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Data Seeding

Checked
sgcarstrends·vlatest·data·MIT

Automatically generates realistic synthetic data to augment datasets and improve model training accuracy.

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 motormetrics/motormetrics --skill data-seeding -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

sgcarstrends

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 sgcarstrends/sgcarstrends at d852d8c519.

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

Commit
d852d8c519
Manifest
.agents/skills/data-seeding/SKILL.md
Scanned
2 files, 296 lines
Copies in repo
2 — all of them scanned
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 automatically generates synthetic data to populate databases, primarily for development, testing, and staging environments. It allows you to create realistic datasets by defining seed scripts that insert or update records in database tables. The skill supports generating small, predictable datasets for development, minimal data for testing, and larger, production-like datasets for staging. It also includes the option to use Faker.js for more realistic data generation.

When to use it

  • Populating a new development environment: Quickly create a database with sample data for initial development work.
  • Setting up test databases: Generate consistent, repeatable data for automated testing scenarios.
  • Creating staging environments: Populate a staging database with realistic data that mimics production conditions.
  • Augmenting existing datasets: Add synthetic records to expand the size of an existing dataset and improve model training accuracy (though this is not explicitly detailed in the source).

Key capabilities

  • Seed Script Execution: Runs seed scripts located within packages/database/src/seed/.
  • Table-Specific Seeding: Allows seeding individual tables, such as db:seed:cars.
  • Faker.js Integration: Utilizes Faker.js to generate realistic data for fields like make, model, and month.
  • Environment-Specific Seeds: Supports different seed configurations based on the environment (development, test, staging).
  • Idempotent Seed Operations: Provides options for upserting data (insert or update) using onConflictDoUpdate.
  • Database Clearing: Includes an optional function to clear existing database data before seeding.

Example prompts

Since this is a code-running skill, example prompts would be commands to execute:

  • pnpm -F @sgcarstrends/database db:seed (Run all seed scripts)
  • pnpm -F @sgcarstrends/database db:seed:cars (Seed only the 'cars' table)
  • pnpm -F @sgcarstrends/database add -D @faker-js/faker (Install Faker.js dependency)

Tips & gotchas

  • Dependency: Requires pnpm to be installed and configured correctly for execution.
  • Environment Variables: The NODE_ENV environment variable determines which seed scripts are executed.
  • Database Schema: The skill relies on a pre-existing database schema defined in packages/database/src/db/schema.