Data synthesizer
Skill rafaelghif/antigravity-agents/.agents/skills/data-synthesizer
Enterprise-Grade Guardrails, Zero-Assumption Execution, and Quality Gates for Autonomous AI Coding Agents.
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Generates realistic mock data and database seeds for UI/API testing.
SKILL.md
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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
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Schema Contextualization:
- Call the
schema-managerto read the exact data types, constraints, and relationships of the target tables.
- Call the
-
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.
-
Injection:
- Execute the seeder scripts to populate the local database.
- Verify insertion success by querying a sample of the data.
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Cleanup Protocol:
- Ensure mock generation scripts are placed in proper development directories (e.g.,
database/seeders) and NEVER executed in production environments.
- Ensure mock generation scripts are placed in proper development directories (e.g.,