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 lcr-assessmentgit 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/lcr-assessment)<a href="https://agentmods.dev/skills/vivy-yi/finance-skills/lcr-assessment"><img src="https://agentmods.dev/badge/skills/vivy-yi/finance-skills/lcr-assessment/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/lcr-assessment"><img src="https://agentmods.dev/badge/skills/vivy-yi/finance-skills/lcr-assessment.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 | $0.02319 |
| Opus 5 | $0.00048 | $0.01159 |
| Sonnet 5 | $0.00019 | $0.00464 |
| Haiku 4.5 | $0.00010 | $0.00232 |
Grade A, and why
lcr-assessment 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 — 260 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(LCR 计算规则/流动性风险偏好/监管要求)。
/lcr-assessment — 流动性覆盖率评估
Examples
→ 示例:用户说"帮我计算一下当前的 LCR 指标,看看是否达标",系统应调用本技能,计算流动性覆盖率指标。
→ 示例:用户说"监管要求 LCR 达到 100%,我们目前还差一点,帮我分析原因",系统应调用本技能,分析 LCR 缺口原因。
→ 示例:用户说"帮我们看看有没有什么方法可以快速提升 LCR",系统应调用本技能,制定 LCR 改善方案。
第一步:LCR 计算框架
LCR 定义:
□ 计算公式:LCR = 流动性资产(HQLA)/ 未来 30 天净现金流出
□ 监管要求:
→ 最低要求:[X]%(中国银保监会/巴塞尔 III)
→ 目标值:[X]%(公司内部风险偏好)
数据截止:
□ 计算截止日:[YYYY-MM-DD]
□ 数据来源:[系统名称]
□ 数据状态:[✅ 已核实 / ⚠️ 待核实]
第二步:LCR 计算
优质流动性资产(HQLA)计算:
□ Level 1 资产(无折扣):
→ 现金:[X] 万
→ 央行准备金:[X] 万
→ 国债:[X] 万
→ 合计 Level 1:[X] 万
□ Level 2A 资产([X]% 折扣):
→ 政策性金融债:[X] 万 × [X]% = [X] 万(折扣后)
→ 合计 Level 2A:[X] 万(折扣后)
□ Level 2B 资产([X]% 折扣):
→ [其他合格资产]:[X] 万 × [X]% = [X] 万(折扣后)
□ HQLA 合计:[X] 万
□ HQLA 等级分布:
→ Level 1:[X]%([X] 万)
→ Level 2A:[X]%([X] 万)
→ Level 2B:[X]%([X] 万)
未来 30 天净现金流出计算:
□ 零售存款流出(如适用):
→ 活期存款(稳定):[X] 万 × [X]% = [X] 万
→ 活期存款(欠稳定):[X] 万 × [X]% = [X] 万
□ 对公存款流出:
→ 活期存款:[X] 万 × [X]% = [X] 万
→ 定期存款([X] 天内到期):[X] 万 × [X]% = [X] 万
□ 其他现金流出:
→ 关联方借款到期:[X] 万 × [X]% = [X] 万
→ 担保/承诺流出:[X] 万 × [X]% = [X] 万
□ 现金流入:
→ 到期贷款流入:[X] 万 × [X]% = [X] 万
→ 其他流入:[X] 万
□ 净现金流出 = 总流出 - MIN(总流入,总流出的 [X]%)
= [X] 万 - MIN([X] 万,[X] 万 × [X]%)
= [X] 万
LCR 计算结果:
□ LCR = HQLA / 净现金流出
= [X] 万 / [X] 万
= [X]%
□ LCR vs 监管要求([X]%):[✅ 达标 / 🔴 未达标]
□ LCR vs 内部目标([X]%):[✅ 达标 / ⚠️ 接近目标 / 🔴 未达标]
第三步:结构分析
HQLA 结构分析:
□ HQLA 充足性评估:
→ HQLA 占总能动资产:[X]%
→ HQLA 占负债:[X]%
→ 流动性资产变现能力:[高/中/低]
□ HQLA 集中度风险:
→ 单一资产类型占比:[X]%([资产类型])
→ 集中度风险评级:[高/中/低]
流出结构分析:
□ 流出构成:
→ 存款流出占比:[X]%([X] 万)
→ 借款到期流出占比:[X]%([X] 万)
→ 担保/承诺流出占比:[X]%([X] 万)
□ 关键流出驱动:
→ 最大流出来源:[描述]
→ 来源金额:[X] 万
第四步:风险识别
流动性风险评估:
□ 🔴 高风险项:
→ [风险 1] — 描述 [描述] — 影响 [X] 万
→ [风险 2]
□ 🟡 中风险项:
→ [风险 3] — 描述 [描述]
压力情景分析:
□ 压力情景 1(轻度压力):
→ 假设:存款流出率提升 [X]%,贷款流入减少 [X]%
→ LCR 压力值:[X]%
→ 评估:[✅ >100% / ⚠️ 接近 100% / 🔴 <100%]
□ 压力情景 2(重度压力):
→ 假设:[描述]
→ LCR 压力值:[X]%
→ 评估:[...]
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 · 260 lines · 95 tokens per session scan A 6ca34d3376fc
lcr-assessment 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 2,319 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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