Vibe-Trading is a personal trading agent that gives an AI system tools for market analysis, algorithmic trading, backtesting, and related workflows. It is for users who want an agent to research and evaluate trading strategies or manage simulated and other trading activities. The catalogue contains skills that expose these trading capabilities to compatible agents.
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 HKUDS/Vibe-Trading --skill credit-analysisgit clone --depth 1 https://github.com/HKUDS/Vibe-TradingWrote 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/hkuds/vibe-trading/credit-analysis)<a href="https://agentmods.dev/skills/hkuds/vibe-trading/credit-analysis"><img src="https://agentmods.dev/badge/skills/hkuds/vibe-trading/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/hkuds/vibe-trading/credit-analysis"><img src="https://agentmods.dev/badge/skills/hkuds/vibe-trading/credit-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- 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 | $0.10785 |
| Opus 5 | $0.00018 | $0.05393 |
| Sonnet 5 | $0.00007 | $0.02157 |
| Haiku 4.5 | $0.00004 | $0.01078 |
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 — 910 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 · 910 lines · 36 tokens per session scan A 10d206dac655
credit-analysis is a skill published in the GitHub repository HKUDS/Vibe-Trading (33,177 stars, last pushed yesterday), licensed MIT. It adds 36 tokens to every session and 10,785 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 2026-08-30.
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