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Data synthesizer

Skill rafaelghif/antigravity-agents/.agents/skills/data-synthesizer

Enterprise-Grade Guardrails, Zero-Assumption Execution, and Quality Gates for Autonomous AI Coding Agents.

Install
npx -y skills add rafaelghif/antigravity-agents --skill data-synthesizer

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What its author says it does

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Generates realistic mock data and database seeds for UI/API testing.

SKILL.md

1.6 KB, as published. Nobody here has run it

Data Synthesizer Skill

Objective

To inject realistic, varied, and edge-case inclusive mock data into the application so that the agent and developers can accurately test user interfaces, pagination, and API responses.

When to Execute

  • After running schema migrations on an empty database.
  • When requested to test a UI component that depends on lists, charts, or user profiles.
  • When building API endpoints that require robust dummy data.

Execution Steps

  1. Schema Contextualization:

    • Call the schema-manager to read the exact data types, constraints, and relationships of the target tables.
  2. Mock Generation Strategy:

    • Identify the stack's native seeder framework (e.g., Laravel Seeders, Prisma Studio, Python Faker, Django Fixtures).
    • If a framework exists, write seeder scripts utilizing libraries like Faker to generate realistic names, addresses, and emails.
    • Ensure the generated data includes edge cases (very long strings, nulls, special characters) to stress-test the UI.
  3. Injection:

    • Execute the seeder scripts to populate the local database.
    • Verify insertion success by querying a sample of the data.
  4. Cleanup Protocol:

    • Ensure mock generation scripts are placed in proper development directories (e.g., database/seeders) and NEVER executed in production environments.

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