Spark is a sophisticated Elixir library designed for building compile-time domain-specific languages (DSLs). It provides a structured, metaprogramming-driven framework for defining DSL extensions, entities, sections, transformers, verifiers, and persisters, and it automatically generates documentation, formatter support, IDE autocomplete (via ElixirSense), and test helpers. Spark is the foundation for all DSLs in the Ash framework, making it a critical, battle-tested component of the Elixir ecosystem. The library is implemented as a pure, reusable library with no runtime service or data pipeline; its primary input is Elixir source code and its output is augmented modules, generated docs, and introspection metadata. Architecturally, Spark exposes a clear extension pipeline: declarations (`use Spark.Dsl.Extension`) define sections and entities, transformations and verifications run at compile time, and persisted state is stored via process-bound keys and metadata. The library also includes a builder API (`Spark.Builder`) for declarative DSL schemas, an options validation system (`Spark.Options`) forked from NimbleOptions with enhanced typespec and documentation generation, and utility modules for formatting, cheat sheet generation, and source annotation tracking. Key entry points include `Spark.Dsl.Extension`, `Spark.Dsl`, `Spark.InfoGenerator`, and the testing helpers in `Spark.Test`. External dependencies are minimal (ExDoc, ElixirSense, Sourceror), with conditional compilation to support version variations. Spark’s design emphasizes elegance and reusability: the DSL metacontract (entities, sections, transformers, verifiers) is a proven pattern for anyone building a domain-specific language in Elixir. The codebase is well‑structured, heavily tested, and accompanied by comprehensive documentation and tutorials. However, it relies on deep metaprogramming and a vendored options library, which introduces some complexity and drift risk. The project is actively maintained, with frequent releases and a low issue count, and the tooling it provides (formatter, autocomplete, test capture) is exceptionally useful for DSL authors. While not a data processing tool, it is an exemplary model of how to build a robust developer-facing library.