Agent skill · data analytics · k-dense-ai
matchms
Process, clean, compare, and search tandem mass spectra with matchms. Use for MS/MS file I/O, metadata harmonization, peak filtering, spectral similarity, library matching, score matrices, and molecular-similarity networks. Use pyopenms instead for LC-MS feature detection or proteomics pipelines.
Why this skill is useful
Provides domain-specific knowledge for processing and comparing tandem mass spectra that the AI wouldn't reliably generate on its own.
What it needs
Requires matchms, python, uv installed locally. About 5k tokens when loaded. Last updated 2026-08-03. 32,849 stars on the source repository.
What this skill does
Matchms Purpose and Scope Matchms is a Python package for importing, cleaning, processing, and comparing tandem mass spectra. This skill targets matchms 0.33.1, released 2026-06-08, and corrects several breaking API changes that older tutorials do not reflect. Use matchms for: MS/MS library search and query-versus-reference scoring Metadata harmonization, adduct/precursor handling, and peak filtering Cosine, modified-cosine, neutral-loss, approximate, and entropy scoring Structured score matrices, top-hit extraction, and spectral networks MGF, MSP, mzML, mzXML, JSON, mzSpecLib, and metabolomics-USI workflows Do not use matchms as a replacement for: LC-MS feature detection, chromatographic alignment, peptide identification, or protein quantification — use pyopenms Vendor raw-file conversion — convert to mzML/mzXML first A validated compound-identification protocol — similarity is evidence, not proof of identity Install the Verified Release Create or activate an environment, then install the release used by this skill: Verify the runtime: Matchms 0.33.1 supports Python 3.10-3.14 and installs RDKit as a regular dependency. The old matchms[chemistry] extra is not part of the current package metadata. Operating Workflow 1. Inspect the inputs. Record format, spectrum count, MS level, precursor coverage, ion mode, peak counts, and identifier fields. 2. Load with metadata harmonization enabled unless preserving source keys is a deliberate requirement. 3. Apply the same peak-processing steps to query and reference spectra. Keep metadata enrichment separate when reference annotations are richer. 4. Drop invalid spectra explicitly. Many require filters return None. 5. Choose the score from the scientific question, not from convenience. Modified and neutral-loss scores require valid precursormz. 6. Estimate len(references) len(queries) before scoring. A sparse result container does not automatically avoid computing every requested pair. 7. Report score settings and evidence. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills k-dense-ai/matchms