synthetic-data-workflow

synthetic-data-workflow is a Fine-tuning & training data skill for AI agents, published by DAAF-Contribution-Community in the daaf repository on GitHub. Skillspace rates synthetic-data-workflow D (68/100) and it passed the safety scan; the rating is based on static analysis only.

Privacy-preserving workflow for building synthetic datasets and data source skills when raw data must never enter the DAAF container. The user profiles their sensitive data locally with a disclosure-controlled script; only a summary profile report crosses the boundary; DAAF builds a synthetic dataset and skill from the report alone. Use whenever data is sensitive, proprietary, PII-bearing, HIPAA/FERPA-governed, held in a secure enclave, or the user says the data cannot leave their environment, they cannot upload it, or asks to profile it locally. Covers a four-tier disclosure ladder (T1 schema, T2 marginals, T3 relationships, T4 local high-fidelity synthesis), profile-only generation with simstudy (R) / NumPy-SciPy copulas (Python), and three-part QA (disclosure-safety, report consistency, synthetic-vs-profile validation). Not for synthetic control, the causal-inference method; for that see data-scientist. Synthetic data here is a code-development scaffold, not an analytic substitute.

Grade

D

Quality

68 / 100

Safety

Passed

GitHub stars

220

Unproven: rated by static analysis only, not yet compared head-to-head.

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