TacoSkill LAB
TacoSkill LAB
HomeSkillHubCreatePlaygroundSkillKit
© 2026 TacoSkill LAB
AboutPrivacyTerms
  1. Home
  2. /
  3. SkillHub
  4. /
  5. risk-metrics-calculation
Improve

risk-metrics-calculation

8.7

by wshobson

134Favorites
401Upvotes
0Downvotes

Calculate portfolio risk metrics including VaR, CVaR, Sharpe, Sortino, and drawdown analysis. Use when measuring portfolio risk, implementing risk limits, or building risk monitoring systems.

risk metrics

8.7

Rating

0

Installs

Data & Analytics

Category

Quick Review

Excellent risk metrics skill with comprehensive implementation of industry-standard measures (VaR, CVaR, Sharpe, Sortino, drawdown analysis). The description clearly indicates when to use the skill for portfolio risk measurement, risk limits, and monitoring systems. Implementation is thorough with four well-organized patterns covering core metrics, portfolio-level risk, rolling calculations, and stress testing. Code is production-ready with proper handling of edge cases, multiple VaR methodologies (historical, parametric, Cornish-Fisher), and advanced features like risk parity and correlation analysis. Structure is excellent with clear categorization and quick reference. Novelty is strong—calculating these metrics correctly (especially CVaR, drawdown durations, and stress tests) would require significant tokens and financial domain expertise for a CLI agent. Minor improvement possible: could add visualization helpers or example data handling. Overall, this is a highly valuable skill that meaningfully reduces complexity for risk analytics tasks.

LLM Signals

Description coverage9
Task knowledge10
Structure9
Novelty8

GitHub Signals

26,432
2,921
268
15
Last commit 3 days ago

Publisher

wshobson

wshobson

Skill Author

Related Skills

spark-engineerpandas-proxlsx

Loading SKILL.md…

Try onlineView on GitHub

Publisher

wshobson avatar
wshobson

Skill Author

Related Skills

spark-engineer

Jeffallan

6.4

pandas-pro

Jeffallan

6.4

xlsx

mrgoonie

7.2

infographic-syntax-creator

antvis

6.8
Try online