Agent-Skills.md

Agent Skills: PathML

Full-featured computational pathology toolkit. Use for advanced WSI analysis including multiplexed immunofluorescence (CODEX, Vectra), nucleus segmentation, tissue graph construction, and ML model training on pathology data. Supports 160+ slide formats. For simple tile extraction from H&E slides, histolab may be simpler.

UncategorizedID: k-dense-ai/claude-scientific-skills/pathml

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k-dense-ai/claude-scientific-skills
K-Dense-AILicense: MIT
16,1681,773

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pnpm dlx add-skill https://github.com/K-Dense-AI/claude-scientific-skills/tree/HEAD/skills/pathml

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skills/pathml/SKILL.md

Skill Metadata

Name
pathml
Description
"Use PathML for local, research-only computational pathology workflows: load and tile slides, build preprocessing and QC pipelines, manage h5path data, quantify multiplex images, construct spatial graphs, and plan bounded model inference."

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:

  1. Confirm authorization, consent/waiver, data-use terms, and institutional policy.
  2. De-identify pixels and metadata; keep the re-identification key outside the analysis workspace.
  3. Use pseudonymous patient_id, slide_id, and specimen_id values. Do not put direct identifiers in filenames, logs, .h5path labels, model cards, or reports.
  4. Keep inputs, intermediates, and outputs on approved local encrypted storage.
  5. 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'))"

PathML 3.0.5 declares no package extras: do not use pathml[all]. Its base distribution pins a large scientific/ML stack, including Torch 2.8.0, ONNX 1.17.0, ONNX Runtime 1.17.x, OpenSlide Python 1.3.1, python-bioformats 4.1.0, and python-javabridge 4.0.4.

Install native prerequisites before the uv command:

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

# macOS
brew install openslide openjdk@17

# Windows OpenSlide option documented upstream
vcpkg install openslide

Java/Bio-Formats is needed for the broad multidimensional format backend. OpenSlide handles common brightfield WSI formats more efficiently. CUDA is optional and must match the pinned PyTorch build; follow PyTorch's platform selector rather than guessing a CUDA wheel. See references/image_loading.md.

Stable minimal workflow

PathML 3.0.5 uses slide convenience classes and SlideData.run(). It does not provide SlideData.from_slide(), and Pipeline does not have run():

from pathml.core import HESlide
from pathml.preprocessing import BoxBlur, Pipeline, TissueDetectionHE

slide = HESlide("data/pseudonymous_slide.svs", backend="openslide")
pipeline = Pipeline(
    [
        BoxBlur(kernel_size=5),
        TissueDetectionHE(mask_name="tissue", min_region_size=5000),
    ]
)
slide.run(
    pipeline,
    distributed=False,
    tile_size=512,
    tile_stride=512,
    level=0,
    tile_pad=False,
)
slide.write("derived/pseudonymous_slide.h5path")

Start with a bounded manual sample before a full run:

from itertools import islice

for tile in islice(slide.generate_tiles(shape=512, stride=512, level=0), 8):
    pipeline.apply(tile)
    assert tile.masks["tissue"].shape[:2] == tile.image.shape[:2]

Tiles use (i, j) = (row, column) coordinates at the selected pyramid level. For OpenSlide, PathML maps them to level-0 coordinates internally. Record the level and downsample; convert to (x, y) or micrometres explicitly downstream.

Research workflow

  1. Inventory locally. Validate the manifest, reject URLs/symlinks, inspect only allowlisted technical metadata, and remove identifiers.
  2. Freeze splits. Assign every patient and all their slides to one split before generating overlapping tiles, graphs, normalization references, or features.
  3. Plan bounds. Estimate tile count, RAM, output size, and pipeline stages.
  4. Pilot preprocessing. Inspect tissue masks, whitespace/artifact labels, stain behavior, edge padding, and empty-mask cases on representative training slides. Do not tune from test slides.
  5. Run and preserve coordinates. Keep tile level, (i, j), downsample, MPP, mask names, QC decisions, and failed/skipped tiles.
  6. Build spatial data deliberately. Validate channel order, physical units, instance labels, node-feature alignment, graph edges, and cell-to-tissue assignments.
  7. Infer in bounded batches. Verify model provenance and checksum without loading unknown pickle checkpoints. Keep predictions linked to slide/tile coordinates and stitch overlaps with a documented rule.
  8. Report provenance and limits. Include package lock, source hashes, scanner, stain, parameters, seeds, split manifest, model card, exclusions, and QC.

