Agent skill

Dd Question List Ma

by aifinlab in aifinlab/FinClaw

尽调问题清单助手(并购版)- 生成并购交易尽职调查问题清单。当用户需要为并购项目准备尽调问题、整理尽调清单、设计访谈提纲或梳理并购风险点时触发此技能。适用于投行、PE/VC、企业战投、律所、会所等并购交易场景。

Apache-2.0Auto-check passed

Install Dd Question List Ma

skills CLI
$ npx skills add aifinlab/FinClaw --skill dd-question-list-ma -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install aifinlab/FinClaw dd-question-list-ma --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/aifinlab/FinClaw.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/dd-question-list-ma .claude/skills/dd-question-list-ma && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
dd-question-list-ma
GitHub stars
255
Token cost
~433 tokens
SKILL.md length
96 words
Files
3 (incl. scripts, references)
Skills in repo
74
Repo updated
First seen
Licence
Apache-2.0

At a glance

尽调问题清单助手(并购版)- 生成并购交易尽职调查问题清单。当用户需要为并购项目准备尽调问题、整理尽调清单、设计访谈提纲或梳理并购风险点时触发此技能。适用于投行、PE/VC、企业战投、律所、会所等并购交易场景。

  • Works in 4 steps: 确认交易基本信息 → 生成尽调问题框架 → 输出格式 → …
  • SKILL.md covers 触发场景, 核心工作流, 参考资源 and 注意事项
  • Runs Python scripts from its folder

What it does

Dd Question List Ma is an agent skill from aifinlab/FinClaw. 尽调问题清单助手(并购版)- 生成并购交易尽职调查问题清单。当用户需要为并购项目准备尽调问题、整理尽调清单、设计访谈提纲或梳理并购风险点时触发此技能。适用于投行、PE/VC、企业战投、律所、会所等并购交易场景。

Its SKILL.md is about 430 tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including scripts and reference files (for example `references/ma-dd-checklist.md` and `scripts/generate_checklist.py`).

The licence is Apache-2.0.

Example prompts

  • “/dd-question-list-ma”

Requirements

  • Python 3

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. 确认交易基本信息
  2. 生成尽调问题框架
  3. 输出格式
  4. 差异化处理

What it can do on your machine

Read from SKILL.md and the folder at commit 9e62862. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Dd Question List Ma loads about 433 tokens when it runs, and up to ~1.2k if it reads all its reference files. Until then it costs about 31 tokens; SKILL.md has 96 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~31
When it runs · the whole SKILL.md, loaded when a task matches
~433
With references · SKILL.md plus every file in references/, read only if the agent opens them
~1.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from aifinlab/FinClaw at commit 9e62862, republished under its Apache-2.0 licence (© aifinlab). 96 words, ~433 tokens.

Download SKILL.mdSave it as .claude/skills/dd-question-list-ma/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
dd-question-list-ma
description
尽调问题清单助手(并购版)- 生成并购交易尽职调查问题清单。当用户需要为并购项目准备尽调问题、整理尽调清单、设计访谈提纲或梳理并购风险点时触发此技能。适用于投行、PE/VC、企业战投、律所、会所等并购交易场景。

尽调问题清单助手(并购版)

触发场景

使用此技能当:

  • 用户需要为并购项目生成尽调问题清单
  • 用户要准备买方/卖方尽调访谈提纲
  • 用户需要梳理并购交易的风险关注点
  • 用户要整理尽调工作底稿的问题框架
  • 用户提到"并购尽调"、"M&A DD"、"收购尽调"、"并购访谈"等

核心工作流

1. 确认交易基本信息

首先向用户确认以下信息(如未提供):

  • 交易类型:控股收购/参股投资/资产收购/合并
  • 行业领域:目标公司所处行业
  • 交易阶段:初步接触/签署意向书/正式尽调/交割前
  • 用户角色:买方/卖方/财务顾问/法律顾问
2. 生成尽调问题框架

根据并购交易特点,按以下模块生成问题清单:

2.1 业务与商业尽调
  • 商业模式与盈利逻辑
  • 市场定位与竞争格局
  • 客户集中度与依赖性
  • 供应商关系与供应链稳定性
  • 销售渠道与获客成本
  • 产品/服务生命周期
  • 增长驱动因素与可持续性
2.2 财务尽调
  • 收入确认政策与真实性
  • 成本结构与毛利率波动
  • 应收账款与存货周转
  • 关联交易与资金占用
  • 表外负债与或有事项
  • 盈利预测与假设合理性
  • 税务合规与税收优惠依赖
2.3 法律尽调
  • 股权结构与历史沿革
  • 核心资产权属(土地、房产、专利、商标)
  • 重大合同与履约风险
  • 诉讼仲裁与行政处罚
  • 劳动用工与社保公积金
  • 行业资质与许可
  • 数据安全与隐私合规
2.4 人力资源尽调
  • 核心团队背景与稳定性
  • 关键人员依赖风险
  • 薪酬体系与激励机制
  • 竞业限制与保密协议
  • 企业文化与整合难度
2.5 技术与 IT 尽调(如适用)
  • 核心技术来源与自主性
  • 研发投入与专利布局
  • 技术迭代风险
  • 系统架构与数据安全
  • IT 合规与等保要求
2.6 并购整合专项
  • 协同效应来源与量化
  • 整合计划与时间表
  • 文化差异与冲突点
  • 人员保留方案
  • 系统与流程对接
3. 输出格式

