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 collection-automationgit 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/collection-automation)<a href="https://agentmods.dev/skills/vivy-yi/finance-skills/collection-automation"><img src="https://agentmods.dev/badge/skills/vivy-yi/finance-skills/collection-automation/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/collection-automation"><img src="https://agentmods.dev/badge/skills/vivy-yi/finance-skills/collection-automation.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 | $0.00104 | $0.02031 |
| Opus 5 | $0.00052 | $0.01015 |
| Sonnet 5 | $0.00021 | $0.00406 |
| Haiku 4.5 | $0.00010 | $0.00203 |
Grade A, and why
collection-automation 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 13d 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 — 223 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（催款政策/客户分级/升级矩阵）。
/collection-automation — 自动催款执行
Examples
→ 示例：用户说"客户账期到期了，帮我自动生成催款邮件"，系统应调用本技能，执行自动催款流程。
→ 示例：用户说"有个大客户一直拖欠，已经超过 120 天了，需要升级处理吗"，系统应调用本技能，评估是否达到升级触发条件。
→ 示例：用户说"帮我们设置一套自动催款规则，账期到期前 7 天发提醒，到期后 30 天发正式催款"，系统应调用本技能，配置自动催款规则。
催款阶段定义
阶段 1（逾期 1-30 天）：
触发：自动系统（短信/邮件）
内容：友好提醒（付款到期提示）
负责人：AR 系统自动
阶段 2（逾期 31-60 天）：
触发：人工（邮件 + 电话）
内容：正式提醒，说明逾期金额和到期日
负责人：AR 专员
阶段 3（逾期 61-90 天）：
触发：人工（书面催款函 + 电话）
内容：正式催款，要求 X 日内付款
负责人：AR 主管
阶段 4（逾期 91-180 天）：
触发：律师函 + 停止接单
内容：法律警告，暂停新订单
负责人：AR 主管 + 法务
阶段 5（逾期 > 180 天）：
触发：法律诉讼准备 / 坏账核销
内容：诉讼评估或核销
负责人：财务经理 + 法务 + CEO
第一步：获取逾期清单
从 [ERP] 获取逾期应收数据：
筛选条件：
- 到期日 < 截止日期（已到期）
- 状态：未清
- 排除已承诺付款且在承诺期内的项
导出字段：
客户名称 | 逾期金额 | 逾期天数 | 客户等级 | 合同账期 | 已催次数 | 最后联系日期
第二步：分阶段催款触发
按逾期天数分配催款阶段：
□ 逾期 1-30 天（阶段 1 → 阶段 2）：
→ 发送自动提醒短信/邮件
→ 记录至催款历史
□ 逾期 31-60 天（阶段 2）：
→ AR 专员人工跟进（电话 + 邮件）
→ 要求客户提供付款计划
→ 记录客户承诺付款日期
□ 逾期 61-90 天（阶段 3）：
→ 发送正式书面催款函
→ 电话跟进
→ 如有还款承诺，要求书面确认
□ 逾期 91-180 天（阶段 4）：
→ 发送律师函（法务介入）
→ 冻结该客户的新订单
→ 评估法律诉讼可行性
□ 逾期 > 180 天（阶段 5）：
→ 启动诉讼评估
→ 或准备核销材料
→ 报财务经理 + CEO 审批
第三步：客户承诺付款跟踪
记录客户承诺信息：
□ 客户名称：[名称]
□ 承诺付款金额：[XXX万]
□ 承诺付款日期：[YYYY-MM-DD]
□ 承诺付款方式：[银行转账/支票/...]
