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 scanpygit 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/scanpy)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/scanpy"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/scanpy/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/scanpy"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/scanpy.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 | $0.00093 | $0.04178 |
| Opus 5 | $0.00046 | $0.02089 |
| Sonnet 5 | $0.00019 | $0.00836 |
| Haiku 4.5 | $0.00009 | $0.00418 |
Grade A, and why
scanpy 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 — 321 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Scanpy: Single-Cell Analysis
Overview
Scanpy is a scalable Python toolkit for analyzing single-cell RNA-seq data, built on AnnData. Apply this skill for complete single-cell workflows including quality control, normalization, dimensionality reduction, clustering, marker gene identification, visualization, and trajectory analysis. Current stable release: scanpy 1.12.x (January 2026).
Installation
Requires Python 3.12+ (scanpy 1.12 dropped Python ≤3.11) and anndata ≥0.10.
uv pip install "scanpy[leiden]"
The [leiden] extra installs python-igraph and leidenalg, required for Leiden clustering. For reproducible environments, pin a version: uv pip install "scanpy[leiden]==1.12.1".
For large or out-of-core datasets, many functions support Dask arrays (experimental):
uv pip install "scanpy[leiden]" dask
See the Using dask with Scanpy tutorial. For GPU-accelerated scanpy-like operations, use rapids-singlecell as a separate package.
If the input is an R-native single-cell object (.rds, .RData, Seurat, or SingleCellExperiment), first convert it to .h5ad with R tooling, then load it with Scanpy. Read references/r_interop.md for agent-run installation and conversion instructions across macOS, Linux, and Windows.
For AnnData structure and I/O details, use the anndata skill. For probabilistic models and batch correction, use scvi-tools.
When to Use This Skill
This skill should be used when:
- Analyzing single-cell RNA-seq data (.h5ad, 10X, CSV formats)
- Working with R-friendly single-cell datasets (
.rds,.RData, Seurat, SingleCellExperiment) that need conversion to.h5ad - Performing quality control on scRNA-seq datasets
- Creating UMAP, t-SNE, or PCA visualizations
- Identifying cell clusters and finding marker genes
- Annotating cell types based on gene expression
- Conducting trajectory inference or pseudotime analysis
- Generating publication-quality single-cell plots
What ships with it
24 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.
- assets/analysis_template.py 9.6 KB runs code
- assets/celltype_mapping.json 184 B
- assets/gene_signatures.json 355 B
- assets/pipeline_config.json 380 B
- references/analysis_workflow.md 6.5 KB
- references/api_reference.md 8.2 KB
- references/plotting_guide.md 9.9 KB
- references/r_interop.md 11 KB
- references/standard_workflow.md 6.0 KB
- scripts/_common.py 4.4 KB runs code
- scripts/annotate.py 3.5 KB runs code
- scripts/batch_correct.py 2.7 KB runs code
- scripts/cluster.py 2.5 KB runs code
- scripts/convert.py 1.5 KB runs code
- scripts/find_markers.py 3.4 KB runs code
- scripts/inspect_data.py 2.8 KB runs code
- scripts/plot.py 3.5 KB runs code
- scripts/preprocess.py 3.7 KB runs code
- scripts/pseudobulk.py 2.9 KB runs code
- scripts/qc_analysis.py 4.5 KB runs code
- scripts/reduce_dimensions.py 2.6 KB runs code
- scripts/run_pipeline.py 7.9 KB runs code
- scripts/score_genes.py 3.7 KB runs code
- scripts/subset.py 2.5 KB runs code
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 · 321 lines · 93 tokens per session scan A aad13ea7d74d
scanpy is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (44,469 stars, last pushed today), licensed MIT. It adds 93 tokens to every session and 4,178 once invoked, about $0.0005 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
A routed repertoire of 90 scientific thinking operators for biological research agents - visual reasoning, detectability and information budgets, search reframing, causal identification, competing explanations, observation and selection processes, pipeline artifact diagnosis, effort allocation, and confirmation…
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…
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.
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.
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.