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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

84%

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

76%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 high-signal, actionable reference for a non-trivial experimental API, with complete runnable examples and a useful builder table. Its main weakness is workflow clarity: the end-to-end process is implied rather than presented as an explicit, checkpointed sequence.

Suggestions

Add an explicit numbered "Workflow" section (1. fill the rerun-data-model mapping table → 2. configure ParquetReader.stream() → 3. drop /__properties → 4. apply lenses → 5. .to_chunks()) with the stream() schema validation framed as a checkpoint and an error-recovery note, rather than leaving the sequence implied across sections.

De-duplicate the Gotchas section against the body: gotchas #3, #4, #5, #9 repeat the static_columns raise behavior, the `data` struct naming, prefix-stripped field names, and the keep-columnar guidance already stated in their respective sections — collapse these to one-line cross-references or remove the redundancy.

Consider moving the full lens builder table and the Selector grammar into a bundled reference file (e.g. references/lens-builders.md) referenced from SKILL.md, so the main file stays a lean overview and progressive disclosure reaches a cleaner split.

DimensionReasoningScore

Conciseness

Largely efficient and free of basic-concept over-explanation, but the Gotchas section restates body content (static_columns raise behavior, the `data` struct naming, prefix-stripped field names, keep-the-pipeline-columnar) that could be trimmed; short of the every-token-earns-its-place leanness of a 5.

4 / 5

Actionability

Provides copy-paste-ready runnable examples with imports for the primary flows (end-to-end Transform3D, Points3D via to_packed_component), an exhaustive concrete builder signature table, and a help command — covering the common cases fully.

5 / 5

Workflow Clarity

The process is only implied (fill the rerun-data-model mapping table, configure stream(), drop /__properties, apply lenses) and validation exists (schema check at stream(), static-column raise), but there is no explicit numbered sequence with checkpoints or error-recovery framing, matching the 3 anchor's implicit-sequence characterization.

3 / 5

Progressive Disclosure

Well-sectioned with a clearly signaled References section pointing one level deep to source files and sibling skills, but it is a monolithic ~240-line document with the builder table and selector grammar inlined rather than split into bundled reference files, so it does not reach the 5 anchor's "content appropriately split".

4 / 5

Total

16

/

20

Passed

Description

92%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 clearly states capabilities, gives an explicit "Read when" trigger with concrete phrases, and draws clear boundaries against sibling skills. The only minor gap is trigger-term breadth — no file extension or plain-synonym variants.

DimensionReasoningScore

Specificity

Names multiple concrete actions — "Ingest tabular Parquet files into Rerun chunk streams", "column grouping, timeline/index columns, static columns", and "lenses (DeriveLens) that assemble the typed components (Transform3D, Scalars)" — giving comprehensive coverage rather than the minor gaps of a 4.

5 / 5

Completeness

Explicitly answers both what (ingest via ParquetReader, the column/timeline/lens operations) and when ("Read when converting trajectory or sensor tables ... into entities and components") with concrete trigger phrases, matching the 5 anchor.

5 / 5

Trigger Term Quality

Good natural-term coverage ("Parquet files", "trajectory or sensor tables", "LeRobot-style parquet", "exported logs") but no file extension or plain synonyms like "convert parquet", short of the comprehensive synonym/extension coverage of a 5.

4 / 5

Distinctiveness Conflict Risk

Occupies a clear niche (Rerun parquet ingestion via rerun.experimental.ParquetReader) with distinct triggers and explicit boundary setting ("Builds on rerun-chunk-processing and rerun-data-model"), minimizing conflict risk.

5 / 5

Total

19

/

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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