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 zamushwani/biomedical-ai-skills --skill computational-pathologygit clone --depth 1 https://github.com/zamushwani/biomedical-ai-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/zamushwani/biomedical-ai-skills/computational-pathology)<a href="https://agentmods.dev/skills/zamushwani/biomedical-ai-skills/computational-pathology"><img src="https://agentmods.dev/badge/skills/zamushwani/biomedical-ai-skills/computational-pathology/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/zamushwani/biomedical-ai-skills/computational-pathology"><img src="https://agentmods.dev/badge/skills/zamushwani/biomedical-ai-skills/computational-pathology.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 | $0.00000 | $0.09929 |
| Opus 5 | $0.00000 | $0.04965 |
| Sonnet 5 | $0.00000 | $0.01986 |
| Haiku 4.5 | $0.00000 | $0.00993 |
Grade A, and why
computational-pathology 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 11d 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 — 849 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Computational Pathology
Whole-slide image processing and slide-level modelling for cancer histopathology. Covers reading vendor formats with OpenSlide, the coordinate and resolution semantics that cause most WSI bugs, tissue detection, tile extraction, stain normalization, H&E colour deconvolution, pathology foundation models as tile encoders, multiple instance learning for slide-level prediction, cell segmentation and classification, spatial statistics on cell positions, and integration with molecular data.
When to Use This Skill
Activate when the user requests:
- Reading
.svs,.ndpi,.mrxs,.scn, or other whole-slide formats - OpenSlide, TiaToolbox, histolab, or slideio pipelines
- Tissue detection or background removal on a slide
- Tile or patch extraction for downstream modelling
- Stain normalization across slides or scanners
- H&E colour deconvolution, or separating hematoxylin from eosin
- Working at a target magnification or microns-per-pixel
- Tile embeddings from UNI, CONCH, Virchow, Prov-GigaPath, H-optimus or Phikon
- Multiple instance learning with CLAM, DSMIL, TransMIL or attention pooling
- Slide-level prediction from tile features
- Splitting a pathology cohort without leakage
- Cell or nucleus segmentation (StarDist, HoVer-Net, Cellpose, InstanSeg)
- Tumour region or tertiary lymphoid structure detection
- Spatial statistics on segmented cell positions
- Correlating morphology with matched expression or mutation data
Inputs
| Data Type | Format | Source |
|---|---|---|
| Whole-slide image | .svs, .ndpi, .mrxs, .tiff, .scn |
GDC (TCGA), CAMELYON, PANDA |
| Slide metadata | OpenSlide properties | embedded in the file |
| Annotations | GeoJSON, XML, .qpdata |
QuPath, ASAP, pathologist review |
| Reference tile | RGB image | a slide chosen as the stain target |
Environment
Versions verified 2026-08.
pip install openslide-python openslide-bin # 1.4.6 and 4.0.1.2
openslide-python is a BINDING, not the library. Its only dependency is
Pillow, so `pip install openslide-python` alone gives you an import that
fails at load time with a missing-library error.
openslide-bin ships the compiled OpenSlide C library (4.0.1) as a wheel,
which is why the upstream README tells you to install both. Before it
existed you needed a system package (apt/brew/conda). If you inherit a
pipeline that documents `brew install openslide`, that still works; do not
mix the two in one environment.
What ships with it
6 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.
- 11d ago First seen · 849 lines · 0 tokens per session scan A 5bfb5efd5c89
computational-pathology is a skill published in the GitHub repository zamushwani/biomedical-ai-skills (1 stars, last pushed 13d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 9,929 tokens. 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 2026-08-31.
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