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credit-analysis

Assess corporate default probability and price bonds using quantitative financial models.

Updated Jun 30, 2026
Search Tags:#financial modeling#risk management#fixed income#credit analysis#bond pricing#default probability
One-click install
npx skills add https://github.com/20YN04/vibe-trading-macos --skill credit-analysis-20yn04
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: credit-analysis
Source: https://github.com/20YN04/vibe-trading-macos/tree/main/agent/src/skills/credit-analysis
Command: npx skills add https://github.com/20YN04/vibe-trading-macos --skill credit-analysis-20yn04

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, scipy, pandas.

What problem does it solve?

This Skill addresses the complexity of evaluating credit risk, bond pricing, and market strategy, providing a structured framework for quantitative and qualitative financial analysis.

Core Features & Use Cases

  • Quantitative Risk Modeling: Implements Altman Z-Score, Merton structural models, and KMV-based EDF calculations to assess corporate financial health and default probability.
  • Fixed Income Analytics: Provides precise tools for bond pricing, duration/convexity calculations, and yield curve fitting (Nelson-Siegel/Svensson).
  • Use Case: A user can input a company's financial statement data to generate an Altman Z-Score, or calculate the DV01 and modified duration of a bond portfolio to hedge against interest rate volatility.

Quick Start

Use the credit-analysis skill to calculate the modified duration and convexity for a 5-year bond with a 5 percent coupon and 4 percent yield.

Frequently Asked Questions about credit-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I calculate default probability using financial modeling?▼

Default probability is calculated using quantitative models like the Altman Z-Score, Merton structural models, and KMV-based EDF calculations. This skill applies these mathematical frameworks to corporate financial statement data to assess financial health and estimate default likelihood.

Can I compute bond pricing and modified duration with Python?▼

Yes, you can compute bond pricing, modified duration, and convexity using Python. The skill leverages scientific computing libraries like numpy, scipy, and pandas to execute the complex mathematical formulas required for fixed income valuation.

How does yield curve modeling work for fixed income analytics?▼

Yield curve modeling fits mathematical models like Nelson-Siegel and Svensson to market data. This process generates a continuous yield curve used for precise fixed income analytics and interest rate risk assessment.

Do I need numpy and scipy for corporate credit risk assessment?▼

Yes, numpy and scipy are required dependencies for corporate credit risk assessment. These scientific computing libraries provide the mathematical infrastructure to execute complex quantitative financial formulas for default probability estimation and structural modeling.

What is the best way to hedge against interest rate volatility for a bond portfolio?▼

The best way to hedge interest rate volatility is calculating DV01 and modified duration for your bond portfolio. This skill computes these fixed income metrics to quantify interest rate sensitivity and support financial hedging strategies.

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