claude

Synthetic Data

Generate synthetic datasets from schemas, real data, or LLM-driven personas. Tabular fit-and-sample (SDV: GaussianCopula, CTGAN, TVAE), Faker/Mimesis schema generation, deterministic PII swap, LLM-driven real-to-synth conversion for unstructured records, and SDMetrics-based quality/privacy evaluation (plus embedding-based leakage checks for text).

31 on GitHubby Daniel Rosehillv0.1.0MITupdated yesterdayData & Datasets
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Plugin synthetic-data from marketplace danielrosehill in danielrosehill/Claude-Code-Plugins. Commands read from the repo's manifest — today.

What it installs

Skills (8)

  • evaluate-quality

    Run fidelity, utility, and privacy diagnostics on a synthetic dataset against the real source, generating a markdown report.

  • real-to-synth-llm

    Transform real text records into synthetic counterparts that preserve semantics while changing specifics, using Claude.

  • replace-pii

    Anonymise a real dataset by replacing PII columns with realistic Faker values, preserving structure and referential integrity.

  • setup-workspace

    Initialize a synthetic data workspace directory with standard folders, Python requirements, and venv setup.

  • tabular-from-real

    Fit a generative model on real tabular data and sample synthetic rows preserving correlations and distributions.

  • tabular-from-schema

    Generate a synthetic tabular dataset from a JSON schema describing columns, types, and Faker providers.

  • text-records-llm

    Generate synthetic text records (support tickets, reviews, medical notes, etc.) using Claude based on a persona pool and schema.

  • tools-reference

    Reference card of open-source synthetic data tools, when to use each, install commands, and design patterns.

View source on GitHub blog.danielrosehill.com

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