Assess credit risk and default probability for corporate bonds.
npx skills add https://github.com/keith-mvs/ordinis --skill credit-risk
Please help me install this Agent Skill. Skill: credit-risk Source: https://github.com/keith-mvs/ordinis/tree/main/docs/knowledge-base/domains/signals/fixed-income/credit-risk Command: npx skills add https://github.com/keith-mvs/ordinis --skill credit-risk
This Skill provides frameworks to evaluate issuer credit risk, downgrade probabilities, and market-implied default signals to support fixed income risk management and portfolio decisions.
Simple instruction: "Estimate implied default probability from given credit spread, recovery, and horizon."
High-intent search queries and answers about installing and using this skill.
Implied default probability is calculated from credit spreads by using the spread, recovery rate, and time horizon. This Skill applies market-based models to derive probability estimates, enabling you to infer what the market is pricing for default risk without waiting for rating agency actions.
PD (probability of default) measures the likelihood an issuer will default; LGD (loss given default) estimates the percentage of principal lost if default occurs. This Skill provides frameworks to model both independently, then combine them into expected-loss calculations for portfolio decisions.
Credit rating migrations track issuer movements between rating tiers; spread dynamics measure how bond prices adjust to risk changes. This Skill delivers templates and dashboards to monitor these shifts across investment-grade and high-yield bonds, supporting early detection of credit deterioration.
Yes. This Skill is built for corporate bond and credit-portfolio analysis, letting you evaluate issuer fundamentals against market signals, analyze downgrade probability, and compare implied default risk across holdings to optimize allocation decisions.
You need numpy (≥1.24.0), pandas (≥2.0.0), and scipy (≥1.10.0). These libraries enable numerical computation, data manipulation, and statistical modeling required to calculate PD, LGD, expected loss, and credit-spread decomposition outputs.
Rating scales from Moody's, S&P, and Fitch encode historical default frequencies tied to each tier. This Skill contextualizes rating agency frameworks and provides tools to interpret rating movements alongside market-implied signals for multi-dimensional risk assessment.
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writerExplain credit risk drivers, PD/LGD/EAD components, and CECL/IFRS 9 loss calculations.
executiveusaAggregate bond-level pricing and cashflows into portfolio metrics and scenario P&L.
wudyeEstimate credit risk and credit spreads for fixed-income instruments using quantitative default models.
