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Csv Data Summarizer

Checked
ovachiever·vlatest·data

Helps with CSV, data as part of agent workflows 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 ovachiever/droid-tings --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

ovachiever

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 ovachiever/droid-tings at 7acd12a754.

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

Commit
7acd12a754
Manifest
skills/csv-data-summarizer/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

The CSV Data Summarizer skill allows AI agents to ingest raw comma-separated values files and automatically generate concise, structured summaries of the contained data. It transforms unstructured tabular inputs into readable insights without requiring manual parsing or external database connections.

When to use it

  • Quickly audit large datasets to identify trends, outliers, or missing entries before deeper analysis.
  • Convert raw export logs from applications like Excel or Google Sheets into natural language reports for stakeholders.
  • Validate data integrity by having an agent scan a file and highlight specific rows that deviate from expected patterns.

Key capabilities

  • Reads and parses standard CSV file formats directly within the agent's context.
  • Analyzes column headers and row values to infer data types and relationships.
  • Generates text-based summaries highlighting key statistics, anomalies, or overall data health.

Example prompts

  • "Summarize the attached sales.csv file and tell me which product category had the highest revenue last quarter."
  • "Scan this employee_data.csv for any rows where the 'start_date' is missing or invalid."
  • "Read the inventory_levels.csv and provide a brief overview of items currently below their minimum stock threshold."

Tips & gotchas

Ensure your CSV files are properly formatted with consistent delimiters to avoid parsing errors. For very large datasets, consider summarizing specific columns first rather than processing the entire file at once to maintain performance.