按以下结构输出问题清单:

markdown
# [目标公司名称] 并购尽职调查问题清单

## 交易概况
- 交易类型:
- 行业领域:
- 尽调阶段:

## 一、业务与商业尽调
### 1.1 商业模式
- [具体问题 1]
- [具体问题 2]
...

### 1.2 市场与竞争
...

## 二、财务尽调
...

## 三、法律尽调
...

## 四、人力资源尽调
...

## 五、并购整合专项
...

## 重点关注事项(优先级排序)
1. [高优先级风险点]
2. [中优先级风险点]
3. [低优先级风险点]
4. 差异化处理

根据交易特点调整问题深度:

  • 控股收购:侧重整合、协同、控制权安排
  • 参股投资:侧重财务回报、退出机制、少数股东保护
  • 跨境并购:增加外汇、反垄断、CFIUS 等监管审批问题
  • 关联交易:侧重定价公允性、独立性、利益输送风险

参考资源

如需更详细的问题模板,可参考:

  • references/ma-dd-checklist.md - 并购尽调标准问题库
  • references/industry-specific.md - 行业特定问题(TMT/医疗/制造/消费等)

注意事项

  • 问题应具体可执行,避免空泛
  • 根据尽调阶段调整问题深度(初步尽调 vs 正式尽调)
  • 标注问题的优先级和预计耗时
  • 考虑问题之间的逻辑关联和访谈顺序
  • 预留补充问题的空间(根据尽调发现动态调整)

© aifinlab, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (scripts, references) in skills/dd-question-list-ma of aifinlab/FinClaw.

  • SKILL.md
  • references/ma-dd-checklist.md
  • scripts/generate_checklist.py

Open the folder on GitHubat commit 9e62862

Compare with similar skills

Dd Question List Ma next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Dd Question List Ma compared with similar skills
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Discovery Question Formnexu-io/open-design100k—~1.5kAutomated safety check: PassApache-2.0
Ask User QuestionMemTensor/MemOS12k—~1kAutomated safety check: PassApache-2.0
Bar Prep Questionsanthropics/claude-for-legal9.6k2 repos~4.9kAutomated safety check: PassApache-2.0
Question Tuninggarrytan/gstack136k—~14kAutomated safety check: NotesMIT

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Questions about Dd Question List Ma

What does Dd Question List Ma do?

尽调问题清单助手(并购版)- 生成并购交易尽职调查问题清单。当用户需要为并购项目准备尽调问题、整理尽调清单、设计访谈提纲或梳理并购风险点时触发此技能。适用于投行、PE/VC、企业战投、律所、会所等并购交易场景。. Dd Question List Ma is an agent skill from aifinlab/FinClaw.

How do I install Dd Question List Ma in Claude Code?

Run `npx skills add aifinlab/FinClaw --skill dd-question-list-ma -a claude-code`. Or copy the skill folder (skills/dd-question-list-ma in aifinlab/FinClaw) into .claude/skills/dd-question-list-ma in your project. Claude Code loads it when a task matches its description.

How do I install Dd Question List Ma in Codex?

Run `npx skills add aifinlab/FinClaw --skill dd-question-list-ma -a codex`. Or copy the skill folder (skills/dd-question-list-ma in aifinlab/FinClaw) into .agents/skills/dd-question-list-ma in your project. Codex loads it when a task matches its description.

Can I use Dd Question List Ma in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add aifinlab/FinClaw --skill dd-question-list-ma -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/dd-question-list-ma, .gemini/skills/dd-question-list-ma, .github/skills/dd-question-list-ma and .opencode/skills/dd-question-list-ma in your project.

What does Dd Question List Ma need to run?

Going by SKILL.md and its folder, Dd Question List Ma needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Dd Question List Ma access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Dd Question List Ma safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Dd Question List Ma use?

Dd Question List Ma is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Dd Question List Ma use?

About 433 tokens (SKILL.md is roughly 1.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 736 tokens, read only when the agent opens those files.

What are the alternatives to Dd Question List Ma?

Skills that share tags, products or a category with Dd Question List Ma: Five Questions (brycewang-stanford/Auto-Empirical-Research-Skills, 4.6k stars), Discovery Question Form (nexu-io/open-design, 100k stars), Ask User Question (MemTensor/MemOS, 12k stars) and Bar Prep Questions (anthropics/claude-for-legal, 9.6k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Dd Question List Ma?

aifinlab (a GitHub user) maintains it in aifinlab/FinClaw, which has 255 GitHub stars. The repository holds 74 skills in this directory. The repository was last updated on May 13, 2026.

Source: aifinlab/FinClaw on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.