Skill · em Dados, IA e pesquisa

pathml

Computational pathology toolkit for analyzing whole-slide images (WSI) and multiparametric imaging data. Use this skill when working with histopathology slides, H&E stained images, multiplex immunofluorescence (CODEX, Vectra), spatial proteomics, nucleus detection/segmentation, tissue graph…

Procedência

Origem: davila7/claude-code-templates
Caminho: cli-tool/components/skills/scientific/pathml
Versão fixada: 57f899e5394bb8ca166f38eacae8f0853cbfe033
Licença: MIT
Espelhado em 25/09/2026
nenhum download no Claude Code Templates (lido em 25/09/2026)

Antes de instalar

7 arquivos · 109,1 KB · só texto, nenhum script

Instalar na sua CLI

O comando baixa a versão fixada (commit 57f899e) direto da origem, para a pasta que a CLI lê. Precisa de curl (macOS e Linux); no Windows não há comando, porque o Rook Labs é para macOS.

Claude Code

Neste projeto: instala em .claude/skills/pathml/.

d=".claude/skills/pathml"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/pathml"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/data_management.md" "$u/references/data_management.md" \
  -o "$d/references/graphs.md" "$u/references/graphs.md" \
  -o "$d/references/image_loading.md" "$u/references/image_loading.md" \
  -o "$d/references/machine_learning.md" "$u/references/machine_learning.md" \
  -o "$d/references/multiparametric.md" "$u/references/multiparametric.md" \
  -o "$d/references/preprocessing.md" "$u/references/preprocessing.md" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Global: instala em ~/.claude/skills/pathml/.

d="$HOME/.claude/skills/pathml"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/pathml"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/data_management.md" "$u/references/data_management.md" \
  -o "$d/references/graphs.md" "$u/references/graphs.md" \
  -o "$d/references/image_loading.md" "$u/references/image_loading.md" \
  -o "$d/references/machine_learning.md" "$u/references/machine_learning.md" \
  -o "$d/references/multiparametric.md" "$u/references/multiparametric.md" \
  -o "$d/references/preprocessing.md" "$u/references/preprocessing.md" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Codex

Neste projeto: instala em .agents/skills/pathml/.

d=".agents/skills/pathml"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/pathml"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/data_management.md" "$u/references/data_management.md" \
  -o "$d/references/graphs.md" "$u/references/graphs.md" \
  -o "$d/references/image_loading.md" "$u/references/image_loading.md" \
  -o "$d/references/machine_learning.md" "$u/references/machine_learning.md" \
  -o "$d/references/multiparametric.md" "$u/references/multiparametric.md" \
  -o "$d/references/preprocessing.md" "$u/references/preprocessing.md" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Global: instala em ~/.agents/skills/pathml/.

d="$HOME/.agents/skills/pathml"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/pathml"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/data_management.md" "$u/references/data_management.md" \
  -o "$d/references/graphs.md" "$u/references/graphs.md" \
  -o "$d/references/image_loading.md" "$u/references/image_loading.md" \
  -o "$d/references/machine_learning.md" "$u/references/machine_learning.md" \
  -o "$d/references/multiparametric.md" "$u/references/multiparametric.md" \
  -o "$d/references/preprocessing.md" "$u/references/preprocessing.md" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Antigravity

Neste projeto: instala em .agents/skills/pathml/.

d=".agents/skills/pathml"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/pathml"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/data_management.md" "$u/references/data_management.md" \
  -o "$d/references/graphs.md" "$u/references/graphs.md" \
  -o "$d/references/image_loading.md" "$u/references/image_loading.md" \
  -o "$d/references/machine_learning.md" "$u/references/machine_learning.md" \
  -o "$d/references/multiparametric.md" "$u/references/multiparametric.md" \
  -o "$d/references/preprocessing.md" "$u/references/preprocessing.md" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Global: instala em ~/.gemini/antigravity-cli/skills/pathml/.

d="$HOME/.gemini/antigravity-cli/skills/pathml"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/pathml"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/data_management.md" "$u/references/data_management.md" \
  -o "$d/references/graphs.md" "$u/references/graphs.md" \
  -o "$d/references/image_loading.md" "$u/references/image_loading.md" \
  -o "$d/references/machine_learning.md" "$u/references/machine_learning.md" \
  -o "$d/references/multiparametric.md" "$u/references/multiparametric.md" \
  -o "$d/references/preprocessing.md" "$u/references/preprocessing.md" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Peça ao Rook

Já usa o Rook Labs? Cole no chat do Rook: instale a skill https://rooklabs.sh/marketplace/cct.pathml

Prévia do SKILL.md

---
name: pathml
description: Computational pathology toolkit for analyzing whole-slide images (WSI) and multiparametric imaging data. Use this skill when working with histopathology slides, H&E stained images, multiplex immunofluorescence (CODEX, Vectra), spatial proteomics, nucleus detection/segmentation, tissue graph construction, o…
---

# PathML

## Overview

PathML is a comprehensive Python toolkit for computational pathology workflows, designed to facilitate machine learning and image analysis for whole-slide pathology images. The framework provides modular, composable tools for loading diverse slide formats, preprocessing images, constructing spatial graphs, training dee…

## When to Use This Skill

Apply this skill for:
- Loading and processing whole-slide images (WSI) in various proprietary formats
- Preprocessing H&E stained tissue images with stain normalization
- Nucleus detection, segmentation, and classification workflows
- Building cell and tissue graphs for spatial analysis
- Training or deploying machine learning models (HoVer-Net, HACTNet) on pathology data
- Analyzing multiparametric imaging (CODEX, Vectra, MERFISH) for spatial proteomics
- Quantifying marker expression from multiplex immunofluorescence
- Managing large-scale pathology datasets with HDF5 storage
- Tile-based analysis and stitching operations

## Core Capabilities

PathML provides six major capability areas documented in detail within reference files:

### 1. Image Loading & Formats

Load whole-slide images from 160+ proprietary formats including Aperio SVS, Hamamatsu NDPI, Leica SCN, Zeiss ZVI, DICOM, and OME-TIFF. PathML automatically handles vendor-specific formats and provides unified interfaces for accessing image pyramids, metadata, and regions of interest.

**See:** `references/image_loading.md` for supported formats, loading strategies, and working with different slide types.

### 2. Preprocessing Pipelines

Build modular preprocessing pipelines by composing transforms for image manipulation, quality control, stain normalization, tissue detection, and mask operations. PathML's Pipeline architecture enables reproducible, scalable preprocessing across large datasets.

**Key transforms:**
- `StainNormalizationHE` - Macenko/Vahadane stain normalization
…

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