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Variance Analysis

Official
anthropics·vlatest·data·Apache-2.0

Quantifies data fluctuations to identify trends, anomalies, and potential risks across diverse datasets.

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/knowledge-work-plugins --skill variance-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/knowledge-work-plugins at da38ec1ee8.

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

Commit
da38ec1ee8
Manifest
finance/skills/variance-analysis/SKILL.md
Scanned
1 file, 249 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

This skill enables AI agents to perform statistical variance analysis on datasets, identifying how individual data points deviate from the mean. It helps quantify the spread or dispersion within a dataset to reveal patterns of consistency or volatility.

When to use it

  • Analyzing financial reports to detect unusual fluctuations in revenue or expenses compared to historical averages.
  • Evaluating manufacturing quality control metrics to determine if product dimensions vary significantly from the target specification.
  • Assessing marketing campaign performance by measuring how much individual channel results diverge from the overall average ROI.

Key capabilities

  • Calculates mean and variance values for numerical datasets.
  • Identifies outliers based on statistical deviation thresholds.
  • Interprets data spread to highlight consistency or volatility in trends.

Example prompts

  • "Analyze the sales figures for Q3 and calculate the variance to see if performance was consistent across all weeks."
  • "Identify any data points in this temperature log that show high variance compared to the daily average."
  • "Compare the variance of customer wait times between the morning and afternoon shifts to determine which period is less predictable."

Tips & gotchas

Ensure your input data is numeric and relatively clean, as extreme outliers can skew variance calculations significantly. For small datasets, remember that variance estimates may be less reliable than for larger samples.