CtrlK
BlogDocsLog inGet started
Tessl Logo

rerun-data-model

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.

66

Quality

81%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

75%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 well-organized, decision-focused skill body that delivers concrete API names, a worked mapping table, and clear sequencing with a design-checkpoint. It appropriately defers mechanism details to sibling skills; the main gaps are the absence of a literal code example and the lack of any local bundle files to validate internal progressive disclosure.

Suggestions

Add one short, complete copy-paste code snippet (e.g., reader.stream() + a lens producing a chunk) to lift actionability toward fully executable guidance.

Introduce an explicit validate→fix→retry checkpoint for the mapping-table step (e.g., 'review the table against the source schema; if a row cannot name an archetype/component, re-resolve before coding') to strengthen the feedback loop.

Consider moving the 7-item gotchas list into a short local references file (e.g., references/gotchas.md) so the main body stays a lean overview and progressive disclosure has an internal one-level-deep target.

DimensionReasoningScore

Conciseness

The body is lean and decision-focused ('This skill is just the decisions') and avoids re-explaining Rerun or robotics basics, but contains a few framing sentences ('Modeling decides _what_… this decides _how_') and a long gotchas list that occasionally re-states concepts; mostly efficient with minor trims possible, so not a 5.

4 / 5

Actionability

Provides concrete, executable guidance — named readers (McapReader, UrdfTree, ParquetReader, RrdReader, RrdReader, log_file_from_path), exact archetypes (Scalars, Points3D, Pinhole, VideoStream), a worked mapping table with real example rows, and exact indexing ('[0]/[0][0] (0-based DataFrame, 1-based SQL)'); falls short of 5 only because there is no literal copy-paste code block, though the named APIs are specific enough to act on.

4 / 5

Workflow Clarity

Sequences a clear design workflow (pick mechanism → model → decisions → mapping table → patterns → gotchas) with an explicit checkpoint ('Before writing conversion code, fill in the mapping table below … a human can review it in seconds'); not a 5 because the validation is a reviewable design artifact rather than an explicit validate→fix→retry feedback loop, and the skill is decision/design-oriented rather than batch-destructive so the destructive-op cap does not apply.

4 / 5

Progressive Disclosure

The body acts as an overview and repeatedly routes out one level deep to clearly signaled sibling skills ('For mechanism details see rerun-chunk-processing,' 'See rerun-catalog-queries,' the importer skills), keeping this file appropriately high-level; not a 5 because no local bundle files exist (references/, scripts/, assets/ are absent) and all references point to external sibling skills rather than an internal bundle structure.

4 / 5

Total

16

/

20

Passed

Description

87%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 explicitly covers both what the skill does and when to use it, with concrete trigger phrases and clear routing to sibling skills. Minor room to tighten the 'what' into discrete enumerated actions and add a couple more natural synonyms.

DimensionReasoningScore

Specificity

Names concrete actions — 'maps onto the Rerun data model,' 'Resolves the entity-vs-component, property-vs-component-vs-layer, and static-vs-temporal decisions,' 'routes to the mechanism' — listing several specific decisions and routing targets rather than vague language; falls short of a 5 only because the actions are framed as decision-resolution rather than an enumerated set of discrete operations.

4 / 5

Completeness

Explicitly answers both 'what' ('maps raw multimodal robot data onto the Rerun data model … resolves the … decisions and routes to the mechanism') and 'when' ('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') with concrete trigger phrases.

5 / 5

Trigger Term Quality

Includes natural user phrases such as 'convert/ingest/preprocess robot data into an .rrd,' 'build a Rerun recording,' and 'modeling or converting a dataset,' plus the .rrd extension; good coverage but a few natural synonyms like 'visualize' or 'log to Rerun' are only implicit.

4 / 5

Distinctiveness Conflict Risk

Occupies a clear niche ('Rerun data model,' '.rrd,' multimodal robot data) and explicitly names the sibling skills it routes to (rerun-mcap, rerun-urdf, rerun-parquet, rerun-mp4, rerun-lerobot, rerun-chunk-processing), which sharpens boundaries and minimizes conflict with those closely related skills.

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

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.