Mgmt Paper Data Cleaning

Q: Why use Mgmt Paper Data Cleaning on TypingMind?

Because you install it once and use it with any model. Mgmt Paper Data Cleaning 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.

Q: How do I install Mgmt Paper Data Cleaning in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/HaonanAlex/MGMT-Paper-Skills/tree/main/mgmt-paper-data-cleaning. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Q: Which AI models can use Mgmt Paper Data Cleaning?

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.

Q: How many AI models can I use with Mgmt Paper Data Cleaning?

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

Q: Is the Mgmt Paper Data Cleaning AI skill free?

Yes. It is published on GitHub by HaonanAlex under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

Q: 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.

Q: 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.

Community

HaonanAlex

mgmt-paper-data-cleaning

CSMAR (国泰安) 数据清洗标准 SOP，覆盖高频用表 / 通用清洗规则（ST 剔除、IPO 当年剔除、金融行业处理、缺失值）/ 缩尾标准 (winsorize 1%/5%) / 公司 ID 变更追踪 / CSRC 行业代码映射 / 时间对齐 / 国内学者常见陷阱。提供 Stata / R / Python 三语言模板。当用户处理 CSMAR 数据、构建经管类面板数据、或诊断面板数据问题时调用本 Skill。

Overview

Publisher	HaonanAlex
Repository	`MGMT-Paper-Skills`
Skill name	`mgmt-paper-data-cleaning`
Stars	130
Forks	18
Bundled files	14
License	Apache-2.0
Links	Skill source Repository

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.
14 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by HaonanAlex on GitHub. Read the source before you install it.

Installation

Install the Mgmt Paper Data Cleaning AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/HaonanAlex/MGMT-Paper-Skills/tree/main/mgmt-paper-data-cleaning
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.

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/HaonanAlex/MGMT-Paper-Skills.git /tmp/MGMT-Paper-Skills
mkdir -p .claude/skills
cp -r /tmp/MGMT-Paper-Skills/mgmt-paper-data-cleaning .claude/skills/mgmt-paper-data-cleaning

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Mgmt Paper Data Cleaning 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 Mgmt Paper Data Cleaning 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 Mgmt Paper Data Cleaning 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.

MGMT - Paper - Data Cleaning — CSMAR 国泰安数据清洗 SOP

本 Skill 面向使用 CSMAR (国泰安, China Stock Market & Accounting Research Database) 进行实证会计 / 金融 / 公司治理研究的中文研究者，提供一套标准化的清洗流程 (SOP)，把"导入原始库表 → 输出可上跑回归的干净面板"这条链路上的踩坑点显式化。

What it does

列出实证经管研究最高频使用的 CSMAR 库表与关键字段（references/csmar-tables.md）
给出通用清洗规则（删 ST/PT、剔金融业、剔 IPO 当年、缺失值、上市公司 ID 变更）（references/cleaning-rules.md）
标准化缩尾选择：1% / 5%、按年/按行业/全样本（references/winsorize-standard.md）
处理公司股票代码变更、借壳、退市的 ID 追踪（references/id-tracking.md）
CSRC 2012 vs 2001 行业代码映射、制造业 C13–C43 子门类（references/industry-codes.md）
财报年度 vs 自然年、季报年化、月度/日度对齐（references/time-alignment.md）
国内学者高频踩坑（references/pitfalls.md）
Stata / R / Python 三语言可直接跑的清洗模板（code/）

When to use

调用本 Skill 的典型情形：

用户提到 CSMAR / 国泰安 / Stkcd / Symbol / TRD_Dalyr / FS_Combas / FS_Comins 等关键词
用户在做 A 股实证（公司治理、盈余管理、信息披露、ESG、股价同步性、IPO、SEO、并购等）
用户在构建经管类面板数据并询问"要不要剔金融业？""怎么缩尾？""上市公司代码变了怎么处理？"
用户面板回归出现明显异常值、不平衡面板、IPO 当年观测过多等症状
用户准备投国内 C 刊 / SSCI 财会金融期刊，需要按惯例做样本筛选

Skill layout


mgmt-paper-data-cleaning/
├── SKILL.md                          ← 本文件
├── README.md                         ← 用户视角入门
├── references/                       ← 知识库（中文）
│   ├── csmar-tables.md               高频用表清单
│   ├── cleaning-rules.md             通用清洗规则
│   ├── winsorize-standard.md         缩尾标准
│   ├── id-tracking.md                公司 ID 变更
│   ├── industry-codes.md             CSRC 行业代码
│   ├── time-alignment.md             时间对齐
│   └── pitfalls.md                   常见陷阱
└── code/                             ← 三语言模板
    ├── stata/
    │   ├── basic_cleaning.do
    │   └── winsor_industry.do
    ├── r/basic_cleaning.R
    └── python/basic_cleaning.py

