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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.

63

Quality

79%

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SecuritybySnyk

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

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.

Well-structured content with lean, concrete code examples and exemplary progressive disclosure into real bundle files. Weaknesses are the broken install command, an example with undefined variables, and workflows that lack validation checkpoints and error-recovery guidance.

Suggestions

Fix the installation command (currently 'uv uv pip install pyopenms', a duplicated and malformed command) so it is copy-paste executable.

Make the feature detection snippet self-contained by defining 'features' and 'params' before ff.run() is called.

Add validation checkpoints and error-recovery steps (e.g., verifying spectra loaded, checking FDR thresholds, re-checking after parameter changes) to the metabolomics and identification workflows.

DimensionReasoningScore

Conciseness

The body is code-forward and does not explain mass spectrometry basics Claude already knows, but the 'Integration with Other Tools' bullet list and the References section (which repeats the per-section 'See references/...' pointers) are mildly trimmable. Fits anchor 4 (efficient, minor over-explanation) rather than 5 (every token earns its place).

4 / 5

Actionability

Mostly concrete, executable code covering loading, filtering, identification, and export. Two gaps: the install command 'uv uv pip install pyopenms' is a broken duplicated command, and the feature detection snippet uses undefined 'features' and 'params' variables. Not 5 (not copy-paste ready throughout); above 3 (code is real, not pseudocode).

4 / 5

Workflow Clarity

The metabolomics workflow lists five sequenced steps but they are prose labels with no commands, and the body contains no validation checkpoints or error-recovery loops anywhere. Matches anchor 3 (sequence present but checkpoints missing); not 4, which requires most checkpoints present.

3 / 5

Progressive Disclosure

Clear overview structure with each domain section explicitly signaling a one-level-deep reference ('See references/file_io.md' etc.), all six referenced files present in the bundle, plus a summary References index. The slight redundancy of the double pointer listing does not impair navigation, so it holds at anchor 5 rather than dropping to 4.

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.

A strong description with specific capabilities, good natural keywords, explicit use guidance, and an excellent explicit disambiguation against matchms. Main weaknesses are the garbled 'Use for proteomics workflows feature detection' phrasing, noun-phrase rather than verb-led action framing, and missing file-extension trigger terms.

DimensionReasoningScore

Specificity

Lists several specific capabilities ('feature detection, peptide identification, protein quantification, and complex LC-MS/MS pipelines') though as noun phrases rather than verb-led actions, with mild fluff in 'Complete mass spectrometry analysis platform' and 'extensive file formats and algorithms'. Fits anchor 4 (several specific actions, minor gaps) better than 3 (only 1-2 concrete actions) and falls short of 5's fully concrete action list.

4 / 5

Completeness

Both 'what' (analysis platform with enumerated capabilities) and 'when' ('Use for proteomics workflows...', 'Best for proteomics, comprehensive MS data processing') are present. Not 5 because the trigger sentence is grammatically garbled ('Use for proteomics workflows feature detection') and lacks the concrete 'use when the user mentions X' phrasing of the top anchor; not 3 because the 'when' is explicit rather than implied.

4 / 5

Trigger Term Quality

Good natural keyword coverage: 'proteomics', 'peptide identification', 'protein quantification', 'LC-MS/MS', 'mass spectrometry', 'MS data processing'. Missing common synonyms ('mass spec') and file extensions (mzML, mzXML, FASTA) that anchor 5 requires.

4 / 5

Distinctiveness Conflict Risk

Clear niche (LC-MS/MS proteomics) with an explicit boundary clause ('For simple spectral comparison and metabolite ID use matchms') that actively prevents mis-triggering against a sibling skill. Matches anchor 5: distinct triggers, minimal conflict risk.

5 / 5

Total

17

/

20

Passed

Validation

87%

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

CriteriaDescriptionResult

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

Reviewed

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