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 depreciation-analysisgit 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/depreciation-analysis)<a href="https://agentmods.dev/skills/vivy-yi/finance-skills/depreciation-analysis"><img src="https://agentmods.dev/badge/skills/vivy-yi/finance-skills/depreciation-analysis/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/depreciation-analysis"><img src="https://agentmods.dev/badge/skills/vivy-yi/finance-skills/depreciation-analysis.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 | $0.00108 | $0.01475 |
| Opus 5 | $0.00054 | $0.00737 |
| Sonnet 5 | $0.00022 | $0.00295 |
| Haiku 4.5 | $0.00011 | $0.00147 |
Grade A, and why
depreciation-analysis 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 — 170 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（折旧政策/分类/入账科目）。
/depreciation-analysis — 折旧分析
Examples
→ 示例：用户说"帮我们审一下折旧计算是否正确，这个月改了一些资产的使用年限"，系统应调用本技能，执行折旧计算复核。
→ 示例：用户说"新租赁准则实施后，帮我重新算一下使用权资产的折旧"，系统应调用本技能，执行新租赁准则下的折旧重算。
→ 示例：用户说"折旧年限到了但资产还在用，需要怎么处理"，系统应调用本技能，评估折旧年限到期资产的处理方式。
第一步：获取折旧数据
从 [FA] 获取折旧计算表，从 [ERP] 获取折旧凭证：
□ 本月计提折旧：[XXX万]
□ 本年累计折旧：[XXX万]
□ 从 [FA] 获取：
- 各类别折旧金额
- 新增资产折旧
- 减少资产折旧
- 变更调整折旧
第二步：核对 [FA] 与 [ERP] 一致性
折旧计提核对：
□ [FA] 本月折旧：[XXX万]
□ [ERP] 折旧凭证金额：[XXX万]
□ 差异：[±XXX万]
□ 一致性判断：[✅ 一致 / ⚠️ 有差异]
□ 差异原因：[...]
按资产类别核对：
| 资产类别 | [FA]折旧 | [ERP]凭证 | 差异 |
|---------|----------|----------|------|
房屋建筑物 | [XXX万] | [XXX万] | [±XXX万]
机器设备 | [XXX万] | [XXX万] | [±XXX万]
运输设备 | [XXX万] | [XXX万] | [±XXX万]
电子设备 | [XXX万] | [XXX万] | [±XXX万]
...
第三步：折旧变动分析
本月折旧变动原因：
□ 期初折旧：[XXX万]
□ 本月新增折旧：[XXX万]（新增 [X] 台资产）
□ 本月减少折旧：[XXX万]（减少 [X] 台资产）
□ 本月变更调整：[±XXX万]（[折旧年限变更/残值变更]）
□ 本月计提：[XXX万]
□ 期末折旧：[XXX万]
□ vs 上月：[±XXX万]（[±X%]）
□ 变动正常：[✅ 是 / ⚠️ 需确认]
第四步：识别折旧异常
折旧异常识别：
□ 折旧金额为 0 的在用资产：[X] 台
- [资产A]：[原值 XXX万] — 须检查 [原因]
□ 使用寿命已到但未报废：[X] 台
- [资产A]：[原值 XXX万] — 到期日 [YYYY-MM-DD]
□ 折旧率异常的资产：[X] 台
- [资产A]：[原值 XXX万] — 折旧率 [X%] — 异常原因 [描述]
第五步：生成折旧分析报告
═══════════════════════════════════════
折旧分析报告
期间：[YYYY年MM月]
报告日期：[YYYY-MM-DD]
═══════════════════════════════════════
【一、折旧计提概览】
□ 本月计提折旧：[XXX万]
□ 本年累计折旧：[XXX万]
□ vs 上月：[±XXX万]（[±X%]）
□ [FA] 与 [ERP] 一致性：[✅ 一致 / ⚠️ 差异 XXX万]
□ 差异原因：[描述 / 无]
【二、折旧分布】
| 资产类别 | 本月折旧 | 本年累计 | 占比 |
|---------|----------|----------|------|
[类别A] | [XXX万] | [XXX万] | [X%]
[类别B] | [XXX万] | [XXX万] | [X%]
[类别C] | [XXX万] | [XXX万] | [X%]
**合计** | **[XXX万]** | **[XXX万]** | **100%**
【三、折旧变动分析】
□ 新增资产折旧：[+XXX万]（[X] 台）
□ 减少资产折旧：[-XXX万]（[X] 台）
□ 变更调整：[±XXX万]
□ 变动原因分析：
- [原因1]：[描述]
- [原因2]：[描述]
【四、折旧异常识别】
□ 异常资产：[X] 台
□ 须检查项：[X] 项
- [异常项A]：[描述] — 建议 [处理方式]
- [异常项B]：[描述] — 建议 [处理方式]
【五、待处理事项】
□ 须核查：[X] 项
□ 须调整：[X] 项（如有）
□ 须补提折旧：[X] 项（如有）
═══════════════════════════════════════
置信度：[✅ 高 / ⚠️ 中 / 🔴 低]
数据来源：[FA] / [ERP]
═══════════════════════════════════════
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 · 170 lines · 108 tokens per session scan A d46015af65a5
depreciation-analysis is a skill published in the GitHub repository vivy-yi/finance-skills (29 stars, last pushed 3mo ago), licensed MIT. It adds 108 tokens to every session and 1,475 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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