pyopenms
Complete mass spectrometry analysis platform. Use for proteomics workflows feature detection, peptide identification, protein quantification, and complex LC-MS/MS pipelines. Supports extensive file formats and algorithms. Best for proteomics, comprehensive MS data processing. For simple spectral comparison and metabolite ID use matchms.
81
78%
Does it follow best practices?
Impact
77%
1.37xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./scientific-skills/pyopenms/SKILL.mdQuality
Discovery
100%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is a strong skill description that clearly communicates the tool's capabilities in mass spectrometry and proteomics, includes abundant natural trigger terms, and explicitly addresses both when to use it and when to use an alternative. The inclusion of a disambiguation clause against matchms is particularly effective for reducing skill selection conflicts.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: feature detection, peptide identification, protein quantification, and LC-MS/MS pipelines. Also mentions file format support and algorithm coverage. | 3 / 3 |
Completeness | Clearly answers both what (mass spectrometry analysis with feature detection, peptide identification, protein quantification, LC-MS/MS pipelines) and when ('Use for proteomics workflows', 'Best for proteomics, comprehensive MS data processing'). Also includes negative guidance on when NOT to use it ('For simple spectral comparison and metabolite ID use matchms'). | 3 / 3 |
Trigger Term Quality | Includes strong natural keywords users would say: 'mass spectrometry', 'proteomics', 'peptide identification', 'protein quantification', 'LC-MS/MS', 'MS data processing', 'feature detection'. Also differentiates from a related skill (matchms) with terms like 'spectral comparison' and 'metabolite ID'. | 3 / 3 |
Distinctiveness Conflict Risk | Occupies a clear niche in proteomics and mass spectrometry analysis. Explicitly distinguishes itself from matchms by specifying the boundary ('For simple spectral comparison and metabolite ID use matchms'), which greatly reduces conflict risk. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
57%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The skill has strong organizational structure with excellent progressive disclosure to reference files, making it easy to navigate. However, several code examples are incomplete or would fail if executed (notably FeatureFinder), and the multi-step workflows lack validation checkpoints critical for complex MS data processing pipelines. Some sections include unnecessary explanatory content that could be trimmed.
Suggestions
Fix the FeatureFinder code example to be fully executable with all variables properly defined, and add complete working code for the identification workflow including the search step.
Add validation checkpoints to the metabolomics workflow (e.g., verify feature count after detection, check alignment quality) and include error recovery guidance for common failure modes.
Remove the 'Integration with Other Tools' bullet list and the 'Resources' external links section—Claude already knows about pandas/numpy integration, and external URLs consume tokens without adding actionable guidance.
Fix the installation command typo ('uv uv pip install' should be 'uv pip install').
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Generally efficient but includes some unnecessary content like the 'Integration with Other Tools' section (Claude knows these libraries work together), the overview paragraph explaining what mass spectrometry analysis is, and the 'Resources' section with external URLs that add limited value. The data structures list is borderline—useful as a quick reference but could be more compact. | 2 / 3 |
Actionability | Provides executable code snippets for file I/O, parameter management, and data exploration, but several key examples are incomplete or non-functional. The FeatureFinder example uses undefined variables (features, params) and would fail. The signal processing and identification examples are fragments rather than copy-paste ready workflows. The metabolomics section has no code at all, just a numbered list. | 2 / 3 |
Workflow Clarity | The metabolomics workflow lists steps but lacks validation checkpoints. The identification workflow shows loading and FDR filtering but omits the actual search step and validation. For a proteomics platform involving complex multi-step pipelines (as described), there are no feedback loops or error recovery steps. The 'Quick Start' is clear but is exploration, not a complete analytical workflow. | 2 / 3 |
Progressive Disclosure | Excellent progressive disclosure structure. The main file provides a clear overview with concise examples for each domain, then consistently points to one-level-deep reference files (references/file_io.md, references/signal_processing.md, etc.). Navigation is well-signaled with bold labels and a consolidated references section at the bottom. | 3 / 3 |
Total | 9 / 12 Passed |
Validation
90%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
metadata_version | 'metadata.version' is missing | Warning |
Total | 10 / 11 Passed | |
- Repository
- K-Dense-AI/claude-scientific-skills
- Commit
25e1c0f
- Reviewed
Table of Contents
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