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 skloxo/TideTrading --skill credit-analysisgit clone --depth 1 https://github.com/skloxo/TideTradingWrote 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/skloxo/tidetrading/credit-analysis)<a href="https://agentmods.dev/skills/skloxo/tidetrading/credit-analysis"><img src="https://agentmods.dev/badge/skills/skloxo/tidetrading/credit-analysis/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/skloxo/tidetrading/credit-analysis"><img src="https://agentmods.dev/badge/skills/skloxo/tidetrading/credit-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.00036 | $0.11436 |
| Opus 5 | $0.00018 | $0.05718 |
| Sonnet 5 | $0.00007 | $0.02287 |
| Haiku 4.5 | $0.00004 | $0.01144 |
Grade A, and why
credit-analysis 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 11d 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 — 1,132 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Credit Analysis Skill — 固收与信用分析
适用场景
当用户提出以下类型问题时，优先调用本 skill：
- 债券定价、YTM 计算、久期/凸性分析
- 企业信用评级、违约概率估算
- 信用利差分析与交易策略
- 城投债、ABS/MBS 信用评估
- 利率风险管理（DV01、关键利率久期）
- 中国固收市场结构分析
一、信用分析框架
1.1 信用评级体系
主体评级 vs 债项评级
| 类型 | 定义 | 评级对象 |
|---|---|---|
| 主体评级（Issuer Rating） | 发行人整体偿债能力 | 企业、政府、金融机构 |
| 债项评级（Issue Rating） | 特定债券的信用质量 | 具体债券，考虑抵押品、优先级、契约条款 |
债项评级可高于或低于主体评级（取决于担保结构）。
标准普尔 / 穆迪 / 中国评级对照
| S&P | Moody's | 中国评级 | 含义 |
|---|---|---|---|
| AAA | Aaa | AAA | 最高信用质量，极低违约风险 |
| AA+/AA/AA- | Aa1/Aa2/Aa3 | AA+/AA/AA- | 高质量，极低违约风险 |
| A+/A/A- | A1/A2/A3 | A+/A/A- | 较高信用质量 |
| BBB+/BBB/BBB- | Baa1/Baa2/Baa3 | BBB+/BBB/BBB- | 投资级下限（IG/HY分水岭） |
| BB+及以下 | Ba1及以下 | BB+及以下 | 高收益/投机级 |
| D | D | D | 违约 |
中国特点：国内评级虚高，AA级在国内约等同于国际BBB-，需结合评级展望（正面/稳定/负面）综合判断。
1.2 Altman Z-Score 模型
用于预测企业财务困境，原始模型适用于上市制造业：
Z = 1.2×X1 + 1.4×X2 + 3.3×X3 + 0.6×X4 + 1.0×X5
| 变量 | 计算公式 | 含义 |
|---|---|---|
| X1 | 营运资本 / 总资产 | 流动性 |
| X2 | 留存收益 / 总资产 | 盈利积累 |
| X3 | EBIT / 总资产 | 盈利能力 |
| X4 | 股权市值 / 总负债账面值 | 财务杠杆 |
| X5 | 销售收入 / 总资产 | 资产效率 |
判断区间：
- Z > 2.99：安全区（低违约风险）
- 1.81 < Z < 2.99：灰色区（需深入分析）
- Z < 1.81：危险区（高违约风险）
改进版本：
- Z'（私有企业）：X4改用股权账面值，临界值2.90/1.23
- Z''（非制造业/新兴市场）：去掉X5，临界值2.60/1.10
局限性：
- 基于历史数据，滞后性强
- 不适用金融类企业（杠杆定义不同）
- 中国市场需重新标定参数
1.3 Merton 结构化模型
将公司股权视为对公司资产的看涨期权（执行价格=债务面值）：
核心假设：
- 公司资产价值 V 遵循几何布朗运动：
dV = μV dt + σ_V V dW - 债务为零息债，面值 D，到期日 T
- 违约仅在 T 时刻发生（欧式违约设定）
股权定价（BS公式）：
E = V·N(d1) - D·e^(-rT)·N(d2)
d1 = [ln(V/D) + (r + σ_V²/2)T] / (σ_V·√T)
d2 = d1 - σ_V·√T
违约概率（风险中性）：
PD = N(-d2)
距违约距离（DD, Distance to Default）：
DD = [ln(V/D) + (μ - σ_V²/2)T] / (σ_V·√T)
信用利差估算：
信用利差 ≈ -ln[N(d2) + (V/D·e^(rT))·N(-d1)] / T
参数估算方法（联立方程组）：
- E = V·N(d1) - D·e^(-rT)·N(d2)
- σ_E·E = N(d1)·σ_V·V
1.4 KMV 模型（预期违约频率 EDF）
KMV 是 Merton 模型的商业化实现，由穆迪收购：
步骤：
- 用股价和股权波动率反推资产价值 V 和资产波动率 σ_V
- 计算违约触发点（Default Point）：
DP = 短期债务 + 0.5×长期债务 - 计算距违约距离：
DD = (V - DP) / (V × σ_V) - 通过历史违约数据库将 DD 映射为 EDF（非正态映射）
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.
- 11d ago First seen · 1,132 lines · 36 tokens per session scan A cdcf1bc2a1be
credit-analysis is a skill published in the GitHub repository skloxo/TideTrading (10 stars, last pushed 3d ago), licensed MIT. It adds 36 tokens to every session and 11,436 once invoked, about $0.0002 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 2026-08-31.
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