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.
npx skills add vivy-yi/finance-skills --skill financial-due-diligencegit clone --depth 1 https://github.com/vivy-yi/finance-skillsWrote 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.
[](https://agentmods.dev/skills/vivy-yi/finance-skills/financial-due-diligence)<a href="https://agentmods.dev/skills/vivy-yi/finance-skills/financial-due-diligence"><img src="https://agentmods.dev/badge/skills/vivy-yi/finance-skills/financial-due-diligence/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.
<a href="https://agentmods.dev/skills/vivy-yi/finance-skills/financial-due-diligence"><img src="https://agentmods.dev/badge/skills/vivy-yi/finance-skills/financial-due-diligence.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00109 | $0.02473 |
| Opus 5 | $0.00055 | $0.01236 |
| Sonnet 5 | $0.00022 | $0.00495 |
| Haiku 4.5 | $0.00011 | $0.00247 |
Grade A, and why
financial-due-diligence 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 9d ago.
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.
How it starts
The opening of the file, as written. The whole thing — 280 lines — stays where its author put it; the contents beside it link to each section on GitHub.
加载上下文
首次使用时: 读取 ../../CLAUDE.md 获取场景级配置(DD 标准清单/风险矩阵/估值方法偏好)。
/financial-due-diligence — 财务尽职调查
Examples
→ 示例:用户说"目标公司的 DD 报告出来了,帮我审阅一下财务风险部分",系统应调用本技能,审阅财务 DD 报告并识别关键风险点。
→ 示例:用户说"目标公司有几笔表外负债,需要在估值里考虑吗",系统应调用本技能,评估表外负债的估值影响。
→ 示例:用户说"帮我们审一下目标公司历史毛利率的真实性,有没有美化",系统应调用本技能,执行财务数据真实性分析。
第一步:DD 范围与计划确认
交易背景:
□ 目标公司:[名称]
□ 交易类型:[股权收购/资产收购/少数股权投资]
□ 目标公司估值:[X] 万(股权价值)
□ DD 范围:[全面 DD / 重点 DD(财务/税务/法务)]
□ DD 团队配置:
→ 财务 DD:[X] 人
→ 税务 DD:[X] 人
→ 法务 DD:[X] 人
□ 关键时间节点:
→ DD 启动:[YYYY-MM-DD]
→ DD 现场执行:[YYYY-MM-DD 至 YYYY-MM-DD]
→ DD 报告提交:[YYYY-MM-DD]
DD 重点关注领域(根据交易类型调整):
□ 股权收购重点:
→ 收入真实性与盈利质量
→ 历史财务数据是否规范
→ 隐性负债识别
□ 少数股权投资重点:
→少数股东权益保护条款
→ 财务报表透明度
→ 利润分配机制
第二步:财务数据核实
历史财务数据核实:
□ 核实历史期间:[最近 3-5 年]
□ 审计情况:
→ 近 [X] 年经审计:[是/否]
→ 审计师:[名称]
→ 审计意见:[无保留/保留/无法表示]
□ 财务数据质量评估:
| 项目 | 数据可信度 | 主要发现 |
|------|----------|---------|
| 收入 | [高/中/低] | [描述] |
| 毛利率 | [高/中/低] | [描述] |
| 净利润 | [高/中/低] | [描述] |
| 资产负债 | [高/中/低] | [描述] |
收入真实性核实:
□ 收入核查方法:
→ 银行流水核对:[已完成/部分/未执行]
→ 客户函证:[回函率 X%,差异 X%]
→ 合同抽查:[抽查比例 X%,差异 X%]
→ 物流/出库记录核对:[已完成/部分]
□ 收入异常发现:
→ 虚构收入:[是/否] — [金额 X 万,占比 X%]
→ 跨期调节:[是/否] — [描述]
→ 关联方交易虚增:[是/否] — [描述]
第三步:盈利能力分析
盈利能力核实:
□ 毛利率分析:
→ 报表毛利率:[X]%
→ 核实后毛利率:[X]%(调整后)
→ 差异:[+/-X]%(差异原因:[描述])
□ 调整后 EBITDA:
→ 报表 EBITDA:[X] 万
→ 调整项:
· [调整项 1]:[+/-X] 万
· [调整项 2]:[+/-X] 万
→ 调整后 EBITDA:[X] 万
→ 调整后 EBITDA 率:[X]%
非经常性损益分析:
□ 非经常性损益:[X] 万
→ 政府补助:[X] 万
→ 资产处置收益:[X] 万
→ 其他:[X] 万
□ 经常性损益:[X] 万
□ 经常性损益占比:[X]%([✅ 盈利质量良好 / ⚠️ 依赖非经常性损益])
第四步:资产质量评估
资产核实:
□ 应收账款:
→ 账龄分布:[X] 天以内 [X]%,[X] 天以上 [X]%
→ 实际回收情况:[核实描述]
→ 可疑/坏账风险:[X] 万([X]%)
□ 存货:
→ 存货周转天数:[X] 天
→ 呆滞/过时风险:[X] 万([X]%)
→ 估值调整建议:[X] 万
□ 固定资产/无形资产:
→ 权属清晰度:[✅ 清晰 / ⚠️ 存在瑕疵]
→ 估值合理性:[评估描述]
□ 资产核实结论:
→ 总资产核实金额:[X] 万(vs 报表 [X] 万)
→ 资产质量评级:[高/中/低]
第五步:负债与或有负债识别
负债核实:
□ 负债核实结果:
→ 报表总负债:[X] 万
→ 核实后总负债:[X] 万
→ 差异:[+/-X] 万
□ 主要差异项:
→ [差异项 1]:[+/-X] 万
→ [差异项 2]:[+/-X] 万
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.
- 9d ago First seen · 280 lines · 109 tokens per session scan A 3e11021e7ba4
financial-due-diligence is a skill published in the GitHub repository vivy-yi/finance-skills (29 stars, last pushed 3mo ago), licensed MIT. It adds 109 tokens to every session and 2,473 once invoked, about $0.0005 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 2026-09-03.
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