Scientific Agent Skills is a collection of reusable procedures that give AI agents capabilities for scientific research across areas such as biology, chemistry, medicine, and drug discovery. It is used by researchers and by people building AI scientist workflows with compatible coding agents. The catalogue contains many of the project's skills and supporting 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 K-Dense-AI/scientific-agent-skills --skill matchmsgit clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skillsWrote 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/k-dense-ai/scientific-agent-skills/matchms)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/matchms"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/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/k-dense-ai/scientific-agent-skills/matchms"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/matchms.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- 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 | $0.00064 | $0.02686 |
| Opus 5 | $0.00032 | $0.01343 |
| Sonnet 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 8d 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.
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.
- 8d ago First seen · 293 lines · 64 tokens per session scan A 059f2bcca9e8
matchms is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (44,469 stars, last pushed today), licensed MIT. It adds 64 tokens to every session and 2,686 once invoked, about $0.0003 per session on Opus 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 2026-09-03.
Other skills, from other repositories
discovery-toolbox
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discovery-director
Operate as a research director making original discoveries from a given biological question and dataset. Use when the task is open-ended scientific research, exploring omics or experimental data for findings, hypothesis generation and testing, screening a large candidate space of genes, variants, features or…
polars-dovmed
Search PMC Open Access and bioRxiv corpora with polars-dovmed. Use when structured, reproducible literature queries should run through the hosted API or local parquet indexes.
bio-interdomain-hgt
Detect and polarize interdomain horizontal gene transfer with homology, context, and phylogenetic checks. Use when studying lateral gene transfer, virus-host gene exchange, endogenous viral elements, or donor direction.
csag-extraction
Extract a Conditional Scientific Argumentation Graph and grounded Q&A from a manuscript. Use when representing assertions, contexts, evidence links, and inference steps in machine-readable form.
exploratory-data-analysis
Inspect scientific data and generate a Markdown structure-and-quality report. Use when triaging tabular, array, sequence, HDF5, JSON, or raster files before downstream analysis.