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Python library for mass spectrometry data processing: import/export mzML/MGF/MSP, filter and harmonize metadata, compute spectral similarity (cosine & modified
MatchMS provides a comprehensive Python toolkit for processing, filtering, and comparing mass spectra. It imports and exports common formats (mzML, MGF, MSP, JSON), standardizes metadata, applies extensive peak and metadata filters, and computes spectral similarity using multiple algorithms (cosine, modified cosine, fingerprint-based). The library supports building reusable processing pipelines and producing outputs suitable for library searching, clustering, and downstream analysis.
Use MatchMS when you need to preprocess raw MS/MS spectra, harmonize metadata across datasets, filter low-quality peaks, or perform large-scale spectral similarity searches and library matching for metabolomics or small-molecule analysis. It is appropriate for reproducible workflows, batch processing of spectral libraries, and integrating similarity scoring into pipelines.
This is a Python library intended for agents with Python execution or code-assistant capabilities (e.g., Copilot/Codex/Claude Code). Agents that can run Python tooling and manage scientific packages will best leverage MatchMS.
Documentation-only skill for the matchms Python library (mass spectrometry data processing). No scripts bundled — purely reference content with code examples for importing/exporting MS data, filtering, spectral similarity, and pipeline building. SKILL.md is well-structured with clear sections and pointers to a references/ directory, but entirely static with no executable logic or output contracts.
Clean, benign skill — just documentation for a specialized scientific Python library. No security concerns whatsoever. Low usefulness score reflects the extremely narrow audience (mass spectrometry / metabolomics researchers).
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