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.

1.37x

Quality

78%

Does it follow best practices?

Impact

77%

1.37x

Average score across 3 eval scenarios

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./scientific-skills/pyopenms/SKILL.md

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 a strong skill description that clearly defines its domain (mass spectrometry / proteomics), lists specific capabilities, provides explicit usage triggers, and even includes disambiguation guidance against a related skill (matchms). The only minor weakness is that the sentence structure is slightly fragmented, but the content is comprehensive and well-targeted.

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 platform for 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.

This skill provides a well-structured overview of PyOpenMS with good progressive disclosure to reference files, but suffers from incomplete/non-executable code examples in key areas (feature detection, identification) and lacks validation checkpoints in multi-step workflows. Some sections include unnecessary content that Claude already knows (library integrations, basic concept explanations), reducing token efficiency.

Suggestions

Fix the FeatureFinder code example to be fully executable with all variables defined, or provide a minimal but complete working snippet

Add validation checkpoints to the metabolomics workflow (e.g., verify peak count after centroiding, check feature count after detection) and make steps concrete with actual commands

Remove the 'Integration with Other Tools' section—Claude already knows about NumPy/Pandas/Matplotlib integration—and trim the overview paragraph to avoid restating the skill description

Dimension	Reasoning	Score
Conciseness	Generally efficient but includes some unnecessary content like the 'Integration with Other Tools' section listing obvious integrations (NumPy, Pandas, Matplotlib) that Claude already knows about, and the overview paragraph restates what the description already covers. The data structures section is borderline—useful as a quick reference but could be leaner.	2 / 3
Actionability	Provides some executable code examples (file loading, Gaussian filter, export to pandas), but several key examples are incomplete or non-functional. The FeatureFinder example uses undefined variables (features, params) making it not copy-paste ready. The identification workflow only shows loading results, not running a search. Many sections defer to reference files without providing enough standalone guidance.	2 / 3
Workflow Clarity	The metabolomics workflow lists steps but lacks concrete commands and validation checkpoints. The 'Quick Start' is clear but simple. Multi-step proteomics workflows (feature detection → identification → quantification) are not sequenced with validation steps. For a tool involving complex pipelines with potential data integrity issues, the lack of explicit validation/error-checking steps is a gap.	2 / 3
Progressive Disclosure	Excellent progressive disclosure structure. The main file serves as a clear overview with concise examples, and consistently points to one-level-deep reference files for detailed content. References are well-signaled throughout the document and collected in a final section. Navigation is straightforward.	3 / 3
	Total	9 / 12 Passed

Validation

90%

Warnings & errors only

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: 2 days ago

Table of Contents

Discovery Implementation Validation

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