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-disclosuregit 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-disclosure)<a href="https://agentmods.dev/skills/vivy-yi/finance-skills/ecl-disclosure"><img src="https://agentmods.dev/badge/skills/vivy-yi/finance-skills/ecl-disclosure/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-disclosure"><img src="https://agentmods.dev/badge/skills/vivy-yi/finance-skills/ecl-disclosure.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 | $0.02022 |
| Opus 5 | $0.00048 | $0.01011 |
| Sonnet 5 | $0.00019 | $0.00404 |
| Haiku 4.5 | $0.00010 | $0.00202 |
Grade A, and why
ecl-disclosure 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 — 214 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 获取豁免条件/披露格式要求。
/ecl-disclosure — IFRS 9 / IFRS 7 信用风险披露
第一步:ECL 变动表(IFRS 7 规定)
□ 期初 → 期末 ECL 变动分解:
期初 ECL([YYYY-MM-DD]):[X] 万元
□ 本期净增加(减少):
□ 新增敞口(期初无 → 期末有):[X] 万元
□ 原有敞口 ECL 变化(模型重评):[X] 万元
□ Stage 转移影响:
→ Stage 1 → 2:[X] 万元(增加拨备)
→ Stage 2 → 3:[X] 万元(增加拨备)
→ Stage 2 → 1:[X] 万元(减少拨备)
□ 收回/核销:[X] 万元
□ 宏观调整:[X] 万元
期末 ECL([YYYY-MM-DD]):[X] 万元
□ 变动勾稽:
□ 期初 [X] + 净增加 [X] = 期末 [X] — [✅ 勾稽正确 / ⚠️ 差异 X 万元]
第二步:Stage 转移披露
□ 本期 Stage 转移汇总:
| 转移方向 | 敞口(万)| 占比 |
|----------|-----------|------|
| Stage 1 → Stage 2 | [X] | [X]% |
| Stage 1 → Stage 3 | [X] | [X]% |
| Stage 2 → Stage 1 | [X] | [X]% |
| Stage 2 → Stage 3 | [X] | [X]% |
| Stage 3 → Stage 2 | [X] | [X]% |
□ 主要转移原因说明(IFRS 7 要求):
□ Stage 1 → Stage 2:[X] 个客户转移,主要原因 [PD 评级下降 / 逾期 30 天 / Watch List]
□ Stage 2 → Stage 3:[X] 个客户转移,主要原因 [逾期 90 天 / 违约]
□ 是否须单独披露重大转移:[✅ 是 / ⚠️ 否]
□ 重大转移定义:敞口 > 合计敞口 [X]% 或 ECL 影响 > [X] 万元
□ 须单独披露:[X] 个客户 — [描述]
第三步:宏观调整披露
□ 宏观调整方法说明:
□ 宏观因子:[GDP 增速 / 房价指数 / 行业指数]
□ 情景权重:[乐观 X]% / [基准 X]% / [悲观 X]%
□ 宏观调整对 ECL 的影响:[±X] 万元
□ 宏观假设与实际数据对比:
□ 假设 GDP [X]% vs 实际 [X]% — [✅ 接近 / ⚠️ 偏离]
□ 假设房价 [X]% vs 实际 [X]% — [✅ 接近 / ⚠️ 偏离]
□ 披露位置:IFRS 7 信用风险披露附注 — 宏观调整说明
第四步:敏感性分析披露
□ 关键参数敏感性(IFRS 7 要求):
□ PD 敏感性:
□ PD +10% → ECL 增加 [X] 万元
□ PD -10% → ECL 减少 [X] 万元
□ LGD 敏感性:
□ LGD +10% → ECL 增加 [X] 万元
□ LGD -10% → ECL 减少 [X] 万元
□ 宏观情景敏感性:
□ 悲观情景权重 +10% → ECL 增加 [X] 万元
□ 敏感性披露结论:
□ 整体 ECL 对 [PD / LGD / 宏观] 最敏感
□ 主要驱动因子:[变量名]
第五步:模型假设披露
□ 模型假设披露(IFRS 9 要求):
□ PD 假设:
□ 内部评级映射方法:[...]
□ 历史数据观察期:[X] 年
□ 前瞻调整方法:[情景加权 / 基准映射 / ...]
□ PD 假设是否发生变化:[是/否 — 变化内容:...]
□ LGD 假设:
□ 抵押品折价率:[X]%(不同抵押品类型分别列示)
□ 回收周期假设:[X] 个月
□ LGD 假设是否发生变化:[是/否 — 变化内容:...]
□ EAD 假设:
□ 表外敞口 CCF:[X]%(不同产品类型)
□ CCF 假设是否发生变化:[是/否 — 变化内容:...]
□ 豁免条件使用(如适用):
□ 低信用风险豁免:[X] 个敞口 — 评级 [A-及以上]
□ 是否在附注披露:[✅ 是 / ⚠️ 否]
第六步:审计配合与审批
□ 须提交审计的事项:
□ ECL 变动表:[✅ 已提供 / ☐ 待提供]
□ Stage 转移说明:[✅ 已提供 / ☐ 待提供]
□ 宏观调整方法:[✅ 已提供 / ☐ 待提供]
□ 敏感性分析:[✅ 已提供 / ☐ 待提供]
□ 模型假设变更:[✅ 已提供 / ☐ 待提供]
□ 审计调整意见:
□ 审计调整:[X] 万元([增加/减少] ECL)
□ 调整原因:[...]
□ 是否须修改模型:[是/否]
□ 审批记录:
□ 风险总监(CRO)审批:[✅ 已审批 / ☐ 待审批]
□ CFO 审批:[✅ 已审批 / ☐ 待审批]
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 · 214 lines · 95 tokens per session scan A 648dd4dd6ef2
ecl-disclosure 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 2,022 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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