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Cn Ib Diligence Workpaper

Community
QiushenZou
cn-ib-diligence-workpaper

Structure Chinese investment-banking due-diligence materials into traceable schedules, testing workpapers, exception logs, and closure records. Use for IPO、再融资、并购重组 or sponsorship diligence involving 关联方、董监高、银行流水、资金往来、客户供应商、合同、资质证照、房产租赁、子公司、股权沿革、合规证明、抽样核查、访谈、函证 or Excel底稿. Use cn-ib-document-control when the task is only tracking receipt, signatures, originals, scans, and versions.

Overview

PublisherQiushenZou
Repositorycn-investment-banking-skills
Skill namecn-ib-diligence-workpaper
Stars
97
Forks
0
Bundled files
7
LicenseApache-2.0
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.

  • 7 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Cn Ib Diligence Workpaper 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/QiushenZou/cn-investment-banking-skills/tree/main/skills/cn-ib-diligence-workpaper
  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/QiushenZou/cn-investment-banking-skills.git /tmp/cn-investment-banking-skills
mkdir -p .claude/skills
cp -r /tmp/cn-investment-banking-skills/skills/cn-ib-diligence-workpaper .claude/skills/cn-ib-diligence-workpaper
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cn Ib Diligence Workpaper 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 Cn Ib Diligence Workpaper 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 Cn Ib Diligence Workpaper 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.

CN IB Diligence Workpaper

Turn unstructured evidence into a reproducible workpaper that links objective, population, procedure, evidence, exception, conclusion, reviewer, and closure.

Principles

  1. Start from the diligence objective, not from the available files.
  2. Preserve population, period, entity, sample method, and evidence provenance.
  3. Separate factual evidence, management explanation, analyst calculation, and professional conclusion.
  4. Never infer no exception from missing evidence.
  5. Record contradictory evidence rather than selecting the more convenient source.
  6. Do not claim sponsor, legal, or accounting sign-off. Route judgment to the responsible professional.
  7. Apply confidentiality and personal-data minimization. Never place live bank-account or identity data in public examples.

Workflow

1. Define the workpaper objective

State:

  • assertion or risk addressed;
  • entity and period;
  • population and completeness source;
  • materiality or review threshold;
  • planned procedure;
  • expected evidence;
  • responsible preparer and reviewer.

Read references/workstreams.md for common workstreams. Tailor the objective to the transaction instead of copying a generic checklist.

2. Build the population

Before selecting samples or summarizing findings:

  1. identify the source system or source schedule;
  2. record extraction date and preparer;
  3. reconcile population totals to an independent record where possible;
  4. preserve excluded records and exclusion rationale;
  5. mark incomplete populations as a scope limitation.

Do not treat a management-prepared list as complete without a completeness procedure.

3. Select and document procedures

Possible procedures include:

  • inspection;
  • recalculation;
  • external confirmation;
  • interview;
  • database or public-record search;
  • source-to-ledger tracing;
  • ledger-to-source vouching;
  • cross-document comparison;
  • analytical review;
  • full-population rule scan;
  • risk-based or statistical sampling.

Record why the procedure addresses the stated risk. For samples, record population size, selection logic, sample size, and selected IDs.

4. Record evidence

Use assets/workpaper-template.csv or equivalent XLSX fields. Read references/evidence-standard.md.

Minimum lineage:

text
workpaper_id, workstream, objective, procedure, subject, period,
population_source, sample_basis, source_document, source_location,
evidence_date, preparer, reviewer, status, exception,
management_explanation, conclusion, follow_up, closure_evidence

Retain calculations as formulas in spreadsheets. Keep assumptions in labeled cells and cite source documents in comments or evidence columns.

5. Evaluate exceptions

Read references/exception-protocol.md. For each exception:

  • describe the observed fact;
  • state the expected condition;
  • quantify the amount or scope where possible;
  • obtain and label management explanation;
  • identify corroborating or contradictory evidence;
  • assess whether the population or other samples may be affected;
  • assign owner and deadline;
  • record the closure test and evidence.

Avoid vague labels such as 已解释 without the explanation and corroboration.

6. Validate the workpaper

For a CSV-form workpaper, run:

bash
python3 scripts/validate_workpaper.py workpaper.csv --output review-issues.csv

The script checks required lineage, duplicate IDs, invalid statuses, unsupported conclusions, and exception closure. Inspect the original evidence before resolving its findings.

For Word-format diligence questionnaires, run:

bash
python3 scripts/scan_questionnaire_completeness.py questionnaire.docx --output questionnaire-review.csv

The scanner distinguishes unchecked yes/no controls from drafting placeholders. It flags conflicting or unanswered selections and affirmative answers with apparently blank detail fields. A signature warning means only that execution cannot be verified from the DOCX package; it is not proof that a signature or seal is missing. Convert legacy .doc files only in a temporary directory before scanning.

7. Form the conclusion

Conclude only within the tested scope. Use formulations such as:

  • 在上述核查范围及程序下,未发现……;
  • 发现以下例外,尚待……;
  • 由于未取得……,暂无法就……形成结论.

Do not use 不存在任何问题 when testing was sampled, evidence was incomplete, or exceptions remain open.

8. Review and close

Completion requires:

  • objective and population are defined;
  • source and location are traceable;
  • calculations are reproducible;
  • exceptions have owners and follow-up;
  • conclusion matches evidence and scope;
  • reviewer and review date are recorded;
  • resolved exceptions retain closure evidence;
  • confidential data is protected.

Outputs

Deliver:

  1. structured workpaper schedule;
  2. exception log;
  3. evidence-gap list;
  4. open follow-up list by owner;
  5. scoped conclusion and limitations;
  6. reviewer-ready closure status.

Use temporary directories for extraction and previews. Save only final requested workpapers in the deal folder.

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 Cn Ib Diligence Workpaper AI skill do?

Structure Chinese investment-banking due-diligence materials into traceable schedules, testing workpapers, exception logs, and closure records. Use for IPO、再融资、并购重组 or sponsorship diligence involving 关联方、董监高、银行流水、资金往来、客户供应商、合同、资质证照、房产租赁、子公司、股权沿革、合规证明、抽样核查、访谈、函证 or Excel底稿. Use cn-ib-document-control when the task is only tracking receipt, signatures, originals, scans, and versions.

Why use Cn Ib Diligence Workpaper on TypingMind?

Because you install it once and use it with any model. Cn Ib Diligence Workpaper 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 Cn Ib Diligence Workpaper in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/QiushenZou/cn-investment-banking-skills/tree/main/skills/cn-ib-diligence-workpaper. 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 Cn Ib Diligence Workpaper?

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 Cn Ib Diligence Workpaper?

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

Is the Cn Ib Diligence Workpaper AI skill free?

Yes. It is published on GitHub by QiushenZou 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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