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 zarr-pythongit 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/zarr-python)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/zarr-python"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/zarr-python/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/zarr-python"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/zarr-python.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00059 | $0.02359 |
| Opus 5.5 | $0.00024 | $0.00944 |
| Sonnet 5.5 | $0.00012 | $0.00472 |
| Haiku 4.5 | $0.00006 | $0.00236 |
Grade A, and why
zarr-python 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 20d 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
1 near-identical copy found in the catalogue:
- zarr-python — 100% identical, 310 lines differ
How it starts
The opening of the file, as written. The whole thing — 189 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Zarr Python
When to use
Use for chunked scientific arrays, hierarchical stores, codecs, sharding, partial reads,
cloud object storage, and NumPy/Dask/Xarray interoperability. This community guide targets
Zarr-Python 3.4.0, released 2026-09-15, with Python 3.12+. The package version and
on-disk format are separate: this release reads/writes formats 2 and 3; new arrays default
to format 3. Keep downstream packages that require zarr<3 in their own environments.
Install
uv pip install "zarr==3.4.0" "numpy==2.5.3"
# Optional remote backends and migration CLI:
uv pip install "zarr[remote,cli]==3.4.0" "fsspec==2026.9.0" "s3fs==2026.9.0" "gcsfs==2026.8.1"
Commit the project's resolved lockfile. Optional integration versions exercised here: Dask 2026.8.0, Xarray 2026.9.0, h5py 3.16.0, NumCodecs 0.17.0, obstore 0.11.1. Local examples below and in the references use tiny synthetic arrays; remote snippets are illustrative and require a real authorized store. This does not establish cloud permissions, production throughput, or compatibility of every downstream reader.
Workflow
- Inspect shape, dtype, axis names, coordinates, units, missing-value convention, format, codec availability, and intended readers. Preserve sample IDs and axis order.
- Choose chunks for actual selections and a memory budget. For sharding, choose shard dimensions that are multiples of chunk dimensions. Benchmark representative data.
- Create a new destination (
overwrite=Falseormode="w-"). Usemode="r"for inspection;"a"can create a missing store and"w"destroys existing content. - Write bounded blocks. Assign one writer per stored chunk, or per shard when sharded; serialize metadata, append, and resize operations.
- Reopen read-only and compare values, dtype, shape, coordinates, units, masks and
metadata. An unwritten or missing chunk normally reads as
fill_value; a successful open alone does not prove data completeness. - For a completed group hierarchy, optionally consolidate metadata. Format-3 consolidation is experimental; refresh it after metadata changes and verify the actual consumers. Publish a completed store only after validation.
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.
- 20d ago Changed · -74 lines · +7 tokens per session 1edaabfc3fc6
- 21d ago Changed · +4 lines 110105d3c029
- 1mo ago First seen · 259 lines · 52 tokens per session scan A 36f6900413be
zarr-python is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (53,578 stars, last pushed 2d ago), licensed MIT. It adds 59 tokens to every session and 2,359 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 2026-09-03.
Other skills, from other repositories
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discovery-toolbox
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rdkit-qsar-pharmacophore
Computes 2048-bit ECFP4 Morgan fingerprints from SMILES, trains LightGBM regressors for pIC50 prediction, and extracts SHAP feature attributions.
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…
bulk-rnaseq-counts-to-de-deseq2
Run differential expression analysis on bulk RNA-seq count data with DESeq2 (R). Covers DESeqDataSet construction from a count matrix, tximport (Salmon/Kallisto), featureCounts, or SummarizedExperiment; pre-filtering; design formulas (simple, batch, paired, interaction, multi-factor, LRT); result extraction by…
seurat-skill
Comprehensive Seurat v5 (R) guide for single-cell RNA-seq and multimodal analysis. Covers installation, standard workflows (Normalize/SCTransform), clustering, integration (CCA/RPCA/Harmony), differential expression (FindMarkers/FindAllMarkers), visualization (DimPlot/FeaturePlot/VlnPlot/DoHeatmap), spatial…