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rerun-mcap

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).

Quality

91%

Does it follow best practices?

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SecuritybySnyk

Passed

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SKILL.md
Quality
Evals
Security

Quality

Content

90%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 exemplary lean reference body: executable code, verified decoder behavior, and high-value gotchas with explicit validation guards. The main improvement areas are sequencing the ingest workflow as ordered steps and turning the References section into well-signaled, navigable pointers.

Suggestions

Add a short numbered quick-start sequence (open reader -> stream -> verify with Chunk.format() -> apply lenses) so the ingest workflow is ordered rather than organized only by topic.

Replace the raw GitHub URL with a labeled reference (e.g., "**End-to-end pipeline example**: robot_data_preprocessing") and consider splitting the 6-item gotchas list into a references file.

DimensionReasoningScore

Conciseness

The body assumes Claude's competence throughout — it never explains what MCAP, Rerun, protobuf, or chunk streams are — and every line carries non-obvious information (decoder interactions, unanchored RE2 regexes, frame_id derivation). Lean with no padding.

5 / 5

Actionability

Copy-paste-ready snippets cover the common cases: `McapReader(mcap_path)` / `reader.stream()`, `.filter(content="/sensors/**")`, `Selector(".joint_positions")`, and diagnostic commands like `reader.stream().to_chunks()` with `Chunk.format()`. The decoder guidance is even backed by a stated verification ("verified: decoders=['protobuf'] alone leaves... a raw :message").

5 / 5

Workflow Clarity

Validation checkpoints are explicit ("Confirm on your file... before deciding anything is missing"; "confirm the defect exists in _your_ data — applied blindly it corrupts") and gotchas include retry loops ("retry with decoders=None to rule out decoder selection"), so the destructive/batch cap does not apply. Not 5 because guidance is organized by topic (API, mapping, gotchas) rather than as a sequenced end-to-end ingest workflow with ordered steps.

4 / 5

Progressive Disclosure

Well-organized sections with effective offloading to sibling skills one level deep (stream mechanics -> rerun-chunk-processing, FK -> rerun-urdf, modeling -> rerun-data-model), and no bundle files exist so nothing is misfiled. Not 5 because the sole concrete reference is a raw external GitHub URL rather than a clearly signaled local reference file, and the dense 6-item gotchas list could live in a split-out reference.

4 / 5

Total

18

/

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 tight, third-person description that states concrete capabilities and an explicit 'Read when...' trigger list covering both normal use and the key failure mode (empty stream). Only minor gap: no '.mcap' extension or synonym coverage.

DimensionReasoningScore

Specificity

"Ingest MCAP files into Rerun chunk streams", "selecting topics or decoders", "decoding custom protobuf messages", and diagnosing "an MCAP-derived stream comes out empty" are multiple specific concrete actions with comprehensive coverage of the skill's domain. Not 4 because coverage is thorough rather than having minor gaps.

5 / 5

Completeness

Explicitly answers what ("Ingest MCAP files into Rerun chunk streams with rerun.chunk.McapReader") and when ("Read when converting an MCAP recording, selecting topics or decoders, decoding custom protobuf messages, or when an MCAP-derived stream comes out empty") with concrete trigger phrases.

5 / 5

Trigger Term Quality

Good natural keyword coverage ("MCAP recording", "topics", "decoders", "custom protobuf messages", "stream comes out empty") that a user working with MCAP data would plausibly say. Not 5 because the ".mcap" file extension and common synonyms are absent; not 3 because far more than 'some relevant keywords' are present.

4 / 5

Distinctiveness Conflict Risk

Clear MCAP-to-Rerun niche with distinct triggers, and it explicitly delimits sibling skills ("Builds on rerun-chunk-processing (stream mechanics) and rerun-data-model"), minimizing wrong-skill triggering.

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.

Validation — 14 / 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

Path

skills/rerun-mcap/SKILL.md

Reviewed

Table of Contents

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