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.

Quality

75%

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tessl review fix ./backend/cli/skills/chemistry/pyopenms/SKILL.md

Quality

Content

72%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is well-structured with strong progressive disclosure and broadly executable examples. The main gaps are a broken install command and the absence of validation/verification checkpoints in batch MS-processing workflows.

Suggestions

Fix the install command: "uv pip install pyopenms" (remove the duplicated uv).

Add validation/verification steps to batch workflows, e.g. after FDR filtering check the reported FDR/Q-value threshold, and after feature detection assert non-empty FeatureMap before downstream linking.

Trim the opening Overview sentence to the skill's purpose rather than restating what the OpenMS library is.

Dimension	Reasoning	Score
Conciseness	Mostly efficient with short sectioned code examples and little padding; the only mild over-explanation is the opening "PyOpenMS provides Python bindings to the OpenMS library... enabling analysis of..." sentence, which states background Claude already knows.	4 / 5
Actionability	Provides many concrete, copy-paste-ready code blocks (mzML load, GaussFilter, FeatureFinder, IdXML/FDR, pandas export) covering common cases, but the install line "uv uv pip install pyopenms" duplicates the uv command and is not directly runnable as written.	4 / 5
Workflow Clarity	Sequenced steps exist (e.g. the numbered metabolomics workflow), but batch/data-processing operations like feature detection, FDR filtering, and raw-data processing have no validation or verification checkpoints, so the destructive/batch cap of 3 applies.	3 / 5
Progressive Disclosure	Clear overview with six well-signaled, one-level-deep references that all resolve to real files in references/, plus a consolidated References list, making navigation easy and appropriately split.	5 / 5
	Total	16 / 20 Passed

Description

78%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is specific, well-triggered, and clearly niched with an explicit hand-off to a sibling skill. Main weaknesses are phrasing roughness (a dropped comma) and "when" guidance stated as workflow names rather than concrete user-voice triggers.

Suggestions

Add a comma so the action list parses cleanly: "Use for proteomics workflows: feature detection, peptide identification, protein quantification, and complex LC-MS/MS pipelines."

Add user-voice trigger phrasing such as "Use when the user mentions mass spectrometry, proteomics, LC-MS/MS, or .mzML files."

Mention a key file extension (e.g. mzML/mzXML) among the trigger terms so natural file-based requests match.

Dimension	Reasoning	Score
Specificity	Names the domain and lists several concrete actions ("feature detection, peptide identification, protein quantification, and complex LC-MS/MS pipelines") plus "Supports extensive file formats and algorithms", though the latter is somewhat generic and a missing comma slightly muddies the action list.	4 / 5
Completeness	Explicitly answers both "what" (a platform with listed capabilities) and "when" ("Use for proteomics workflows..."), but the "when" is phrased as workflow names rather than the more explicit user-voice trigger pattern of the 5 anchor.	4 / 5
Trigger Term Quality	Good natural-term coverage ("mass spectrometry", "proteomics", "LC-MS/MS", "metabolite ID", "protein quantification", "feature detection") but misses common synonyms and file extensions like mzML that users would mention.	4 / 5
Distinctiveness Conflict Risk	Occupies a clear mass-spectrometry/proteomics niche with distinct triggers and even adds a boundary clause ("For simple spectral comparison and metabolite ID use matchms"), giving minimal conflict risk.	5 / 5
	Total	17 / 20 Passed

Validation

87%

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
metadata_version	'metadata.version' is missing	Warning
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	14 / 16 Passed

Repository: synthetic-sciences/openscience
Path: backend/cli/skills/chemistry/pyopenms/SKILL.md
Commit: 3a6c3a9

Reviewed: 1 day ago

Table of Contents

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