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risk-metrics-calculation

Calculate portfolio risk metrics including VaR, CVaR, Sharpe, Sortino, and drawdown analysis

Install / Use

npx skills add wshobson/agents --skill risk-metrics-calculation

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

83/100

Category

Operations

Supported Platforms

Universal

Tags

Our assessment of risk-metrics-calculation

risk-metrics-calculation scores 83/100 on our quality scale, 173rd of 259 Operations skills we index.

Its SKILL.md is 2.0 KB long, well organised into 9 sections with 1 code example: moderately detailed.

With 39,920 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
20/30
Structure
17/20
Description
12/15
Adoption
20/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 5 days ago, so risk-metrics-calculation is actively maintained.
  • It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

Safety scan

No issues found

Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands.

Automated pattern scan on 2026-09-25. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.

risk-metrics-calculation compared with similar skills

All 4 of these similar skills score higher than risk-metrics-calculation; compare them before choosing.

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Frequently asked questions

How do I install risk-metrics-calculation?
Run npx skills add wshobson/agents --skill risk-metrics-calculation. The install tabs above show the steps for each supported agent.
Which AI agents does risk-metrics-calculation work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is risk-metrics-calculation safe to use?
Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is risk-metrics-calculation still maintained?
The repository was last updated 5 days ago, so risk-metrics-calculation is actively maintained.

name: risk-metrics-calculation description: 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 Calculation

Comprehensive risk measurement toolkit for portfolio management, including Value at Risk, Expected Shortfall, and drawdown analysis.

When to Use This Skill

  • Measuring portfolio risk
  • Implementing risk limits
  • Building risk dashboards
  • Calculating risk-adjusted returns
  • Setting position sizes
  • Regulatory reporting

Core Concepts

1. Risk Metric Categories

| Category | Metrics | Use Case | | ----------------- | --------------- | -------------------- | | Volatility | Std Dev, Beta | General risk | | Tail Risk | VaR, CVaR | Extreme losses | | Drawdown | Max DD, Calmar | Capital preservation | | Risk-Adjusted | Sharpe, Sortino | Performance |

2. Time Horizons

Intraday:   Minute/hourly VaR for day traders
Daily:      Standard risk reporting
Weekly:     Rebalancing decisions
Monthly:    Performance attribution
Annual:     Strategic allocation

Detailed patterns and worked examples

Detailed pattern documentation lives in references/details.md. Read that file when the navigation tier above is insufficient.

Best Practices

Do's

  • Use multiple metrics - No single metric captures all risk
  • Consider tail risk - VaR isn't enough, use CVaR
  • Rolling analysis - Risk changes over time
  • Stress test - Historical and hypothetical
  • Document assumptions - Distribution, lookback, etc.

Don'ts

  • Don't rely on VaR alone - Underestimates tail risk
  • Don't assume normality - Returns are fat-tailed
  • Don't ignore correlation - Increases in stress
  • Don't use short lookbacks - Miss regime changes
  • Don't forget transaction costs - Affects realized risk

Related Skills

View on GitHub
GitHub Stars39.9k
CategoryOperations
Updated5d ago
Forks4.3k

Languages

Python

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions