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.
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Excellent skill for metabolomics mass spectrometry analysis. The description clearly identifies use cases (spectral matching, similarity scoring, compound identification) and scope boundaries (vs pyopenms for proteomics). SKILL.md provides comprehensive coverage with 6 well-organized capability sections, complete code examples, and clean separation of detailed references into dedicated files. Task knowledge is outstanding—includes practical code for importing multiple formats (mgf, mzML, msp), applying 40+ filters, computing various similarity metrics (cosine, modified cosine, fingerprint-based), and building processing pipelines. Structure is exemplary: concise main document with logical sections and detailed references properly externalized. Novelty is strong—spectral similarity calculations, metadata harmonization, and multi-step processing pipelines would require significant tokens and domain expertise for a CLI agent to accomplish from scratch. This skill meaningfully reduces complexity for metabolomics researchers. Minor limitation: while highly specialized, the domain scope is narrower than some other skills, slightly reducing universal applicability.
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