matchms skill

by K-Dense-AI·K-Dense-AI/scientific-agent-skills·47k stars·MIT

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.

A100/100content scan

Is the matchms skill safe?

Clean: nothing in its files matched our rules. We read 8 files in the folder on 2026-09-28.

No findings.

Install the matchms skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description.

git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills.git /tmp/scientific-agent-skills
mkdir -p ~/.claude/skills
cp -r /tmp/scientific-agent-skills/skills/matchms ~/.claude/skills/matchms

available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

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:

protein quantification — use pyopenms

LC-MS feature detection, chromatographic alignment, peptide identification, or

proof of identity

Vendor raw-file conversion — convert to mzML/mzXML first
A validated compound-identification protocol — similarity is evidence, not

Install the Verified Release

Create or activate an environment, then install the release used by this skill:

uv pip install "matchms==0.33.1"

Verify the runtime:

uv run python -c "import matchms; print(matchms.__version__)"

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

coverage, ion mode, peak counts, and identifier fields.

Inspect the inputs. Record format, spectrum count, MS level, precursor

a deliberate requirement.

Load with metadata harmonization enabled unless preserving source keys is

Keep metadata enrichment separate when reference annotations are richer.

Apply the same peak-processing steps to query and reference spectra.

Modified and neutral-loss scores require valid precursor_mz.

Drop invalid spectra explicitly. Many require_* filters return None.
Choose the score from the scientific question, not from convenience.

container does not automatically avoid computing every requested pair.

Estimate len(references) len(queries) before scoring.** A sparse result

score name, number of matched peaks when available, and candidate metadata.

Report score settings and evidence. Include tolerance, preprocessing,

agreement, ion/adduct compatibility, and orthogonal evidence.

Validate top hits visually and chemically. Use mirror plots, precursor

Current API Guardrails

These points prevent the most common failures from pre-0.33 examples:

removed in 0.32.0.

Use ModifiedCosineGreedy or ModifiedCosineHungarian; ModifiedCosine was

spectrum.losses, spectrum.compute_losses(...), or NeutralLossesCosine directly.

Do not call add_losses(). It was removed in 0.27.0; use

process_spectra().

SpectrumProcessor is not callable. Use process_spectrum() or

fields such as CosineGreedyscore and CosineGreedymatches.

processspectra() returns (processedspectra, processing_report).
Scores.scores is a StackedSparseArray, often with separate structured

reference indices.

scoresbyquery() returns (referencespectrum, scorerecord) pairs, not

deprecated.

Prefer spectra in parameter names. The legacy spelling spectrums is

Never load pickle files from an untrusted source; unpickling can execute code.

See references/migration.md for a complete old-to-current mapping.

Quick Start: Clean and Search a Library

from matchms import SpectrumProcessor, calculate_scores
from matchms.filtering import (
    default_filters,
    normalize_intensities,
    require_minimum_number_of_peaks,
    select_by_relative_intensity,
)
from matchms.importing import load_spectra
from matchms.similarity import ModifiedCosineGreedy


def load_and_process(path):
    spectra = [default_filters(spectrum) for spectrum in load_spectra(path)]
    processor = SpectrumProcessor(
        [
            normalize_intensities,
            (select_by_relative_intensity, {"intensity_from": 0.01}),
            (require_minimum_number_of_peaks, {"n_required": 5}),
        ]
    )
    processed, _ = processor.process_spectra(
        spectra,
        progress_bar=False,
        create_report=False,
    )
    return processed


references = load_and_process("library.msp")
queries = load_and_process("queries.mgf")

metric = ModifiedCosineGreedy(tolerance=0.02)
scores = calculate_scores(
    references=references,
    queries=queries,
    similarity_function=metric,
)

score_name = "ModifiedCosineGreedy_score"
matches_name = "ModifiedCosineGreedy_matches"
for query in queries:
    ranked = scores.scores_by_query(query, name=score_

SpectrumProcessor automatically orders built-in filters according to matchms's filter order. The aggregate defaultfilters callable is not in that registry, so run it first as above or expand its nine component filters. Inspect processor.processingsteps and preserve it with results.

Pair Scoring

Similarity classes expose pair() for one reference/query pair. Cosine-family results are structured NumPy scalars:

from matchms.similarity import CosineGreedy

result = CosineGreedy(tolerance=0.02).pair(reference, query)
similarity = float(result["score"])
matched_peaks = int(result["matches"])

Use calculate_scores() for matrix-oriented methods such as FlashSimilarity; its single-pair path is supported but intentionally not the optimized path.