Highest-frequency tables (quick reference)

库表英文名	中文	频率	关键字段
`TRD_Dalyr`	个股交易日数据	日	`Stkcd, Trddt, Clsprc, Dretwd, Dsmvosd`
`TRD_Mnth`	个股交易月数据	月	`Stkcd, Trdmnt, Mclsprc, Mretwd, Msmvosd`
`FS_Combas`	资产负债表（合并）	季/年	`Stkcd, Accper, A001000000 (资产总计), A002000000 (负债)`
`FS_Comins`	利润表（合并）	季/年	`Stkcd, Accper, B001100000 (营业收入), B002000000 (净利润)`
`FS_Comscfd`	现金流量表（合并直接法）	季/年	`Stkcd, Accper, C001000000` 系列
`FN_Fn041`	财务指标—相对价值类	年	`Stkcd, Accper, ROA, ROE, EPS`
`EN_EnterpriseInfo`	上市公司基本信息	截面	`Stkcd, ListDt, IndCd, EstDt`
`CG_Ybasic`	公司治理年度基本表	年	`Stkcd, Reptdt, Y0301a (董事人数), Y1101b (独董)`
`STK_MKT_DalyR`	A 股市场日度收益（含市场指数）	日	`Trddt, Cdretwdos (流通市值加权)`

字段名以 CSMAR 最新版数据手册为准；以上为长期沿用的命名约定。

Standard cleaning pipeline (cookbook)

实证会计 / 金融论文样本筛选的"标准菜单"（按惯例顺序）：

样本期窗口 — 按研究设计选定（常见 1998–2023，避开 1990s 早期数据质量问题）
剔除金融业 — 行业代码 J（CSRC 2012）或 I（CSRC 2001）
*剔除 ST / ST / PT 公司 — 通过 TRD_Dalyr 的 Trdsta 字段（交易状态码）
剔除 IPO 当年观测 — Trddt - ListDt < 1 year 全部丢
剔除 B 股 / H 股双重上市样本（视研究设计）— Stkcd 首位为 9 的为 B 股
关键变量缺失值处理 — 一般直接删除（不可插补盈利等关键变量）
缩尾极值 — 连续型变量按 1% / 99% 缩尾（详见 winsorize-standard.md）
检查面板平衡度 — xtset Stkcd year + xtdescribe 输出
保存中间数据 — .dta / .parquet 格式 + 一份变量字典

Three-language templates

每条样本筛选规则在 Stata / R / Python 都有对应实现：

Stata：code/stata/basic_cleaning.do (载入 + 清洗 + 保存)；code/stata/winsor_industry.do (winsor2 行业内缩尾)
R：code/r/basic_cleaning.R (dplyr + DescTools::Winsorize)
Python：code/python/basic_cleaning.py (pandas + scipy.stats.mstats.winsorize)

代码模板均包含合成 minimal 数据用于自检，可独立运行。

Common pitfalls (must read before regression)

详见 references/pitfalls.md。最高频四个：

剔金融业但忘记剔房地产 / 类金融 —— 银行业、保险业之外，K 房地产业是否剔有学派分歧
Stkcd 当成不可变 ID —— 借壳上市后 Stkcd 不变但公司本质已变
季报年化口径混乱 —— Accper 的 03-31 / 06-30 / 09-30 / 12-31 不能直接相加
缩尾在子样本回归前 vs 后做 —— 标准做法是全样本统一缩尾，子样本不再二次缩尾

License & data access

本 Skill 代码 / 文档：Apache 2.0
CSMAR 数据本身：商业数据库，需机构订阅。本 Skill 不分发任何 CSMAR 数据，仅提供清洗代码。
字段名 / 表名引用自 CSMAR 公开数据手册（官网免费下载）。

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Mgmt Paper Data Cleaning AI skill do?

Why use Mgmt Paper Data Cleaning on TypingMind?

Because you install it once and use it with any model. Mgmt Paper Data Cleaning 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 Mgmt Paper Data Cleaning in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/HaonanAlex/MGMT-Paper-Skills/tree/main/mgmt-paper-data-cleaning. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Mgmt Paper Data Cleaning?

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 Mgmt Paper Data Cleaning?

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

Is the Mgmt Paper Data Cleaning AI skill free?

Yes. It is published on GitHub by HaonanAlex under the Apache-2.0 license. 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

MGMT - Paper - Data Cleaning — CSMAR 国泰安数据清洗 SOP

What it does

When to use

Skill layout

Highest-frequency tables (quick reference)

Standard cleaning pipeline (cookbook)

Three-language templates

Common pitfalls (must read before regression)

License & data access

Bundled files

Frequently asked questions

Related AI Skills

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