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Data Modeler

Checked
matteocervelli·vlatest·database·MIT

Automatically generates relational database schemas from unstructured data descriptions, optimizing for efficiency and clarity.

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 matteocervelli/llms --skill data-modeler --full-depth -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

matteocervelli

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 matteocervelli/llms at 1e1f2089eb.

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

Commit
1e1f2089eb
Manifest
.archive/claude-v1/skills/data-modeler/SKILL.md
Scanned
3 files, 2,049 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 data-modeler skill allows AI agents to design and define database schemas. It can generate table structures, including column names, data types, and relationships between tables based on textual descriptions of the desired data organization. This facilitates efficient data storage and retrieval for various applications.

When to use it

  • Designing a new application: Define the database schema upfront before development begins.
  • Refactoring an existing database: Generate updated schemas based on evolving requirements.
  • Data migration projects: Create target database structures for migrating data from one system to another.
  • Prototyping and experimentation: Quickly explore different database designs without manual coding.

Key capabilities

  • Generates table schemas from natural language descriptions.
  • Defines column names, data types, and constraints.
  • Establishes relationships between tables (e.g., foreign keys).

Example prompts

  • "Create a database schema for managing customer orders, including customers, products, and order details."
  • "Design a table to store book information with fields for title, author, ISBN, and publication date."
  • “Generate a relational model for tracking employee attendance.”

Tips & gotchas

The quality of the generated schema depends heavily on the clarity and completeness of the input description. Providing detailed requirements will yield better results.