rerun-data-model

name	rerun-data-model
description	How raw multimodal robot data maps onto the Rerun data model. Read FIRST, before modeling or converting a dataset — and whenever you are about to convert/ingest/preprocess robot data into an .rrd or build a Rerun recording, even if not asked for the data model. Resolves the entity-vs-component, property-vs-component-vs-layer, and static-vs-temporal decisions and routes to the mechanism (do it with readers and lenses, not hand-built chunks or per-message rr.log): rerun-chunk-processing and the importer skills rerun-mcap, rerun-urdf, rerun-parquet, rerun-mp4, rerun-lerobot.
user_invocable	true
allowed-tools	Read, Grep, Bash, WebFetch

Rerun data model

The hard part of ingesting a dataset is the modeling decision, not the API call. Get the model right and any mechanism works; get it wrong and queries, views, and training all break. This skill is just the decisions. For mechanism details see rerun-chunk-processing (pipeline mechanics) and the importer skills it routes to: rerun-mcap,rerun-urdf,rerun-parquet, rerun-mp4, rerun-lerobot. For exact signatures, the docs at rerun.io/docs/concepts/logging-and-ingestion.

Before writing conversion code, fill in the mapping table below. It is the design, and a human can review it in seconds.

Pick the mechanism before you model the bytes

Modeling decides each datum becomes; this decides it gets there — and the default is :