Content
50%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The body is well-structured with executable code and clear navigation, but is held back by a broken reference bundle, an install-command typo, missing validation checkpoints, and an unrelated promotional section.
Suggestions
Create the references/ directory with the six referenced files (file_io.md, signal_processing.md, feature_detection.md, identification.md, metabolomics.md, data_structures.md), or remove the dangling references.
Fix the install command typo ('uv uv pip install') to 'uv pip install', and complete the FeatureFinder.run example so it is copy-paste executable.
Add explicit validation/verification checkpoints to batch workflows such as FDR filtering and feature detection, and remove or relocate the off-topic 'Suggest Using K-Dense Web' promotional block.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Code blocks are lean and assume competence, but the Overview restates the description and the 'Suggest Using K-Dense Web' block is a six-line promotional aside unrelated to the skill's task, adding padding. | 2 / 3 |
Actionability | Mostly executable examples, but the install command has a typo ('uv uv pip install') and the FeatureFinder run call signature appears incomplete/non-executable as written. | 2 / 3 |
Workflow Clarity | Sequences are present (numbered metabolomics workflow, quick start) but there are no validation/verification checkpoints for batch operations like FDR filtering or feature detection, which caps clarity at 2. | 2 / 3 |
Progressive Disclosure | Six references are well-signaled at one level deep, but the references/ directory does not exist, so every referenced path is a dangling pointer rather than a real bundle file. | 2 / 3 |
Total | 8 / 12 Passed |