synthetic-sciences/openscience is an AI workbench that carries out scientific research by reading papers, forming hypotheses, writing and running code, conducting experiments, analyzing results, and preparing reports. Researchers use it for work in machine learning, biology, physics, and chemistry with remote or local models. Catalogue add-ons extend its scientific workflows through skills and instructions.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add synthetic-sciences/openscience --skill matchmsgit clone --depth 1 https://github.com/synthetic-sciences/openscienceWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/synthetic-sciences/openscience/matchms)<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/matchms"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/matchms/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/matchms"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/matchms.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00062 | $0.01653 |
| Opus 5.5 | $0.00025 | $0.00661 |
| Sonnet 5.5 | $0.00012 | $0.00331 |
| Haiku 4.5 | $0.00006 | $0.00165 |
Grade A, and why
matchms scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
8 near-identical copies found in the catalogue:
- matchms — 97% identical, 9 lines differ
- matchms — 95% identical, 7 lines differ
- matchms — 95% identical, 10 lines differ
- matchms — 95% identical, 10 lines differ
- matchms — 95% identical, 10 lines differ
- matchms — 92% identical, 9 lines differ
- matchms — 83% identical, 10 lines differ
- matchms — 83% identical, 10 lines differ
How it starts
The opening of the file, as written. The whole thing — 209 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Matchms
Overview
Matchms is an open-source Python library for mass spectrometry data processing and analysis. Import spectra from various formats, standardize metadata, filter peaks, calculate spectral similarities, and build reproducible analytical workflows.
Core Capabilities
1. Importing and Exporting Mass Spectrometry Data
Load spectra from multiple file formats and export processed data:
from matchms.importing import load_from_mgf, load_from_mzml, load_from_msp, load_from_json
from matchms.exporting import save_as_mgf, save_as_msp, save_as_json
# Import spectra
spectra = list(load_from_mgf("spectra.mgf"))
spectra = list(load_from_mzml("data.mzML"))
spectra = list(load_from_msp("library.msp"))
# Export processed spectra
save_as_mgf(spectra, "output.mgf")
save_as_json(spectra, "output.json")
Supported formats:
- mzML and mzXML (raw mass spectrometry formats)
- MGF (Mascot Generic Format)
- MSP (spectral library format)
- JSON (GNPS-compatible)
- metabolomics-USI references
- Pickle (Python serialization)
For detailed importing/exporting documentation, consult references/importing_exporting.md.
2. Spectrum Filtering and Processing
Apply comprehensive filters to standardize metadata and refine peak data:
from matchms.filtering import default_filters, normalize_intensities
from matchms.filtering import select_by_relative_intensity, require_minimum_number_of_peaks
# Apply default metadata harmonization filters
spectrum = default_filters(spectrum)
# Normalize peak intensities
spectrum = normalize_intensities(spectrum)
# Filter peaks by relative intensity
spectrum = select_by_relative_intensity(spectrum, intensity_from=0.01, intensity_to=1.0)
# Require minimum peaks
spectrum = require_minimum_number_of_peaks(spectrum, n_required=5)
Filter categories:
- Metadata processing: Harmonize compound names, derive chemical structures, standardize adducts, correct charges
- Peak filtering: Normalize intensities, select by m/z or intensity, remove precursor peaks
- Quality control: Require minimum peaks, validate precursor m/z, ensure metadata completeness
- Chemical annotation: Add fingerprints, derive InChI/SMILES, repair structural mismatches
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago Changed · +6 lines 9f15e0c133c9
- 1mo ago First seen · 203 lines · 62 tokens per session scan A 78ec0e53cae8
matchms is a skill published in the GitHub repository synthetic-sciences/openscience (4,266 stars, last pushed yesterday), licensed Apache-2.0. It adds 62 tokens to every session and 1,653 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
songsee
Audio spectrograms/features (mel, chroma, MFCC) via CLI.
arxiv
Search arXiv papers by keyword, author, category, or ID.
research-paper-writing
Write ML papers for NeurIPS/ICML/ICLR: design→submit.
paper-revision-author
Revise independently drafted paper sections into one coherent LaTeX body before the abstract is written.
paper-plot-stub
Plot a results CSV (x, ybaseline, yours) as a two-line matplotlib chart and write a PDF. Demo-only.
knowledge-extension-ecosystem-lsp-code-intelligence
A codebase guide to LSP, the standard protocol behind editor features such as symbol queries, file diagnostics, and language-aware code information.