cn-ib-diligence-workpaper

cn-ib-diligence-workpaper is a skill for Codex from Qiushen-first/cn-investment-banking-skills. It costs 131 tokens per session (1,149 once invoked), scanned A, original, Apache-2.0.

A workpaper structure for Chinese investment-banking due diligence, where due diligence means checking evidence about a company before a transaction. It links each test to its purpose, evidence, exceptions, conclusion, reviewer, and closure.

In plain words
What is it for?
Use it to review related parties, bank records, payments, customers, suppliers, contracts, licenses, property, subsidiaries, ownership history, interviews, confirmations, and samples.
Why use it?
It prevents conclusions from being based on missing evidence and keeps facts, management explanations, calculations, and professional judgments separate.

Skill for Codex

Written for Codex: agents/openai.yaml present.

Good fit Use it to review related parties, bank records, payments, customers, suppliers, contracts, licenses, property, subsidiaries, ownership history, interviews, confirmations, and samples.

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Install with agentmods
npx agentmods add skills/qiushen-first/cn-investment-banking-skills/cn-ib-diligence-workpaper
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add Qiushen-first/cn-investment-banking-skills --skill cn-ib-diligence-workpaper
Clone the repo
git clone --depth 1 https://github.com/Qiushen-first/cn-investment-banking-skills

Made for: Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for cn-ib-diligence-workpaper

README.md
[![agentmods](https://agentmods.dev/badge/skills/qiushen-first/cn-investment-banking-skills/cn-ib-diligence-workpaper/github.svg)](https://agentmods.dev/skills/qiushen-first/cn-investment-banking-skills/cn-ib-diligence-workpaper)
Your own site
<a href="https://agentmods.dev/skills/qiushen-first/cn-investment-banking-skills/cn-ib-diligence-workpaper"><img src="https://agentmods.dev/badge/skills/qiushen-first/cn-investment-banking-skills/cn-ib-diligence-workpaper/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for cn-ib-diligence-workpaper

Your own site · 80×15
<a href="https://agentmods.dev/skills/qiushen-first/cn-investment-banking-skills/cn-ib-diligence-workpaper"><img src="https://agentmods.dev/badge/skills/qiushen-first/cn-investment-banking-skills/cn-ib-diligence-workpaper.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 131 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,149 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00131 $0.01149
Opus 5 $0.00066 $0.00575
Sonnet 5 $0.00026 $0.00230
Haiku 4.5 $0.00013 $0.00115

Measured 12d ago against content hash 349c26350a91, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

cn-ib-diligence-workpaper scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/scan_questionnaire_completeness.py, scripts/validate_workpaper.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/cn-ib-diligence-workpaper/SKILL.md · 149 lines

How it starts

The opening of the file, as written. The whole thing — 149 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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.

Read the full file on GitHub · 149 lines

Files

What ships with it

7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 12d ago First seen · 149 lines · 131 tokens per session scan A 349c26350a91

Subscribe to this mod's changes

cn-ib-diligence-workpaper is a skill published in the GitHub repository Qiushen-first/cn-investment-banking-skills (101 stars, last pushed 26d ago), licensed Apache-2.0. It adds 131 tokens to every session and 1,149 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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