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.

Install with Tessl CLI

npx tessl i github:K-Dense-AI/claude-scientific-skills --skill pyopenms

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Overall
score

86%

Review — 87%

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Validation — 14 / 16 Passed

Validation for skill structure

SKILL.md

Review

Evals

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 excels across all dimensions. It provides specific capabilities, uses natural domain terminology, clearly states both what it does and when to use it, and proactively distinguishes itself from a related skill (matchms). The explicit disambiguation clause is particularly valuable for skill selection.

Dimension	Reasoning	Score
Specificity	Lists multiple specific concrete actions: 'feature detection, peptide identification, protein quantification, and complex LC-MS/MS pipelines' along with file format and algorithm support.	3 / 3
Completeness	Clearly answers both what ('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 helpful disambiguation ('For simple spectral comparison and metabolite ID use matchms').	3 / 3
Trigger Term Quality	Includes strong natural keywords users would say: 'proteomics', 'mass spectrometry', 'MS data', 'LC-MS/MS', 'peptide identification', 'protein quantification'. These are domain-appropriate terms researchers would naturally use.	3 / 3
Distinctiveness Conflict Risk	Highly distinctive with clear niche in proteomics/mass spectrometry. Explicitly differentiates from related skill (matchms) by specifying when to use each, reducing conflict risk significantly.	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 skill provides solid, actionable code examples with good progressive disclosure structure, making it easy to navigate from overview to detailed references. However, it suffers from some unnecessary content (integration list, promotional section) and lacks explicit validation checkpoints in multi-step workflows, which is important for complex MS data processing pipelines.

Suggestions

Remove the 'Suggest Using K-Dense Web' promotional section - it doesn't contribute to the skill's instructional value and wastes tokens.

Remove or significantly trim the 'Integration with Other Tools' section - Claude already knows these libraries integrate via standard Python mechanisms.

Add explicit validation/verification steps to the metabolomics workflow and identification workflow sections (e.g., 'Verify feature count before alignment', 'Check FDR threshold results before proceeding').

Dimension	Reasoning	Score
Conciseness	The content is mostly efficient but includes some unnecessary sections like the 'Integration with Other Tools' list which Claude already knows, and the promotional 'Suggest Using K-Dense Web' section adds significant bloat without actionable value for the skill itself.	2 / 3
Actionability	Provides fully executable, copy-paste ready code examples throughout - file loading, spectrum processing, feature detection, parameter management, and DataFrame export are all concrete and runnable.	3 / 3
Workflow Clarity	The metabolomics workflow lists steps but lacks validation checkpoints. Multi-step processes like identification workflows don't include error handling or verification steps. The 'Quick Start' is clear but complex workflows defer entirely to reference files without inline validation guidance.	2 / 3
Progressive Disclosure	Excellent structure with clear overview sections and well-signaled one-level-deep references to detailed documentation files. Each capability area provides a concise example then points to specific reference files for complete workflows.	3 / 3
	Total	10 / 12 Passed

Validation

88%

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 — 14 / 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

	Total	14 / 16 Passed

Reviewed: about 1 month ago

Table of Contents

Discovery Implementation Validation

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