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 ifrs9-credit-risk-mastergit 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/ifrs9-credit-risk-master)<a href="https://agentmods.dev/skills/vivy-yi/finance-skills/ifrs9-credit-risk-master"><img src="https://agentmods.dev/badge/skills/vivy-yi/finance-skills/ifrs9-credit-risk-master/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/ifrs9-credit-risk-master"><img src="https://agentmods.dev/badge/skills/vivy-yi/finance-skills/ifrs9-credit-risk-master.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 | $0.00158 | $0.02264 |
| Opus 5 | $0.00079 | $0.01132 |
| Sonnet 5 | $0.00032 | $0.00453 |
| Haiku 4.5 | $0.00016 | $0.00226 |
Grade A, and why
ifrs9-credit-risk-master 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 — 250 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 Stage 分类标准/PD-LGD-EAD 参数/升级规则。
/ifrs9-credit-risk-master — IFRS 9 信用风险敞口评估主流程
完整评估流程
Step 1:敞口收集与 Stage 分类
→ 执行 stage-classification
→ 确认每个敞口所处 Stage
Step 2:ECL 计量
→ 执行 ecl-calculation
→ 计算每个敞口的 12 个月/存续期 ECL
Step 3:宏观前瞻调整
→ 执行 macro-forward-adjustment
→ 应用宏观情景权重调整 ECL
Step 4:ECL 结果验证
→ 迁徙分析(NCR)
→ 覆盖率合理性验证
→ 与上期/同业对比
Step 5:监管披露
→ 执行 ecl-disclosure
→ 生成 IFRS 7 / IFRS 9 披露信息
第一步:敞口收集与 Stage 分类
执行 Skill: stage-classification
□ 执行状态:[成功/失败]
□ 敞口汇总:
□ Stage 1 敞口:[X] 万元([X] 个客户/交易)
□ Stage 2 敞口:[X] 万元([X] 个客户/交易)
□ Stage 3 敞口:[X] 万元([X] 个客户/交易)
□ 合计敞口:[X] 万元
□ Stage 分布:
□ Stage 1 占比:[X]% — [✅ 正常 / ⚠️ 偏高]
□ Stage 2 占比:[X]% — [✅ 正常 / ⚠️ 偏高]
□ Stage 3 占比:[X]% — [✅ 正常 / ⚠️ 偏高]
□ 重大 Stage 转移:
□ 新迁入 Stage 2:[X] 个客户 / [X] 万元
□ 新迁入 Stage 3:[X] 个客户 / [X] 万元
□ 转移原因:[描述]
第二步:ECL 计量
执行 Skill: ecl-calculation
□ 执行状态:[成功/失败]
□ ECL 结果:
□ Stage 1 ECL:[X] 万元
□ Stage 2 ECL:[X] 万元
□ Stage 3 ECL:[X] 万元
□ 合计 ECL:[X] 万元
□ 覆盖率:
□ Stage 1 覆盖率:[X]% — [✅ 充足 / ⚠️ 偏低]
□ Stage 2 覆盖率:[X]% — [✅ 充足 / ⚠️ 偏低]
□ Stage 3 覆盖率:[X]% — [✅ 充足 / ⚠️ 偏低]
□ 与上期对比:
□ 上期 ECL:[X] 万元 vs 本期 [X] 万元
□ 变动:[±X] 万元 / [±X]%
□ 主要变动原因:[描述]
第三步:宏观前瞻调整
执行 Skill: macro-forward-adjustment
□ 执行状态:[成功/失败/不适用]
□ 宏观情景权重:
□ 乐观情景:[X]% — GDP +[X]%
□ 基准情景:[X]% — GDP [X]%
□ 悲观情景:[X]% — GDP -[X]%
□ 前瞻调整后 ECL:
□ 调整前 ECL:[X] 万元
□ 调整后 ECL:[X] 万元
□ 前瞻调整影响:[±X] 万元 / [±X]%
□ 调整合理性:[✅ 合理 / ⚠️ 偏差较大 — 说明:...]
第四步:结果验证
□ 迁徙分析(NCR):
□ Stage 1 → Stage 2 净转入:[X] 个 / [X] 万元
□ Stage 2 → Stage 3 净转入:[X] 个 / [X] 万元
□ 迁徙是否符合预期:[✅ 是 / ⚠️ 异常 — 说明:...]
□ 覆盖率验证:
□ 是否低于监管最低要求:[是/否]
□ 是否低于同业均值(偏离 > ±[X]%):[是/否]
□ Top 10 敞口集中度:
□ Top 3 敞口占合计 ECL:[X]% — [✅ 分散 / ⚠️ 集中]
□ 重大风险暴露:
□ 单一最大敞口:[客户名] — [X] 万元 / Stage [X] — ECL [X] 万元
□ 须升级至 CRO:[✅ 是 / ⚠️ 否]
第五步:监管披露
执行 Skill: ecl-disclosure
□ 执行状态:[成功/失败]
□ 披露完整性:
□ ECL 变动表(IFRS 7):[✅ 已提供 / ☐ 缺失]
□ Stage 转移说明:[✅ 已提供 / ☐ 缺失]
□ 宏观调整说明:[✅ 已提供 / ☐ 缺失]
□ 敏感性分析:[✅ 已提供 / ☐ 缺失]
□ 审计配合事项:
□ 审计调整:[X] 万元
□ 审计保留意见:[是/否 — 内容:...]
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 · 250 lines · 158 tokens per session scan A 2d428f3d4dbc
ifrs9-credit-risk-master is a skill published in the GitHub repository vivy-yi/finance-skills (29 stars, last pushed 3mo ago), licensed MIT. It adds 158 tokens to every session and 2,264 once invoked, about $0.0008 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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