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 ecl-calculationgit 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/ecl-calculation)<a href="https://agentmods.dev/skills/vivy-yi/finance-skills/ecl-calculation"><img src="https://agentmods.dev/badge/skills/vivy-yi/finance-skills/ecl-calculation/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/ecl-calculation"><img src="https://agentmods.dev/badge/skills/vivy-yi/finance-skills/ecl-calculation.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 | $0.00095 | $0.01890 |
| Opus 5 | $0.00048 | $0.00945 |
| Sonnet 5 | $0.00019 | $0.00378 |
| Haiku 4.5 | $0.00010 | $0.00189 |
Grade A, and why
ecl-calculation 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 — 188 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 PD/LGD/EAD 参数配置和前瞻调整要求。
/ecl-calculation — ECL 计量
第一步:确认参数配置
□ PD 来源:
□ 内部评级系统:[✅ 可用 / ⚠️ 须手动映射]
□ 外部评级映射:[✅ 已建立 / ⚠️ 须建立]
□ 内部历史数据年限:[X] 年
□ LGD 来源:
□ 内部历史回收数据:[✅ 可用 / ⚠️ 须参照外部]
□ 抵押品处理历史:[✅ 可用 / ⚠️ 须补录]
□ EAD 来源:
□ 表内:账面余额 [✅ 直接取数]
□ 表外:已承诺金额 × CCF [X]%
□ 前瞻调整:
□ 是否须应用宏观调整:[是/否]
□ 宏观情景权重:[乐观 X]% / [基准 X]% / [悲观 X]%
第二步:Stage 1 ECL 计量
□ 计量公式:
□ ECL = EAD × PD(12个月) × LGD × [1/(1+r)^t](折现因子)
□ 对每个 Stage 1 敞口计算:
□ [客户 A] — EAD [X]万 × PD [X]% × LGD [X]% × 折现 = ECL [X]元
□ [客户 B] — ...
□ Stage 1 汇总:
□ Stage 1 敞口:[X] 万元
□ Stage 1 ECL:[X] 万元
□ Stage 1 覆盖率:ECL / 敞口 = [X]%
□ PD 合理性验证:
□ Stage 1 加权平均 PD:[X]% — [✅ 与预期一致 / ⚠️ 偏高]
第三步:Stage 2 ECL 计量
□ 计量公式(存续期):
□ ECL = EAD × PD(存续期,当前评级) × LGD × 折现因子
□ PD(存续期) 计算:
□ 当前评级 [X] → 对应存续期 PD [X]%
□ 须使用存续期 PD(不是 12 个月 PD)
□ 对每个 Stage 2 敞口计算:
□ [客户 C] — EAD [X]万 × PD(存续) [X]% × LGD [X]% × 折现 = ECL [X]元
□ ...
□ Stage 2 汇总:
□ Stage 2 敞口:[X] 万元
□ Stage 2 ECL:[X] 万元
□ Stage 2 覆盖率:[X]% — [✅ 充足 / ⚠️ 偏低]
□ PD 合理性验证:
□ Stage 2 加权平均 PD(存续):[X]% — [✅ 与预期一致 / ⚠️ 偏高]
□ Stage 2 vs Stage 1 PD 差异:[X]x — [✅ 合理差异 / ⚠️ 差异过大]
第四步:Stage 3 ECL 计量
□ 计量公式(已减值):
□ ECL = (EAD + 逾期利息资本化) × LGD × 折现因子
□ PD = 100%(已违约)
□ 抵押品处理:
□ 抵押品评估价值:[X] 万元
□ 折现率:[X]%
□ 抵押品净可回收:[X] 万元
□ ECL 扣除抵押品后净额:[X] 万元
□ Stage 3 汇总:
□ Stage 3 敞口:[X] 万元
□ Stage 3 ECL(扣除抵押品前):[X] 万元
□ 抵押品价值:[X] 万元
□ Stage 3 ECL(扣除抵押品后):[X] 万元
□ Stage 3 覆盖率:[X]% — [✅ 充足 / ⚠️ 偏低]
第五步:ECL 汇总与覆盖率验证
□ ECL 汇总:
| Stage | 敞口(万)| ECL(万)| 覆盖率 |
|-------|-----------|----------|---------|
| Stage 1 | [X] | [X] | [X]% |
| Stage 2 | [X] | [X] | [X]% |
| Stage 3 | [X] | [X] | [X]% |
| **合计** | **[X]** | **[X]** | **[X]%** |
□ 覆盖率合理性检查:
□ Stage 1 覆盖率 vs 监管最低 [X]%:[✅ 达标 / ❌ 不达标]
□ Stage 2 覆盖率 vs 监管最低 [X]%:[✅ 达标 / ❌ 不达标]
□ Stage 3 覆盖率 vs 监管最低 [X]%:[✅ 达标 / ❌ 不达标]
□ 与上期对比:
□ 上期 ECL:[X] 万元 → 本期 [X] 万元
□ 变动:[±X] 万元 / [±X]%
□ 主要变动驱动因素:[...]
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 · 188 lines · 95 tokens per session scan A d1e398316fff
ecl-calculation is a skill published in the GitHub repository vivy-yi/finance-skills (29 stars, last pushed 3mo ago), licensed MIT. It adds 95 tokens to every session and 1,890 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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