Choose a Similarity Method

analog search.

CosineGreedy — standard peak cosine with greedy peak assignment.
CosineHungarian — exact assignment; slower, useful for benchmarks.
CosineLinear — current linear-scaling cosine implementation.
ModifiedCosineGreedy — permits precursor-delta-shifted matches; common for

with fragment, neutral-loss, or hybrid matching.

ModifiedCosineHungarian — exact modified-cosine assignment.
NeutralLossesCosine — compares losses computed from precursor and fragments.
BlinkCosine — fast BLINK-style cosine approximation for larger matrices.
FlashSimilarity — optimized matrix scoring using spectral entropy or cosine

nearest-neighbor indexing.

More skills from K-Dense-AI/scientific-agent-skills

AadaptyvHow to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.
AaeonThis skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
AalphagenomeLook up precomputed AlphaGenome Atlas effects for any GRCh38 single-nucleotide variant (AVI score with Phred and 18 SHAP feature attributions, plus raw and quantile scores for RNA-seq, DNase, ATAC, ChIP-TF, ChIP-histone, CAGE, PRO-cap, splicing, polyadenylation and contact-map tracks), score variants or scan windows on demand with the AlphaGenome model for human and mouse (variant scoring, in silico mutagenesis, REF-versus-ALT track prediction), and build Atlas website deep links. Use when the user mentions AlphaGenome, AlphaGenome Atlas, AVI or AlphaGenome Variant Impact, DeepMind variant effect prediction, or wants to prioritise or mechanistically interpret non-coding, regulatory, splicing, enhancer, promoter, or chromatin-accessibility effects of SNVs from a VCF, credible set, or region. Research use only; not a clinical tool.
Aanalytical-method-validationPlan, execute, and document validation, verification, and transfer of analytical procedures under the governing framework - ICH Q2(R2) and Q14, USP <1220>/<1225>/<1226>, ICH M10 bioanalytical, CLSI EP, or ISO/IEC 17025. Use for HPLC, LC-MS/MS, GC, CE, ICP-MS, dissolution, qNMR, qPCR, NIR, and ligand binding or cell-based assays whenever the question is whether a procedure is fit for its intended purpose. Triggers include "method validation", "analytical method validation", "AMV", "validation protocol", "acceptance criteria", "linearity", "reportable range", "accuracy and precision", "repeatability", "intermediate precision", "recovery", "LOD", "LOQ", "detection limit", "quantitation limit", "specificity", "robustness", "method transfer", "method comparison", "Deming", "Passing-Bablok", "Bland-Altman", "equivalence testing", "OOS investigation", "ICH Q2", "Q2(R2)", "Q14", "USP 1225", "ICH M10", "incurred sample reanalysis", "ISR", "CLSI EP", and any request to show that an assay works.
AanndataData structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
AarborAutonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.
AarboretoInfer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.
AastropyCore Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.
AautoskillObserve the user's screen via screenpipe, detect repeated research workflows, match them against existing scientific-agent-skills, and draft new skills (or composition recipes that chain existing ones) for the patterns not yet covered. Use when the user asks to analyze their recent work and propose skills based on what they actually do. Requires the screenpipe daemon (https://github.com/screenpipe/screenpipe) running locally on port 3030 — the skill has no other data source and will refuse to run if screenpipe is unreachable. All detection runs locally; only redacted cluster summaries reach the LLM.
Abenchling-integrationBenchling Python SDK and REST API integration for registry entities, inventory, ELN entries, workflows, Benchling Apps, and Data Warehouse queries. Use when automating lab data with benchling-sdk or the v2 API.
Abgpt-paper-searchSearch scientific papers and retrieve structured experimental data extracted from full-text studies via the BGPT MCP server. Returns 25+ fields per paper including methods, results, sample sizes, quality scores, and conclusions. Use for literature reviews, evidence synthesis, and finding experimental details not available in abstracts alone.
AbidsUse this skill when working with Brain Imaging Data Structure (BIDS) datasets: organizing neuroscience and biomedical data (MRI, EEG, MEG, iEEG, PET, microscopy, NIRS, motion capture, EMG, MR spectroscopy, behavioral), querying BIDS layouts, validating compliance, converting DICOM to BIDS, writing metadata sidecars, or creating BIDS derivatives.

All agent skills → · MCP servers