What problem does it solve? Building reproducible data pipelines in R requires consistent structure, dependency tracking, and caching conventions that are easy to get wrong. This Skill encodes targets framework best practices so pipelines are deterministic, cacheable, and debuggable from the start. ## Core Features & Use Cases - Pipeline Structure: Standard file layout with a single _targets.R entry point and function files organized under R/functions/. - Function-Based Design: Enforces pure, roxygen2-documented functions where every target wraps exactly one function. - Branching Patterns: Covers static branching, dynamic branching with map(), and cross patterns for combinatorial workflows. - Reproducibility Contracts: Format selection guidance (qs, parquet, file, rds), invalidation rules, and error handling with workspace debugging. - Use Case: You need to build a pipeline that ingests CSV files, transforms them, validates output, and writes a parquet dataset. This Skill produces the _targets.R definition and function files so tar_make() runs cleanly and only recomputes outdated targets. ## Quick Start Ask Claude to create a targets pipeline that ingests your raw data files, transforms them, and writes a validated output dataset.
