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Csv Data Summarizer

Checked
coffeefuelbump·vlatest·data

Helps with CSV, data as part of writing, documentation, and content creation workflows.

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 coffeefuelbump/csv-data-summarizer-claude-skill --skill csv-data-summarizer -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

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.

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

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

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

The csv-data-summarizer skill enables AI agents to analyze and summarize data from CSV files. It can extract key insights, identify trends, and present the information in a concise format suitable for reporting or decision-making.

When to use it

  • You need a quick overview of large datasets without manually reviewing all rows.
  • You want to highlight important patterns or anomalies within the data.
  • You're preparing a report and require structured summaries from raw CSV files.
  • You are automating data analysis workflows for regular reporting tasks.

Key capabilities

  • Extracts summary statistics (e.g., averages, totals) from numerical columns.
  • Identifies and highlights unique or repeated values in categorical columns.
  • Detects trends over time if the dataset includes a date/time column.
  • Outputs summaries in plain text or structured formats like JSON or markdown.

Example prompts

  • "Summarize this CSV file and highlight any unusual patterns."
  • "Generate a report with key statistics from the sales data in this CSV."
  • "What are the most common categories in this dataset?"

Tips & gotchas

  • Ensure your CSV files are properly formatted with headers for accurate parsing.
  • Large datasets may take longer to process, so consider optimizing or sampling the data if needed.