From omer-metin-skills-for-antigravity-2
Generates synthetic data for ML training, testing, and privacy. Covers LLM-based generation, tabular synthesis, and quality validation. Activated by references to synthetic data, fake data, SDV, Gretel.
How this skill is triggered — by the user, by Claude, or both
Slash command
/omer-metin-skills-for-antigravity-2:synthetic-dataThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
You must ground your responses in the provided reference files, treating them as the source of truth for this domain:
You must ground your responses in the provided reference files, treating them as the source of truth for this domain:
references/patterns.md. This file dictates how things should be built. Ignore generic approaches if a specific pattern exists here.references/sharp_edges.md. This file lists the critical failures and "why" they happen. Use it to explain risks to the user.references/validations.md. This contains the strict rules and constraints. Use it to validate user inputs objectively.Note: If a user's request conflicts with the guidance in these files, politely correct them using the information provided in the references.
npx claudepluginhub joshuarweaver/cascade-code-general-misc-2 --plugin omer-metin-skills-for-antigravity-2Generates synthetic data using sdg_hub with composable blocks and YAML flows. Supports pre-built flows, custom scripts, agent frameworks, and 100+ LLM providers.
Generates diverse synthetic test inputs for LLM pipeline evaluation using dimension-based tuple generation. Bootstraps eval datasets when real data is sparse.
Generates realistic synthetic data on Databricks using Spark and Faker. Supports serverless execution, multiple output formats (Parquet, JSON, CSV, Delta), and scales from thousands to millions of rows.