒度□ 样本□ 缩尾□ 方向□
【替代度量】<≥1 个,用于稳健性>
【自建指数】编码手册□ 一致性(Kappa)□(如适用)
【与机制对齐】<度量↔信息机制>
【下一步】acr-identification

Frequently asked questions

What does the Acr Measurement AI skill do?

Use when constructing or auditing accounting measures for 《会计研究》 (Accounting Research) — discretionary accruals (modified Jones), real earnings management (Roychowdhury), conservatism (Basu / C-Score), comparability, disclosure indices, audit-quality and tax-avoidance proxies — so each measure is built transparently and reproducibly. Use before identification.

Why use Acr Measurement on TypingMind?

Because you install it once and use it with any model. Acr Measurement is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Acr Measurement in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/franklee16/academic-research-skills/tree/master/Awesome-Journal-Skills-main/Accounting-Research-Skills/skills/acr-measurement. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Acr Measurement?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Acr Measurement?

As many as you like. As long as a model supports skills, you can use Acr Measurement with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Acr Measurement AI skill free?

It is published on GitHub by franklee16. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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