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rerun-lerobot

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.

76

Quality

96%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

93%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.

An efficient, actionable ingestion guide with executable code and a clear two-step workflow; the only gap is the absence of an explicit verification/feedback loop for the batch split-and-register operation.

Suggestions

Add an explicit verification checkpoint after Step 2 (e.g., assert each per-episode RRD has entity paths, or confirm segments appear in the catalog) so the batch operation has a validate->fix->retry loop.

Surface the registration step that the description promises ('registering it on a Rerun catalog') as a concrete Step 3 with code, since the body currently stops at splitting.

DimensionReasoningScore

Conciseness

Lean body that assumes competence — no padding about what LeRobot/Rerun are; explanatory prose is limited to non-obvious gotchas (importer bootstrap caveat, zero-padding, send_properties=False) that each earn their tokens.

5 / 5

Actionability

Two copy-paste-ready code blocks cover the common path end-to-end — snapshot_download + log_file_from_path, then RrdReader loop with send_chunks — with real API calls and concrete parameters.

5 / 5

Workflow Clarity

Clear Step 1 / Step 2 sequence with an inline validation check (skip stores with no entity paths) and a Gotchas checklist, but no explicit output-verification checkpoint or validate->fix->retry feedback loop for this batch per-episode operation.

4 / 5

Progressive Disclosure

Well-organized into intro, Step 1, Step 2, Gotchas, and References; cross-skill pointers (rerun-chunk-processing, rerun-data-model) and the external dataloader reference are one level deep and clearly signaled, appropriate for a single-purpose skill with no bundle files.

5 / 5

Total

19

/

20

Passed

Description

100%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 highly specific, well-triggered description that concretely states both capability and use conditions with natural user terminology and minimal conflict risk.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'Ingest a LeRobot (HuggingFace) dataset into Rerun', 'converting ... to RRDs, splitting it into per-episode segments, or registering it on a Rerun catalog' — plus specific API surfaces (log_file_from_path, RrdReader + send_chunks, ParquetReader), giving comprehensive coverage.

5 / 5

Completeness

Explicitly answers both what ('Ingest a LeRobot ... dataset into Rerun') and when ('Read when converting ... splitting ... or registering it on a Rerun catalog') with concrete trigger phrases.

5 / 5

Trigger Term Quality

Covers the natural terms a user would say — 'LeRobot', 'HuggingFace', 'Rerun', 'RRDs', 'per-episode segments', 'catalog' — including the synonym HuggingFace and the operation verbs (convert, split, register).

5 / 5

Distinctiveness Conflict Risk

Occupies a clear niche (LeRobot datasets into Rerun, catalog registration) with distinct triggers; the named formats and APIs make overlap with other skills minimal.

5 / 5

Total

20

/

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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