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csv-data-summarizer

Checked
coffeefuelbump·v1.0.0·data·MIT

Analyzes CSV files, generates summary stats, and plots quick visualizations using Python and pandas.

Install on your platform

Showing OpenClaw.

  1. 1

    Run this in your terminal

    terminal
    $

What we know about this skill

Publisher

coffeefuelbump

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.

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  • Pinned to a commit: passed

    Scanned coffeefuelbump/csv-data-summarizer-claude-skill at 9b3affd270.

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Commit
9b3affd270
Manifest
SKILL.md
Scanned
5 files, 602 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 analyzes data contained within Comma Separated Value (CSV) files. It leverages Python's pandas library to calculate descriptive statistics like mean, median, standard deviation, and minimum/maximum values for numerical columns. Furthermore, the skill can generate basic visualizations such as histograms or scatter plots based on the CSV data provided.

When to use it

  • Quick Data Exploration: You have a newly acquired dataset in CSV format and need a rapid understanding of its key characteristics.
  • Identifying Outliers: Determine if there are any unusual values within your data that require further investigation.
  • Generating Reports: Create concise summaries of numerical data to include in reports or presentations.
  • Data Quality Checks: Assess the distribution and range of values to identify potential errors or inconsistencies in a dataset.

Key capabilities

  • Calculates descriptive statistics (mean, median, standard deviation, min/max)
  • Generates histograms for numerical columns
  • Creates scatter plots based on two selected columns
  • Uses Python and pandas library for analysis

Example prompts

  • "Summarize the data in 'sales_data.csv' and show me a histogram of the 'revenue' column."
  • "Calculate the mean, median, and standard deviation for all numerical columns in 'customer_data.csv'."
  • "Create a scatter plot showing the relationship between 'advertising_spend' and 'website_visits' from 'marketing_campaigns.csv'."

Tips & gotchas

  • The skill requires access to the CSV file, either through direct upload or a publicly accessible URL.
  • Ensure your CSV file is properly formatted with clear headers for each column; otherwise, the analysis may be inaccurate.