rerun-urdf

Drive the Rerun URDF API (rerun.urdf.UrdfTree) to ingest a URDF as a Transform3D layer on a robot recording. Read when logging a robot model, running forward kinematics from joint states, composing a fixed chain for sensor extrinsics, or when the transform tree will not connect from the data alone. Builds on rerun-chunk-processing (stream/lens mechanics) and rerun-data-model (entity paths, timeline, base-vs-layer).

Quality

83%

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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 dense, actionable body that defers generic stream/lens mechanics to sibling skills and concentrates on the URDF-specific judgment calls, with executable code and a validation-backed forest-resolution checklist. The main improvements are tightening the longer prose sections and considering a split reference file for the full pipeline example.

Suggestions

Tighten the 'How transforms compose' and 'Resolving the transform forest' prose — the six steps could be condensed into a shorter checklist with the graph-check validation called out as the key checkpoint.

Consider moving the full 'Minimal shape' pipeline and the calibration Chunk.from_columns edge into a references/ file, keeping SKILL.md as an overview that links to it, to improve progressive disclosure now that the file is ~250 lines.

Add an inline validation checkpoint in the 'Minimal shape' pipeline itself (e.g., 'confirm the joint-state stream yields rows and the forest has one root before write_rrd') so the workflow's validation is visible end-to-end rather than only in earlier sections.

Dimension	Reasoning	Score
Conciseness	Dense and assumes competence (no padding about what a URDF or quaternion is), but the multi-step 'Resolving the transform forest' and 'How transforms compose' prose could be tightened without losing the judgment-call guidance.	4 / 5
Actionability	Concrete, mostly copy-paste-ready code throughout (UrdfTree.from_file_path, compute_joint_transform_batches, a full two-lens 'Minimal shape' pipeline, a calibration Chunk.from_columns edge), with the few placeholders explicitly flagged as user-supplied data wiring.	4 / 5
Workflow Clarity	Clear two-step pipeline (stream static model -> solve FK -> merge -> write) plus a 6-step forest-resolution checklist with a pre-write graph check and a 'fail loudly or log identity + warning' feedback loop; validation is present but distributed across sections rather than inline in the final pipeline.	4 / 5
Progressive Disclosure	Well-organized into clear section headers with one-level-deep, clearly signaled references (two example URLs and the sibling skills rerun-data-model / rerun-chunk-processing), but all ~250 lines live in one file with no split reference bundle, leaving minor organization gaps.	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 precise, third-person description that names concrete API-driven actions and gives explicit 'Read when...' trigger guidance covering the skill's main scenarios. It is well-distinguished from sibling Rerun skills; the only gap is the absence of a '.urdf' file-extension trigger term.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — 'ingest a URDF as a Transform3D layer', 'logging a robot model', 'running forward kinematics from joint states', 'composing a fixed chain for sensor extrinsics', resolving an unconnected transform tree — giving comprehensive coverage of the skill's use cases.	5 / 5
Completeness	Explicitly answers both 'what' (drive rerun.urdf.UrdfTree to ingest a URDF as a Transform3D layer) and 'when' via a concrete 'Read when...' clause enumerating four trigger scenarios.	5 / 5
Trigger Term Quality	Strong natural terms ('URDF', 'robot model', 'forward kinematics', 'joint states', 'transform tree') but misses the '.urdf' file extension and a few synonyms, so it sits just below the comprehensive-with-extensions anchor.	4 / 5
Distinctiveness Conflict Risk	Occupies a clear niche (Rerun URDF API, Transform3D layer, forward kinematics) and explicitly distinguishes itself from sibling skills it builds on ('rerun-chunk-processing', 'rerun-data-model'), minimizing conflict risk.	5 / 5
	Total	19 / 20 Passed

Validation

87%

Warnings & errors only

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

Criteria	Description	Result
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-urdf/SKILL.md
Commit: f30766b

Reviewed: ~6 hours ago

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