No-network default and explicit consent gate

Do not instantiate download-capable classes or set dataset download=True unless the user explicitly opts in after receiving the endpoint and disclosure:

  • SegmentMIFRemote downloads an ONNX file from https://huggingface.co/pathml/test/resolve/main/mesmer.onnx at construction, then runs inference locally. Stable source does not upload image pixels. The request still discloses network metadata such as IP address and headers and creates temp.onnx; there is no built-in checksum or offline flag.
  • Deprecated SegmentMIF imports local DeepCell Mesmer, but DeepCell model initialization may need separately provisioned weights. It is not a PathML extra and is not the preferred stable API.
  • RemoteTestHoverNet downloads a model from Hugging Face.
  • PanNukeDataModule(download=True) contacts Warwick; DeepFocusDataModule contacts Zenodo. Both default to download=False.

Before any future hosted prediction call, state the exact destination, pixel channels/regions, metadata, identifiers, retention, legal basis, and safeguards; obtain explicit consent; and never send PHI by default. Prefer reviewed, checksummed local model artifacts and local inference.

Model-code security

  • PyTorch model.eval() means evaluation mode for modules; it is not Python's dangerous built-in evaluator. Never use Python dynamic evaluation or execution.
  • Do not name local files pathml.py, torch.py, onnx.py, or after standard libraries; shadow modules can silently change imports.
  • PathML's EntityDataset loads .pt objects with weights_only=False. Never open an untrusted graph/checkpoint. Treat pickle-based pipelines and .pt files as executable code.
  • ONNX is safer than pickle but not inherently trusted. Verify source, SHA-256, expected input/output schema, file size, and runtime limits; use isolation for third-party models.

Bundled local CLIs

All helpers reject URLs and symlinks, cap inputs/work, use strict JSON, avoid network access, and require no PathML import for --help:

python scripts/slide_manifest.py validate --manifest manifest.csv --root .
python scripts/slide_manifest.py inspect --slide data/example.svs --root .
python scripts/plan_pipeline.py --width 100000 --height 80000 --tile-size 512 --stride 512
python scripts/image_qc.py synthetic --width 256 --height 256
python scripts/validate_spatial_schema.py graph --input graph.json --root .
python scripts/validate_spatial_schema.py multiplex --input cells.csv --root .
python scripts/plan_inference.py --tile-count 4000 --batch-size 16 --height 256 --width 256

The inference planner reads numbers or a bounded JSON model card only; it never imports a model framework or opens a checkpoint.

Detailed references

  • references/image_loading.md — slide classes, backends, formats, levels, coordinates, technical metadata, and privacy.
  • references/preprocessing.md — stable transforms, masks/QC, stain processing, pipeline execution, and leakage prevention.
  • references/data_management.md — .h5path, manifests, datasets, provenance, splits, and safe downloads.
  • references/multiparametric.md — multidimensional layout, CODEX/Vectra, quantification, AnnData, DeepCell/Mesmer, and network disclosure.
  • references/graphs.md — instance maps, feature alignment, KNN/RAG/HACT graphs, spatial units, schemas, and validation.
  • references/machine_learning.md — HoVer-Net/HACTNet, local ONNX inference, batching, checkpoint trust, evaluation, and model provenance.

Primary sources

All checked 2026-07-23:

  • PyPI metadata: https://pypi.org/project/pathml/3.0.5/
  • Stable source tag: https://github.com/Dana-Farber-AIOS/pathml/tree/v3.0.5
  • Releases: https://github.com/Dana-Farber-AIOS/pathml/releases
  • Stable documentation: https://pathml.readthedocs.io/en/stable/
  • Rosenthal et al. (2022), PathML toolkit: https://doi.org/10.1158/1541-7786.MCR-21-0665
  • Omar et al. (2025), multiplex workflows: https://doi.org/10.1016/j.labinv.2025.104220