Writing a small executable that uses VectorPipe is straight-forward. The entire main isn’t much more than:

import geotrellis.proj4.WebMercator
import geotrellis.spark._
import geotrellis.spark.tiling._
import geotrellis.vectortile.VectorTile
import org.apache.spark._
import org.apache.spark.rdd.RDD
import vectorpipe._  /* All types and functions. Also exposes the `osm` submodule used below. */

/* Initialize a `SparkContext`, necessary for all `RDD` work */
implicit val sc: SparkContext = new SparkContext(
  new SparkConf().setMaster("local[*]").setAppName("vectorpipe-example")

/* Describe the dimensions of your data area */
val layout: LayoutDefinition =
  ZoomedLayoutScheme.layoutForZoom(15, WebMercator.worldExtent, 512)

/* From an OSM data source, mocked as "empty" for this example */
val (nodes, ways, relations): (RDD[(Long, osm.Node)], RDD[(Long, osm.Way)], RDD[(Long, osm.Relation)]) =
  (sc.emptyRDD, sc.emptyRDD, sc.emptyRDD)

/* All OSM Elements lifted into GeoTrellis Geometry types.
 * Note: type OSMFeature = Feature[Geometry, ElementData]
val features: RDD[osm.OSMFeature] =
  osm.features(nodes, ways, relations).geometries

/* All Geometries clipped to your `layout` grid */
val featGrid: RDD[(SpatialKey, Iterable[osm.OSMFeature])] =
  grid(Clip.byHybrid, logToStdout, layout, features)

/* A grid of Vector Tiles */
val tiles: RDD[(SpatialKey, VectorTile)] =
  vectortiles(Collate.byOSM, layout, featGrid)

/* Further processing here, writing to S3, etc. */

/* Halt Spark nicely */

A full example of processing some OSM XML can be found here.