Design a Rerun blueprint from the data, then iterate on it from headless screenshots. Read this when laying out a recording or dataset in the viewer, designing a default blueprint, or deciding which views show which entities. Covers archetype-to-view mapping, layout reasoning, the rrb construction API, the contents grammar, and the screenshot loop.
Performance patterns and gotchas for querying a Rerun catalog from Python. Reach for this when a CatalogClient/dataset query is unexpectedly slow, or when shaping a per-segment / per-episode pipeline that hits the catalog from many places.
Core mechanics of the Rerun Chunk Processing API (rerun.experimental) — LazyChunkStream pipelines, Chunk, lenses (MutateLens/DeriveLens/Selector), RrdReader, writing optimized RRDs. Read when building or reviewing any ingestion, conversion, or RRD preprocessing pipeline. Source-specific knowledge lives in the importer skills (rerun-mcap, rerun-urdf, rerun-parquet, rerun-lerobot); read rerun-data-model first to decide what the data should become.
How raw multimodal robot data maps onto the Rerun data model. Read FIRST, before modeling or converting a dataset. Resolves the entity-vs-component, property-vs-component-vs-layer, and static-vs-temporal decisions, then points at the mechanism skills (rerun-chunk-processing and the importer skills rerun-mcap, rerun-urdf, rerun-parquet, rerun-lerobot) for the how.
Ingest a LeRobot (HuggingFace) dataset into Rerun. Read when converting a LeRobot dataset to RRDs, splitting it into per-episode segments, or registering it on a Rerun catalog. Covers the built-in directory importer (log_file_from_path), the RrdReader + send_chunks per-episode split, and when to drop to ParquetReader for custom control.
Ingest MCAP files into Rerun chunk streams with rerun.experimental.McapReader. Read when converting an MCAP recording, selecting topics or decoders, fixing protobuf schemas that ship without compiled descriptors, or when an MCAP-derived stream comes out empty. Builds on rerun-chunk-processing (stream mechanics) and rerun-data-model (what the topics should become).
Ingest tabular Parquet files into Rerun chunk streams with rerun.experimental.ParquetReader. Read when converting trajectory or sensor tables (LeRobot-style parquet, exported logs) into entities and components — column grouping, timeline/index columns, static columns, and ColumnRules that assemble typed components (Transform3D, Scalars) from flat columns. Builds on rerun-chunk-processing and rerun-data-model.
Drive the Rerun URDF API (rerun.urdf.UrdfTree) to ingest a URDF as a Transform3D layer on a robot recording. Read when logging a robot model, running forward kinematics from joint states, composing a fixed chain for sensor extrinsics, or when the transform tree will not connect from the data alone. Builds on rerun-chunk-processing (stream/lens mechanics) and rerun-data-model (entity paths, timeline, base-vs-layer).