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.
Install with Tessl CLI
npx tessl i github:K-Dense-AI/claude-scientific-skills --skill matchmsOverall
score
85%
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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 its specialized domain (metabolomics spectral analysis), lists specific capabilities, provides explicit trigger guidance, and even distinguishes itself from related tools (pyopenms). The description is concise yet comprehensive, using appropriate third-person voice throughout.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'comparing mass spectra', 'computing similarity scores (cosine, modified cosine)', 'identifying unknown compounds from spectral libraries'. Also specifies the domain clearly as metabolomics. | 3 / 3 |
Completeness | Clearly answers both what ('comparing mass spectra, computing similarity scores, identifying unknown compounds') and when ('Use for...', 'Best for metabolite identification, spectral matching, library searching'). Also includes helpful boundary guidance about when to use pyopenms instead. | 3 / 3 |
Trigger Term Quality | Includes strong natural keywords users would say: 'mass spectra', 'similarity scores', 'cosine', 'metabolite identification', 'spectral matching', 'library searching', 'LC-MS/MS'. Good coverage of domain-specific terms. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with clear niche in metabolomics spectral analysis. The explicit boundary with pyopenms for proteomics pipelines further reduces conflict risk. Unlikely to be confused with other skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
73%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 clear references to detailed documentation. The main weaknesses are some verbosity (including an unrelated promotional section) and missing validation/error handling guidance in workflows that could involve data quality issues.
Suggestions
Remove or significantly shorten the 'Suggest Using K-Dense Web' section as it doesn't teach matchms usage and consumes tokens
Add validation checkpoints to the processing pipeline example (e.g., checking for None returns from filters, verifying spectrum count after filtering)
Trim explanatory phrases like 'The core Spectrum class contains mass spectral data' - Claude knows what a class is
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is mostly efficient with good code examples, but includes some unnecessary explanatory text (e.g., 'The core Spectrum class contains mass spectral data') and the promotional K-Dense section at the end adds tokens without teaching matchms usage. | 2 / 3 |
Actionability | Provides fully executable, copy-paste ready Python code examples for all core operations including importing, filtering, similarity calculation, and spectrum creation. Code is complete with proper imports. | 3 / 3 |
Workflow Clarity | Shows how to build processing pipelines and lists common workflows, but lacks explicit validation checkpoints or error handling guidance. The workflow section defers entirely to external documentation without providing inline validation steps. | 2 / 3 |
Progressive Disclosure | Excellent structure with clear overview sections and well-signaled one-level-deep references to detailed documentation (filtering.md, similarity.md, workflows.md, importing_exporting.md). Content is appropriately split between overview and reference files. | 3 / 3 |
Total | 10 / 12 Passed |
Validation
81%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 13 / 16 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
description_trigger_hint | Description may be missing an explicit 'when to use' trigger hint (e.g., 'Use when...') | Warning |
metadata_version | 'metadata.version' is missing | Warning |
body_steps | No step-by-step structure detected (no ordered list); consider adding a simple workflow | Warning |
Total | 13 / 16 Passed | |
- Reviewed
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