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Data Design

Checked
miles990·vlatest·database·MIT

Helps with data, design 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 miles990/claude-software-skills --skill data-design -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

miles990

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 miles990/claude-software-skills at 73632f218e.

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

Commit
73632f218e
Manifest
software-design/data-design/SKILL.md
Scanned
3 files, 627 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

This skill enables AI agents to design and structure data models, including defining entities, attributes, relationships, and constraints. It can generate database schemas based on textual descriptions of requirements or existing data formats. The agent can also optimize data designs for performance and scalability.

When to use it

  • Designing a new relational database schema from scratch based on business requirements.
  • Converting an existing data format (like JSON) into a structured database table design.
  • Optimizing an existing database schema for improved query performance.
  • Generating Entity-Relationship Diagrams (ERDs) to visualize the data model.

Key capabilities

  • Entity definition and attribute specification
  • Relationship modeling (one-to-many, many-to-many)
  • Constraint definition (primary keys, foreign keys, unique constraints)
  • Database schema generation (SQL DDL)
  • ERD visualization

Example prompts

  • "Design a database schema for an online bookstore, including tables for books, authors, and customers."
  • "Convert this JSON data into a relational database table design: [JSON data]"
  • "Generate an ERD representing the relationships between students, courses, and instructors."

Tips & gotchas

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