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Data Visualization

Checked
inference-sh·vlatest·data

Visually explore inferred relationships and patterns within your data using interactive charts and graphs generated by inference-sh.

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 inference-sh/skills --skill data-visualization -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

inference-sh

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 inference-sh/skills at c2d5678f14.

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

Commit
c2d5678f14
Manifest
guides/design/data-visualization/SKILL.md
Scanned
1 file, 195 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

Data Visualization Skill Guide

What it does

This skill enables AI agents to generate and render interactive charts, graphs, and visual representations directly from structured data inputs. It transforms raw datasets into clear, actionable insights without requiring manual export or external tooling.

When to use it

  • Exploratory Analysis: Quickly visualize trends in sales figures or user metrics during a brainstorming session.
  • Report Generation: Create summary charts for executive reports based on database queries.
  • Dashboard Creation: Build dynamic dashboards that update automatically as new data streams in.
  • Educational Explanation: Illustrate complex statistical concepts like distributions or correlations for non-technical audiences.

Key capabilities

  • Renders various chart types including bar, line, scatter, and pie charts.
  • Accepts structured data formats such as CSV, JSON, and SQL query results.
  • Automatically selects appropriate visualization types based on data patterns.
  • Generates publication-ready graphics with customizable styling options.

Example prompts

  • "Create a line chart showing monthly revenue growth over the last year from this CSV file."
  • "Generate a scatter plot comparing customer age against purchase frequency using the provided dataset."
  • "Visualize the distribution of product categories in our inventory as a pie chart."

Tips & gotchas

Ensure your data is clean and properly formatted before visualization, as malformed inputs can lead to inaccurate charts. For best results, specify the desired chart type explicitly if you have a particular analytical goal in mind.