□ 沟通人：[姓名/职位]
□ 承诺可信度评估：[高/中/低]
承诺付款跟进：
客户承诺后：
→ 到承诺日确认是否到账
→ 如未到账，立即再次联系
→ 如连续 2 次承诺失信，升级催款阶段
承诺可信度评估标准：
高可信：客户有历史付款记录 + 客户经营正常
中可信：客户有付款意愿但资金紧张
低可信：客户反复承诺失信 / 客户经营异常
第四步：生成催款执行报告
═══════════════════════════════════════
催款执行报告
批次：[YYYYMMDD-XXX]
截止日期：[YYYY-MM-DD]
生成时间：[YYYY-MM-DD HH:MM]
═══════════════════════════════════════
【催款概览】
本次催款客户数：[X] 家
催款总金额：[XXX万]
其中：
- 阶段1（自动提醒）：[X] 家 / [XXX万]
- 阶段2（人工跟进）：[X] 家 / [XXX万]
- 阶段3（正式催款）：[X] 家 / [XXX万]
- 阶段4（律师函）：[X] 家 / [XXX万]
- 阶段5（诉讼/核销）：[X] 家 / [XXX万]
【阶段执行详情】
□ 阶段 1：自动提醒
客户 | 逾期金额 | 逾期天数 | 发送状态
------|----------|---------|----------
[客户A] | [XX万] | [X]天 | [✅已发送]
[客户B] | [XX万] | [X]天 | [✅已发送]
□ 阶段 2：人工跟进
客户 | 逾期金额 | 逾期天数 | 联系结果 | 客户承诺
------|----------|---------|---------|----------
[客户C] | [XX万] | [X]天 | [已联系] | [承诺XX日付款XXX万]
[客户D] | [XX万] | [X]天 | [未联系] | [待回访]
□ 阶段 3：正式催款
客户 | 逾期金额 | 逾期天数 | 催款函状态 | 客户回应
------|----------|---------|-----------|----------
[客户E] | [XX万] | [X]天 | [✅已发送] | [无回应]
□ 阶段 4：律师函
客户 | 逾期金额 | 逾期天数 | 新订单状态 | 下一步
------|----------|---------|-----------|--------
[客户F] | [XX万] | [X]天 | [已冻结] | [评估诉讼]
【承诺付款跟踪】
承诺付款日已到期：[X] 笔
- 已到账：[X] 笔 / [XXX万]
- 未到账：[X] 笔 / [XXX万] → 已再次联系
【升级事项】
须升级至 AR 主管：
□ [客户名称] — 逾期 [X] 天 / [XXX万] — 多次承诺失信
须升级至财务经理：
□ [客户名称] — 逾期 [X] 天 / [XXX万] — 须核销评估
【下期待跟进】
客户 | 逾期金额 | 下次跟进日期 | 下次跟进方式
------|----------|------------|-------------
[客户G] | [XX万] | [YYYY-MM-DD] | [电话/邮件/上门]
[客户H] | [XX万] | [YYYY-MM-DD] | [电话/邮件/上门]
═══════════════════════════════════════
执行人：[AR专员/AR主管]
执行时间：[YYYY-MM-DD HH:MM]
═══════════════════════════════════════
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.
- 13d ago First seen · 223 lines · 104 tokens per session scan A 7167c1785fb1
collection-automation is a skill published in the GitHub repository vivy-yi/finance-skills (29 stars, last pushed 3mo ago), licensed MIT. It adds 104 tokens to every session and 2,031 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 2026-08-30.
Other skills, from other repositories
wealth-structure
A framework for thinking about wealth through ownership, responsibility, leverage, and avoiding risks that could end your progress.
alpha-vantage
Access real-time and historical stock market data, forex rates, cryptocurrency prices, commodities, economic indicators, and 50+ technical indicators via the Alpha Vantage API. Use when fetching stock prices (OHLCV), company fundamentals (income statement, balance sheet, cash flow), earnings, options data, market…
hedgefundmonitor
Query the OFR (Office of Financial Research) Hedge Fund Monitor API for hedge fund data including SEC Form PF aggregated statistics, CFTC Traders in Financial Futures, FICC Sponsored Repo volumes, and FRB SCOOS dealer financing terms. Access time series data on hedge fund size, leverage, counterparties, liquidity…
edgartools
Python library for accessing, analyzing, and extracting data from SEC EDGAR filings. Use when working with SEC filings, financial statements (income statement, balance sheet, cash flow), XBRL financial data, insider trading (Form 4), institutional holdings (13F), company financials, annual/quarterly reports (10-K…
asc-ppp-pricing
Set territory-specific pricing for subscriptions and in-app purchases using current asc setup, pricing summary, price import, and price schedule commands. Use when adjusting prices by country or implementing localized PPP strategies.
fred-economic-data
Query FRED (Federal Reserve Economic Data) API for 800,000+ economic time series from 100+ sources. Access GDP, unemployment, inflation, interest rates, exchange rates, housing, and regional data. Use for macroeconomic analysis, financial research, policy studies, economic forecasting, and academic research requiring…