Skip to content
TrustedSkillsbeta
Back to all skills

Distribution Analyzer

Listed
dengineproblem·vlatest·data

Analyzes data distributions to reveal patterns & insights, helping you understand your dataset's characteristics and make informed decisions.

Repository

Install on your platform

This skill no longer installs under the name distribution-analyzer. dengineproblem/agents-monorepo is deleted or private, so it cannot be installed from.

We check each listing against its upstream repository with the rules the skills CLI uses, last on 2026-10-22. We don't show a command we know will fail.

Repository install instructions

What we know about this skill

Publisher

dengineproblem

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.

Nobody has reviewed this code. TrustedSkills indexes and links skills; it does not audit, run or scan them. A skill runs with whatever access you give your agent — read the source before you install it.

Automated safety checks

scanned

We could not read this skill's source, so none of the checks below were run.

Not scanned. repository not found or private Source recorded as dengineproblem/agents-monorepo.
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 distribution-analyzer skill processes data to identify and visualize statistical distributions, helping AI agents understand the underlying patterns within datasets. It transforms raw numerical inputs into actionable insights regarding frequency, central tendency, and variance.

When to use it

  • Analyzing survey results to determine if responses follow a normal or skewed distribution.
  • Detecting outliers in sensor data streams before they impact system reliability.
  • Evaluating the spread of financial returns to assess investment risk profiles.
  • Validating whether experimental data meets assumptions required for specific statistical tests.

Key capabilities

  • Identifies and characterizes various statistical distribution types (e.g., normal, uniform, exponential).
  • Generates visual representations of data frequency and density.
  • Calculates key statistical metrics such as mean, median, mode, and standard deviation.
  • Compares multiple datasets to highlight differences in their distributional properties.

Example prompts

  • "Analyze the attached sales figures and describe the distribution pattern you observe."
  • "Visualize the frequency of user login times over the last month using a distribution plot."
  • "Determine if this temperature dataset follows a normal distribution and calculate the standard deviation."

Tips & gotchas

Ensure your input data is clean and numerical, as text or categorical-only inputs will not yield valid distributional analysis. For large datasets, consider sampling first to prevent performance bottlenecks during visualization generation.