Perform fixed-income credit analysis with EDF/PD calculations and pricing models.
npx skills add https://github.com/GGwujun/SigmX --skill credit-analysis-ggwujun
Please help me install this Agent Skill. Skill: credit-analysis Source: https://github.com/GGwujun/SigmX/tree/main/agent/src/skills/credit-analysis Command: npx skills add https://github.com/GGwujun/SigmX --skill credit-analysis-ggwujun
这项技能帮助投资研究/风险管理团队快速进行固收信用分析，覆盖发行人与债项评估、定价与风险测算，显著提升分析效率与一致性。
Input a dataset of bonds and issuers to generate initial PD/EDF, price sensitivity, and credit spread analyses.
High-intent search queries and answers about installing and using this skill.
You can calculate probability of default (PD) and expected default frequency (EDF) using the Merton model and Altman Z-Score frameworks. This skill processes issuer datasets to generate quantitative default risk estimates for corporate debt.
The best way to perform fixed-income credit analysis across corporate, municipal, and sovereign debt is using scriptable workflows with modular inputs. This skill delivers reproducible outputs for issuer assessment, pricing models, and risk metrics across markets.
Yes, this credit analysis skill supports scenario analysis and credit spread calculations. It provides bond pricing, duration, convexity, DV01, and spread curve fitting to evaluate risk metrics and valuation under various market conditions.
Yes, you need numpy, pandas, and scipy to run fixed-income pricing models and risk diagnostics. These dependencies enable modular inputs, reproducible outputs, and scriptable workflows for comprehensive credit analysis and valuation tasks.
Yes, you can use Python for municipal and sovereign debt risk assessment and valuation. This skill applies pricing models and EDF/PD calculations across corporate, municipal, and sovereign debt markets to support investment decisions and risk hedging.
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