Skip to content
TrustedSkillsbeta
Back to all skills

Option Vol Analysis

Official
anthropics·vlatest·data·Apache-2.0

Quantifies implied volatility from options data to assess market sentiment and potential price movements.

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 anthropics/financial-services --skill option-vol-analysis -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

anthropics

Matched to the vendor's own GitHub organisation.

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 anthropics/financial-services-plugins at 574ed3624a.

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

Commit
574ed3624a
Manifest
plugins/partner-built/lseg/skills/option-vol-analysis/SKILL.md
Scanned
1 file, 61 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

The option-vol-analysis skill enables AI agents to perform advanced volatility analysis on financial options, calculating implied volatility surfaces and identifying arbitrage opportunities within option chains. It processes complex market data to derive risk metrics essential for quantitative trading strategies.

When to use it

  • Analyzing implied volatility skew across different strike prices and expiration dates for a specific underlying asset.
  • Detecting statistical arbitrage opportunities by comparing theoretical option prices against real-time market quotes.
  • Stress-testing portfolio risk exposure based on potential shifts in the volatility surface during market turbulence.
  • Generating visual heatmaps of volatility surfaces to communicate complex derivative pricing dynamics to stakeholders.

Key capabilities

  • Calculation of implied volatility surfaces from raw option chain data.
  • Identification of arbitrage conditions within option pricing models.
  • Derivation of risk metrics such as vega and gamma for portfolio management.
  • Processing of multi-asset option datasets for comparative analysis.

Example prompts

  • "Calculate the 30-day implied volatility surface for SPY options expiring in the next month."
  • "Identify any arbitrage opportunities in the current AAPL option chain data provided."
  • "Analyze the vega exposure of my current portfolio based on recent volatility shifts in tech stocks."

Tips & gotchas

Ensure your input data includes complete option chains with accurate bid/ask spreads to avoid skewed volatility calculations. This skill requires a foundational understanding of Black-Scholes-Merton models to interpret the output correctly.