A local research toolkit for computational pathology, the use of software to analyze digitized tissue slides and medical images.

Use it to load and tile slides, build preprocessing and quality-control pipelines, work with h5path data, quantify multiplex images, create spatial graphs, and plan bounded model inference.

It organizes slide preparation and quality checks while keeping the workflow intended for research rather than clinical diagnosis.

pathml — Skill for Claude Code by K-Dense-AI

About the project

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.

K-Dense-AI/scientific-agent-skills · 44,003 stars · on GitHub · arxiv.org

Install

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.

Any agent

npx skills add K-Dense-AI/scientific-agent-skills --skill pathml

Clone the repo

git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills

Made for: Claude Code.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/pathml/github.svg)](https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/pathml)

Your own site

<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/pathml"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/pathml/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/pathml"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/pathml.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 46 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 2,772 The whole file, excluding the scripts and references it only reads on demand.

Security scan B 1 finding. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

Socket pass 9 Apr 2026
Snyk pass 9 Apr 2026
NVIDIA SkillSpector warn 7 Sept 2026

SkillSpector: 1 finding, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

medium Privilege Escalation · line 67
Commands invoke sudo or root privileges. Verify this elevated access is necessary and justified.

Fix: Avoid sudo/root unless strictly required. Prefer least-privilege patterns. If elevation is needed, document the justification and scope.

How audits are shown

Origin original No closer match found in the catalogue.

Token cost

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	$0.00046	$0.02772
Opus 5	$0.00023	$0.01386
Sonnet 5	$0.00009	$0.00554
Haiku 4.5	$0.00005	$0.00277

Measured 6d ago against content hash 4a8ba6ac2bb8, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade B, and why

pathml scanned grade B with 1 finding 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 6d ago.

The scan reads SKILL.md. This mod also ships 6 executable files (scripts/_common.py, scripts/image_qc.py, scripts/plan_inference.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

Asks for rootmediumPrivilege escalation

A mod that escalates privileges can change anything on the machine, not only the project.

sudo apt-get install openslide-tools gcc g++ libblas-dev liblapack-dev openjdk-17-jdk

skills/pathml/SKILL.md · 240 lines

How it starts

The opening of the file, as written. The whole thing — 240 lines — stays where its author put it; the contents beside it link to each section on GitHub.

PathML

Scope and safety boundary

Use PathML for local computational pathology research. It is beta research software, not a validated medical device, diagnostic system, clinical decision support tool, or substitute for a pathologist. Do not use outputs to diagnose, grade, stage, or treat a patient.

Pathology files may contain faces, labels, accession numbers, patient identifiers, DICOM tags, filenames, or linked clinical data. Before processing:

Confirm authorization, consent/waiver, data-use terms, and institutional policy.
De-identify pixels and metadata; keep the re-identification key outside the analysis workspace.
Use pseudonymous patient_id, slide_id, and specimen_id values. Do not put direct identifiers in filenames, logs, .h5path labels, model cards, or reports.
Keep inputs, intermediates, and outputs on approved local encrypted storage.
Split by patient (then slide) before tiling or fitting any preprocessing step.

Version baseline, verified 2026-07-23

Installable stable release: PyPI pathml==3.0.5, published 2026-03-24.
The v3.0.5 release notes state Python 3.10-3.12 and sunset 3.9. PyPI does not declare Requires-Python and still has a stale 3.8 classifier, so use the release statement and test the exact environment.
GitHub releases v3.0.6 (2026-04-14) and v3.0.7 (2026-07-09) exist, but PyPI has no artifacts for them as of this review. v3.0.7 updates Torch/TorchVision/ torch-geometric and ONNX export code. Do not mix those source dependencies with the 3.0.5 wheel.
ReadTheDocs /latest identifies itself as 3.0.5. Examples here were checked against the v3.0.5 tag and PyPI wheel metadata, not unversioned snippets.
This skill is MIT-licensed. PathML itself is GPL-2.0 with upstream commercial licensing options; review upstream terms before redistribution.

Reproducible installation

Use Python 3.11 unless the project has tested another supported interpreter:

uv venv --python 3.11
source .venv/bin/activate
uv pip install "pathml==3.0.5"
python -c "import importlib.metadata as m; print(m.version('pathml'))"

Read the full file on GitHub · 240 lines

Files

What ships with it

12 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.

Changes

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.

6d ago First seen · 240 lines · 46 tokens per session scan B 4a8ba6ac2bb8

Subscribe to this mod's changes

pathml is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (44,003 stars, last pushed 2d ago), licensed MIT. It adds 46 tokens to every session and 2,772 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it B with 1 finding (asks for root). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

Scientific ai-scientist bioinformatics chemoinformatics claude claude-skills claudecode

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…

dekan-aleksandr/biodiscovery-skills · 122 tokens

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…

dekan-aleksandr/biodiscovery-skills · 114 tokens

pathml