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Csv Data Visualizer

Checked
ailabs-393·vlatest·data·MIT

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 ailabs-393/ai-labs-claude-skills --skill csv-data-visualizer -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

ailabs-393

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 ailabs-393/ai-labs-claude-skills at 1a12bc7aad.

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

Commit
1a12bc7aad
Manifest
packages/skills/csv-data-visualizer/SKILL.md
Scanned
7 files, 1,333 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-visualizer skill enables AI agents to interpret raw CSV files and generate descriptive insights, charts, or summaries without requiring manual data preprocessing. It transforms structured text data into actionable visual representations and narrative reports automatically.

When to use it

  • Quickly analyze sales logs or inventory spreadsheets to identify trends.
  • Generate bar charts or line graphs from survey response data for presentations.
  • Summarize large datasets containing customer feedback or performance metrics.
  • Convert raw numerical records into readable text summaries for non-technical stakeholders.

Key capabilities

  • Parses standard CSV file formats directly within the agent context.
  • Automatically detects data types and structures to ensure accurate analysis.
  • Generates visualizations such as charts and graphs based on data patterns.
  • Produces natural language summaries explaining key findings from the dataset.

Example prompts

  • "Analyze this CSV file of monthly sales figures and create a line chart showing growth over time."
  • "Summarize the customer feedback in this spreadsheet and highlight the most common complaints."
  • "Convert these raw survey results into a bar chart comparing satisfaction scores across different departments."

Tips & gotchas

Ensure your CSV file is properly formatted with consistent delimiters (commas or semicolons) to avoid parsing errors. For complex datasets, specify the desired visualization type explicitly to guide the agent's output format.