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 histolabgit 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/histolab)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/histolab"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/histolab/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/histolab"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/histolab.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 | $0.00060 | $0.02102 |
| Opus 5.5 | $0.00024 | $0.00841 |
| Sonnet 5.5 | $0.00012 | $0.00420 |
| Haiku 4.5 | $0.00006 | $0.00210 |
Grade A, and why
histolab 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 26d 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:
- histolab — 100% identical, 316 lines differ
- histolab — 92% identical, 307 lines differ
- histolab — 84% identical, 780 lines differ
- histolab — 84% identical, 782 lines differ
- histolab — 84% identical, 782 lines differ
- histolab — 84% identical, 748 lines differ
- histolab — 84% identical, 782 lines differ
- histolab — 83% identical, 752 lines differ
How it starts
The opening of the file, as written. The whole thing — 166 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Histolab
When to use
Use Histolab to inspect WSI metadata, identify tissue, extract image tiles, and standardize H&E staining. Its masks and scores are image-processing heuristics; they do not diagnose cancer, count individual cells, or establish image quality.
Installation
Histolab 0.7.0 remains the latest published release as of the review date. Its release constraints require Python <3.12, NumPy <=1.24.4, scikit-image <0.19.4, SciPy <1.10.1, Pillow <11, and openslide-python 1.3.1. Keep this stack isolated from modern scientific environments. Windows is not supported by this Histolab release.
Install native OpenSlide for your system, then create a dedicated environment (Python 3.10 was tested):
uv venv --python 3.10 .venv-histolab
uv pip install --python .venv-histolab/bin/python 'histolab==0.7.0' pooch matplotlib
.venv-histolab/bin/python -c 'import openslide; print(openslide.__library_version__)'
On macOS with Homebrew, brew install openslide installs the native library.
If the older Python binding cannot find it, launch Python with the library path
set immediately before Python starts:
env DYLD_FALLBACK_LIBRARY_PATH="$(brew --prefix openslide)/lib" .venv-histolab/bin/python -c 'import openslide; print(openslide.__library_version__)'
pooch is optional for remote examples. Start with a local slide or the tiny
bundled cmu_small_region sample; other sample functions may download hundreds
of megabytes. Exact mpp extraction also needs large-image and a matching
source plugin; see slide management.
Workflow
- Inspect
slide.dimensions,slide.levels(a list), andslide.level_dimensions(level)(a method). Check both MPP axes in metadata. - Select physical field of view and pixel resolution; level numbers are not interchangeable across scanners. Preserve level-0 coordinate bounds.
- Choose
TissueMaskfor all tissue sections orBiggestTissueBoxMaskfor the largest section's bounding box. Inspect the mask at its actual resolution. - Configure a tiler and preview with the same mask passed to extraction. Preview methods return a Pillow image; save or display that return value.
- Extract into a distinct per-slide/per-strategy directory. Count saved files, inspect representative tiles, and retain parameters, source IDs and QC flags.
- Split datasets by patient before training/validation/test tile assignment. Fit stain normalization targets on training data only and validate on held-out scanners. A seed reproduces sampling; it does not prevent patient leakage.
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.
- 26d ago Changed · -99 lines · -5 tokens per session af0c6c98951d
- 27d ago Changed · +4 lines 2e81e3f61589
- 1mo ago Changed · +17 lines af4b9a0ab9de
- 1mo ago First seen · 244 lines · 65 tokens per session scan A bb488555cc3b
histolab is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (54,953 stars, last pushed yesterday), licensed MIT. It adds 60 tokens to every session and 2,102 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
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 reciprocal hits, genomic context, and gene trees. Use for virus-host gene exchange, endogenous viral elements, or donor direction.
scholion
Second-opinion support for individuals and clinicians, one person's data at a time — genome, laboratory history, prescriptions, wearables. It reads them locally and states what the data cannot support. No data is baked into the skill: everyone supplies their own. Use it when you need to check a physician's…
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.
notebooks
Author, run and convert marimo or Jupyter notebooks. Use when delivering an analysis notebook executed end to end with figures embedded.