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 agentlas-ai/agentlas-desktop --skill matchmsgit clone --depth 1 https://github.com/agentlas-ai/agentlas-desktopWrote 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/agentlas-ai/agentlas-desktop/matchms)<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/matchms"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/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/agentlas-ai/agentlas-desktop/matchms"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/matchms.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00064 | $0.02686 |
| Opus 5.5 | $0.00026 | $0.01074 |
| Sonnet 5.5 | $0.00013 | $0.00537 |
| Haiku 4.5 | $0.00006 | $0.00269 |
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.
This is a copy
100% identical to matchms — 50 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 293 lines — stays where its author put it; the contents beside it link to each section 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:
- 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:
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
- Inspect the inputs. Record format, spectrum count, MS level, precursor coverage, ion mode, peak counts, and identifier fields.
- Load with metadata harmonization enabled unless preserving source keys is a deliberate requirement.
- Apply the same peak-processing steps to query and reference spectra. Keep metadata enrichment separate when reference annotations are richer.
- Drop invalid spectra explicitly. Many
require_*filters returnNone. - Choose the score from the scientific question, not from convenience.
Modified and neutral-loss scores require valid
precursor_mz. - Estimate
len(references) * len(queries)before scoring. A sparse result container does not automatically avoid computing every requested pair. - Report score settings and evidence. Include tolerance, preprocessing, score name, number of matched peaks when available, and candidate metadata.
- Validate top hits visually and chemically. Use mirror plots, precursor agreement, ion/adduct compatibility, and orthogonal evidence.
What ships with it
7 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 First seen · 293 lines · 64 tokens per session scan A 059f2bcca9e8
matchms is a skill published in the GitHub repository agentlas-ai/agentlas-desktop (6 stars, last pushed yesterday), licensed Apache-2.0. It adds 64 tokens to every session and 2,686 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to matchms, differing in 50 lines, and is treated as a copy.
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