r-data-science
Pass
Audited by Gen Agent Trust Hub on Mar 16, 2026
Risk Level: SAFE
Full Analysis
- [SAFE]: The skill promotes standard data science practices using reputable R packages like the tidyverse, data.table, and arrow for data manipulation and storage.
- [SAFE]: Parallel computing and task management examples (crew, mirai) use industry-standard abstractions for local and distributed (SLURM, AWS Batch) environments, utilizing placeholders for configuration.
- [SAFE]: Pipeline orchestration using the targets package follows best practices for reproducible research, providing clear structures for data dependency tracking and caching.
- [SAFE]: Database connection patterns (DuckDB, Postgres) are implemented using best practices, including the use of dbplyr for lazy evaluation and placeholders for sensitive connection parameters.
- [SAFE]: Machine learning and time series workflows utilize the tidymodels and fable ecosystems, which are maintained by reputable organizations and widely used in the scientific community.
Audit Metadata