rerun-io

@rerun-io source repo

13 skills
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7 hours ago last updated

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Datafusion Python · rerun-io
Use when the user is writing datafusion-python (Apache DataFusion Python bindings) DataFrame or SQL code. Covers imports, data loading, DataFrame operations, expression building, SQL-to-DataFrame mappings, idiomatic patterns, and common pitfalls.

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Rerun Catalog Queries · rerun-io
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.

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Rerun Mp4 · rerun-io
Ingest .mp4 video into Rerun chunk streams with rerun.experimental.Mp4Reader. Read when converting video into a VideoStream, choosing stream vs asset mode, transcoding (B-frames, output codec, GOP size) through FFmpeg, or aligning video PTS onto a recording's wall-clock timeline. Builds on rerun-chunk-processing (stream mechanics) and rerun-data-model (where the video belongs in the recording).

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Rerun Mcap · rerun-io
Ingest MCAP files into Rerun chunk streams with rerun.chunk.McapReader. Read when converting an MCAP recording, selecting topics or decoders, decoding custom protobuf messages, 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).

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Rerun Urdf · rerun-io
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).

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Rerun Lerobot · rerun-io
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.

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Rerun Parquet · rerun-io
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 lenses (DeriveLens) that assemble the typed components (Transform3D, Scalars) from the reader's grouped struct/scalar output. Builds on rerun-chunk-processing and rerun-data-model.

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Rerun Blueprint · rerun-io
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.

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Rerun Data Model · rerun-io
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.

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Assemble Changelog · rerun-io
Assemble the per-PR changeset entries in docs/content/changelog/upcoming/ into a release's changeset-0-XX.md and generate the detailed CHANGELOG.md sections. Use at release time when the user wants to build or finalize the changelog or changeset for a Rerun release, or merge the upcoming/ entries. Invoked via /assemble-changelog or "assemble the changelog for 0.x.y".

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Rerun Chunk Processing · rerun-io
Core mechanics of the Rerun Chunk Processing API (rerun.experimental) — LazyChunkStream pipelines, Chunk, lenses (MutateLens/DeriveLens/Selector), RrdReader, writing optimized RRDs. Read BEFORE writing any ingestion/conversion/preprocessing code (convert an MCAP, build a recording from a dataset, preprocess an .rrd, port an old converter): it mandates reader+lens pipelines and steers away from hand-built chunks — no Chunk.from_columns for data a reader/lens can produce, no per-message rr.log, no manual pa.array assembly. Source-specific knowledge lives in the importer skills (rerun-mcap, rerun-urdf, rerun-parquet, rerun-mp4, rerun-lerobot); read rerun-data-model first to decide what the data should become.

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Add Model · rerun-io bundle
Bring an upstream research model (stereo, mono depth, normals, pose, ...) into this monorepo: fork + pixify the upstream repo with a Rerun demo, then port the minimum into a package as a typed, tested, reviewable PR stack that ends with pixel-verified Rerun output. Use when the user says "add/port/bring in <model>", "fork and pixify <repo>", or "make <method> a predictor". Works from Claude Code and Codex; nothing here depends on runtime-specific tools.

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Fuzz · rerun-io bundle
Run a full lance-fuzz campaign in this repo — bump to the latest Lance tag, verify the tracked crash suite still reproduces, clear the corpus for a clean run, fuzz dataset_ops for a caller-given duration while classifying new crashes as they appear, minimize every finding into a standalone bugs/<slug>/ reproducer afterward, and ask before spending time on a coverage report. Use when the user asks to run a fuzz campaign, port Lance to a new version and fuzz it, or do an overnight/timed fuzzing run in lance-fuzz.

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