Skip to content
TrustedSkillsbeta
Back to all skills

Risk Metrics Calculation

Checked
wshobson·vlatest·devops·MIT

Calculates key risk metrics from data to provide quantifiable insights and inform decision-making regarding potential threats.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add wshobson/agents --skill risk-metrics-calculation -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

wshobson

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned wshobson/agents at 46891e7e60.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
46891e7e60
Manifest
plugins/quantitative-trading/skills/risk-metrics-calculation/SKILL.md
Scanned
2 files, 546 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

The risk-metrics-calculation skill enables AI agents to compute and analyze various financial and operational risk metrics. It supports calculations such as Value at Risk (VaR), Expected Shortfall, volatility measures, and other quantitative tools used in risk management.

When to use it

  • To assess potential losses in investment portfolios under different market conditions.
  • For evaluating the risk exposure of a business operation or financial product.
  • When generating reports for compliance with regulatory requirements in finance or insurance.
  • During scenario analysis to understand how risks might impact decision-making processes.

Key capabilities

  • Calculation of Value at Risk (VaR) and Expected Shortfall
  • Computation of volatility metrics
  • Integration with historical data for risk modeling
  • Support for stress testing scenarios

Example prompts

  • "Calculate the VaR for a stock portfolio over a 10-day period with 95% confidence."
  • "Compute the expected shortfall for this financial instrument under a market crash scenario."
  • "Analyze the volatility of this dataset and provide risk metrics."

Tips & gotchas

  • Ensure input data is clean, complete, and relevant to the metric being calculated.
  • Some advanced features may require access to historical or real-time financial data sources.