Content
65%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.
Well-structured overview with excellent progressive disclosure to real reference files, but it loses points on a broken install command, an incomplete code example, and workflows that lack validation checkpoints for batch MS processing.
Suggestions
Fix the install command: change 'uv uv pip install pyopenms' to a single 'uv pip install pyopenms' so it is copy-paste executable.
Make the FeatureFinder example complete by defining 'features' and 'params' (e.g., ms.FeatureMap() and a Param object) before the run() call.
Add validation checkpoints to the metabolomics and identification workflows (e.g., verify loaded spectra count, confirm FDR threshold applied) to support batch/destructive operations.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly efficient and code-focused, but the Overview section repeats the frontmatter description and a few section intros restate the obvious, so it is not fully lean. | 4 / 5 |
Actionability | Most code is concrete, but the install command 'uv uv pip install pyopenms' has a duplicated 'uv' and the FeatureFinder example references undefined 'features' and 'params' variables — missing key details rather than minor gaps. | 3 / 5 |
Workflow Clarity | The metabolomics workflow gives a clear 5-step sequence but includes no validation or verification checkpoints; per the batch-operation cap, this limits the score to 3. | 3 / 5 |
Progressive Disclosure | SKILL.md is a concise overview with well-signaled, one-level-deep references to six existing reference files (file_io, signal_processing, feature_detection, identification, metabolomics, data_structures), all verified present. | 5 / 5 |
Total | 15 / 20 Passed |