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 aging-analysis-argit 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/aging-analysis-ar)<a href="https://agentmods.dev/skills/vivy-yi/finance-skills/aging-analysis-ar"><img src="https://agentmods.dev/badge/skills/vivy-yi/finance-skills/aging-analysis-ar/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/aging-analysis-ar"><img src="https://agentmods.dev/badge/skills/vivy-yi/finance-skills/aging-analysis-ar.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 | $0.00116 | $0.01854 |
| Opus 5 | $0.00058 | $0.00927 |
| Sonnet 5 | $0.00023 | $0.00371 |
| Haiku 4.5 | $0.00012 | $0.00185 |
Grade A, and why
aging-analysis-ar 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.
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 — 202 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（客户分级/坏账准备规则/催款政策）。
/aging-analysis-ar — 应收账款账龄分析
Examples
→ 示例：用户说"帮我看一下目前的应收账款账龄，超过 90 天的有哪些客户"，系统应调用本技能，执行账龄分析并识别高风险客户。
→ 示例：用户说"这个月回款不理想，帮我分析一下哪些客户拖久了"，系统应调用本技能，按账龄分层分析回款延迟原因。
→ 示例：用户说"VP 说下季度要把 DSO 压降 15 天，帮我看看从哪里入手"，系统应调用本技能，识别 DSO 压降关键杠杆。
账龄分级定义
账龄 = 截止日期 - 到期日（发票日期 + 账期）
账龄区间：
- 正常（0-30 天）：在账期内，正常管理
- 关注（31-60 天）：开始关注，催款介入
- 警告（61-90 天）：须正式催款
- 危险（91-180 天）：须升级催款 + 停止接单
- 坏账（> 180 天）：考虑法律诉讼/核销
第一步：获取 AR 数据
从 [ERP] 获取应收账款数据：
查询条件：
- 截止日期：[YYYY-MM-DD]
- 客户：全部
- 状态：未清（Open）
导出字段：
客户名称 | 发票号码 | 开票日期 | 到期日 | 应收金额 | 账龄天数 | 客户信用等级 | 合同账期
从 [CRM] 补充客户信息：
□ 客户信用等级（A/B/C/D）
□ 客户联系人
□ 信用额度使用情况
□ 最近一次沟通记录
第二步：账龄分层分析
汇总统计：
总应收款：[XXX万]
- 正常（0-30 天）：[XXX万]（[X]%）
- 关注（31-60 天）：[XXX万]（[X]%）
- 警告（61-90 天）：[XXX万]（[X]%）
- 危险（91-180 天）：[XXX万]（[X]%）
- 坏账（> 180 天）：[XXX万]（[X]%）
第三步：坏账准备计算
坏账准备计提（按账龄法）：
□ 1-30 天：0% → 计提 [XXX万]
□ 31-90 天：10% → 计提 [XXX万]
□ 91-180 天：30% → 计提 [XXX万]
□ 181-365 天：50% → 计提 [XXX万]
□ > 365 天：100% → 计提 [XXX万]
坏账准备合计：[XXX万]
第四步：TOP N 高风险客户识别
TOP 5 逾期大客户：
1. [客户名称]：[XXX万] / 逾期 [X] 天
2. [客户名称]：[XXX万] / 逾期 [X] 天
3. ...
风险评估：
→ 单一客户逾期 > [XXX万] 须立即升级
→ 客户逾期 > 90 天须制定专门催款计划
高风险信号检查：
□ 是否有客户连续 3 个月出现在 TOP 5 逾期清单？
□ 是否有客户信用评级在最近 3 个月内被下调？
□ 是否有客户涉及重大诉讼/被执行？
□ 是否有客户付款承诺反复失信？
第五步：生成账龄分析报告
═══════════════════════════════════════
应收账款账龄分析报告
截止日期：[YYYY-MM-DD]
报告生成时间：[YYYY-MM-DD HH:MM]
═══════════════════════════════════════
【总体概览】
总应收款：[XXX万]（[X] 笔）
已逾期应收款：[XXX万]（[X] 笔）
逾期率：[X%]（已逾期 / 总应收款）
【账龄分布】
账龄区间 | 金额 | 占比 | 笔数 | 坏账准备计提
---------|------|------|------|--------------
0-30天 | [XXX万] | [X%] | [X] | [X万]
31-60天 | [XXX万] | [X%] | [X] | [X万]
61-90天 | [XXX万] | [X%] | [X] | [X万]
91-180天| [XXX万] | [X%] | [X] | [X万]
>180天 | [XXX万] | [X%] | [X] | [X万]
合计 | [XXX万] | 100% | [X] | [X万]
【坏账准备】
应计提坏账准备：[XXX万]
实际计提金额：[XXX万]
差额（如有）：[XXX万]
【TOP 5 逾期大客户】
1. [客户名称] | 逾期金额：[XXX万] | 逾期天数：[X]天 | 客户等级：[A/B/C/D]
→ 最后付款日期：[YYYY-MM-DD]
→ 最后联系日期：[YYYY-MM-DD]
→ 风险等级：[🔴高/⚠️中/✅低]
2. [客户名称] | ... | ... | ...
【须立即跟进项】
□ 逾期 > 90 天的客户：[X] 家 / [XXX万]
→ 须发送正式催款函
→ 须评估是否停止接单
□ 逾期 > 180 天的客户：[X] 家 / [XXX万]
→ 须启动法律诉讼评估
→ 须准备核销材料
□ 客户信用评级下调：[X] 家
→ 须收紧信用额度
□ 新增逾期客户：[X] 家 / [XXX万]
→ 须了解原因，制定催款计划
【下月到期应收预警】
到期日 | 客户 | 金额 | 账龄
--------|------|------|------
[日期] | [客户] | [XX万] | 即将到期
...
═══════════════════════════════════════
置信度：[✅ 高 / ⚠️ 中 / 🔴 低]
报告状态：[✅ 可用 / ⏳ 数据待核实]
═══════════════════════════════════════
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.
- 12d ago First seen · 202 lines · 116 tokens per session scan A 12caab1465bf
aging-analysis-ar is a skill published in the GitHub repository vivy-yi/finance-skills (29 stars, last pushed 3mo ago), licensed MIT. It adds 116 tokens to every session and 1,854 once invoked, about $0.0006 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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