8 skills · sorted by relevance
Build a synthetic-data CI gate for customer-controlled Abridge integration code without calling undocumented vendor interfaces. Use when adding regression checks to an Abridge deployment repository. Trigger with "test the Abridge integration".
The English summary is being prepared. Showing the original description.
Build a local synthetic rehearsal for customer-owned Abridge workflow adapters and failure states. Use when developing integration code without access to patient data or a documented public Abridge sandbox. Trigger with "create an Abridge dev loop".
The English summary is being prepared. Showing the original description.
Generate synthetic datasets in a Jupyter notebook after the dataops synthetic-data preflight passes. Use when creating new synthetic data or preparing a guarded local replacement candidate from an approved source.
The English summary is being prepared. Showing the original description.
Create diverse synthetic test inputs for LLM pipeline evaluation using dimension-based tuple generation. Use when bootstrapping an eval dataset, when real user data is sparse, or when stress-testing specific failure hypotheses. Do NOT use when you already have 100+ representative real traces (use stratified sampling instead), or when the task is collecting production logs.
The English summary is being prepared. Showing the original description.
Controls authorized cybersecurity exercises through inject scheduling, safety gates, stop and restart decisions, communications, and synthetic-data boundaries. Use when serving as exercise control or white-team coordinator, not as compliance assessor or whitehat operator.
The English summary is being prepared. Showing the original description.
Generate realistic synthetic data using Spark + Faker (strongly recommended). Supports serverless execution, multiple output formats (Parquet/JSON/CSV/Delta), and scales from thousands to millions of rows. For small datasets (<10K rows), can optionally generate locally and upload to volumes. Use when user mentions 'synthetic data', 'test data', 'generate data', 'demo dataset', 'Faker', or 'sample data'.
The English summary is being prepared. Showing the original description.
Build privacy-preserving data sharing platforms using synthetic data generation with the SDV library, data clean rooms, secure enclaves, and utility measurement. Covers end-to-end architecture for sharing analytical datasets while preserving individual privacy guarantees.
The English summary is being prepared. Showing the original description.
Expert guide for synthetic dataset generation, LLM-as-a-judge filtering, QLoRA fine-tuning (Unsloth), DPO alignment, and GGUF/Ollama export for local SLMs / Panduan ahli generasi data sintetis, fine-tuning QLoRA, DPO, dan ekspor GGUF/Ollama.
The English summary is being prepared. Showing the original description.