matchms
Spectral similarity and compound identification for metabolomics. Use for comparing mass spectra, computing similarity scores (cosine, modified cosine), and identifying unknown compounds from spectral libraries. Best for metabolite identification, spectral matching, library searching. For full LC-MS/MS proteomics pipelines use pyopenms.
88
86%
Does it follow best practices?
Impact
88%
4.63xAverage score across 3 eval scenarios
Advisory
Suggest reviewing before use
Quality
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 an excellent skill description that clearly defines specific capabilities (spectral comparison, similarity scoring, compound identification), includes rich domain-specific trigger terms, and explicitly addresses both what and when. The inclusion of a boundary condition distinguishing it from the pyopenms skill is a particularly strong feature that reduces conflict risk.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: comparing mass spectra, computing similarity scores (cosine, modified cosine), identifying unknown compounds from spectral libraries. These are well-defined, concrete capabilities. | 3 / 3 |
Completeness | Clearly answers both what (comparing mass spectra, computing similarity scores, identifying compounds) and when ('Use for comparing mass spectra...', 'Best for metabolite identification, spectral matching, library searching'). Also includes a helpful negative boundary distinguishing it from pyopenms for proteomics pipelines. | 3 / 3 |
Trigger Term Quality | Includes strong natural keywords users would say: 'mass spectra', 'similarity scores', 'cosine', 'modified cosine', 'spectral libraries', 'metabolite identification', 'spectral matching', 'library searching', 'metabolomics'. Good coverage of domain-specific terms users would naturally use. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with a clear niche in spectral similarity for metabolomics. The explicit boundary statement ('For full LC-MS/MS proteomics pipelines use pyopenms') actively reduces conflict risk with related skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
72%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a well-structured skill with strong actionability through executable code examples and excellent progressive disclosure via clearly referenced sub-documents. The main weaknesses are moderate verbosity (explanatory bullet lists and descriptions that could be trimmed) and the lack of an inline end-to-end workflow with validation checkpoints, which is instead fully deferred to a reference file.
Suggestions
Add a concise inline end-to-end workflow (load → filter → match → inspect results) with at least one validation checkpoint (e.g., checking spectrum count after filtering, verifying score thresholds) rather than deferring all workflows to references.
Trim the bullet-point lists of supported formats, filter categories, and similarity functions—these are better suited for the reference files and add token overhead to the main skill.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is mostly efficient with good code examples, but includes some unnecessary explanations (e.g., 'Matchms is an open-source Python library for mass spectrometry data processing and analysis' in the overview, listing out all supported formats when Claude could look these up, and some verbose descriptions of filter categories). The bullet lists of available similarity functions and filter categories add bulk that could be trimmed or moved to references. | 2 / 3 |
Actionability | All code examples are fully executable and copy-paste ready with proper imports. The examples cover creating spectra, importing/exporting, filtering, calculating similarities, and building pipelines with concrete, runnable Python code. | 3 / 3 |
Workflow Clarity | Individual operations are clear, but the common end-to-end workflow (load → filter → compare → identify) is deferred to references/workflows.md rather than shown inline. There are no validation checkpoints or error handling steps shown for multi-step processes like spectral matching pipelines, and no feedback loops for quality control. | 2 / 3 |
Progressive Disclosure | Excellent structure with a clear overview in the main file and well-signaled one-level-deep references to filtering.md, similarity.md, importing_exporting.md, and workflows.md. Content is appropriately split between the overview and detailed reference files. | 3 / 3 |
Total | 10 / 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
b58ad7e
- Reviewed
Table of Contents
Is this your skill?
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.