Content
82%Weight 40%Scale 1-5Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
A tight, action-oriented body with executable code, a useful schema-to-archetype table, and strong diagnostic/validation guidance in the gotchas. Structure and references are clean; the only room for improvement is trimming a couple of dense explanatory passages.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly lean and assumes Claude's competence (no preamble explaining MCAP/Rerun/protobuf), with a dense information-rich table and 'see help(McapReader) for the full option set' deferral; a few explanatory passages like the foxglove-decoder paragraph and gotcha 6 could be trimmed slightly. | 4 / 5 |
Actionability | Provides copy-paste-ready executable code — `McapReader(mcap_path)`, `reader.stream()`, `.filter(content="/sensors/**")`, `Selector(".joint_positions")`, `Chunk.format()` — plus a concrete schema-to-archetype table and specific diagnostic commands covering the common cases. | 5 / 5 |
Workflow Clarity | Not a linear numbered pipeline, but the gotchas supply explicit validation checkpoints and feedback loops — 'Check Chunk.format() on a few chunks', 'retry with decoders=None to rule out decoder selection', and 'confirm the defect exists in your data — applied blindly it corrupts correct calibration' — with only minor sequencing gaps. | 4 / 5 |
Progressive Disclosure | Well-organized into clear sections (The API, What a topic becomes, low-level mcap, Gotchas, References) with one-level-deep pointers to sibling skills (rerun-chunk-processing, rerun-urdf, rerun-data-model) and an external example URL; no bundle files exist to split further, so content is appropriately self-contained. | 4 / 5 |
Total | 17 / 20 Passed |