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rerun-parquet

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.

68

Quality

83%

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SKILL.md
Quality
Evals
Security

Quality

Content

82%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A lean, highly actionable skill body with executable examples and a well-organized reference section. The main trim opportunity is intro/Gotchas redundancy, and the API reference could optionally be split into a bundle file.

DimensionReasoningScore

Conciseness

Dense and information-rich, assuming competence (no padding about what parquet/Rerun is); minor redundancy where the intro's 'lenses not pandas' and the /__properties drop are restated in the Gotchas section.

4 / 5

Actionability

Fully executable copy-paste examples (Transform3D and Points3D lens flows, the .drop() call) plus a complete builder table with concrete argument orders cover the common cases.

5 / 5

Workflow Clarity

A clear conceptual sequence is given ('fill in the rerun-data-model mapping table first, then express it through stream()') with schema validation noted at stream(); it is not framed as an explicit numbered validate-fix-retry loop, but the operation is a non-destructive read so the batch/destructive cap does not apply.

4 / 5

Progressive Disclosure

Well-sectioned body with a clearly signaled one-level-deep References section pointing to package source, integration tests, and sibling skills; no bundle files exist, so the inlined API reference (builder table, selector grammar) is a minor externalization gap rather than a structural flaw.

4 / 5

Total

17

/

20

Passed

Description

83%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong, specific description that names concrete capabilities and an explicit use trigger, well-distinguished from sibling skills. It could improve trigger-term naturalness by adding user-utterance phrasings and the .parquet extension.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — ingest parquet into Rerun chunk streams, column grouping, timeline/index columns, static columns, and lenses assembling typed components (Transform3D, Scalars) — giving comprehensive coverage of the reader's capabilities.

5 / 5

Completeness

Clearly states what ('Ingest tabular Parquet files into Rerun chunk streams with rerun.experimental.ParquetReader') and an explicit when ('Read when converting trajectory or sensor tables...'), but the when is scenario-framed rather than the 'when the user mentions X' trigger form of the top anchor.

4 / 5

Trigger Term Quality

Good natural-term coverage with 'Parquet files', 'LeRobot-style parquet', 'exported logs', and 'trajectory or sensor tables', but it leans technical (DeriveLens, chunk streams) and omits the .parquet extension and common user phrasings.

4 / 5

Distinctiveness Conflict Risk

Occupies a clear niche (ParquetReader/DeriveLens parquet ingestion) and explicitly bounds itself against sibling skills rerun-chunk-processing and rerun-data-model, minimizing conflict risk.

5 / 5

Total

18

/

20

Passed

Validation

87%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

allowed_tools_field

'allowed-tools' contains unusual tool name(s)

Warning

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

14

/

16

Passed

Repository
rerun-io/rerun
Reviewed

Table of Contents

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