Pyhealth
K-Dense-AI/scientific-agent-skills
Builds and validates PyHealth clinical machine-learning pipelines for EHR, signals, imaging, and medical codes.
Computational pathology toolkit for analyzing whole-slide images (WSI) and multiparametric imaging data.
Install Pathml
$ npx skills add davila7/claude-code-templates --skill pathml -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install davila7/claude-code-templates pathml --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/davila7/claude-code-templates.git skills-src && mkdir -p .claude/skills && cp -r skills-src/cli-tool/components/skills/scientific/pathml .claude/skills/pathml && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "pathml" agent skill from https://github.com/davila7/claude-code-templates/tree/main/cli-tool/components/skills/scientific/pathml into .claude/skills/pathml/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pathml", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/davila7/claude-code-templates/tree/main/cli-tool/components/skills/scientific/pathmlType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add davila7/claude-code-templates --skill pathml -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install davila7/claude-code-templates pathml --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/davila7/claude-code-templates.git skills-src && mkdir -p .agents/skills && cp -r skills-src/cli-tool/components/skills/scientific/pathml .agents/skills/pathml && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "pathml" agent skill from https://github.com/davila7/claude-code-templates/tree/main/cli-tool/components/skills/scientific/pathml into .agents/skills/pathml/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pathml", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add davila7/claude-code-templates --skill pathml -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install davila7/claude-code-templates pathml --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/davila7/claude-code-templates.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/cli-tool/components/skills/scientific/pathml .cursor/skills/pathml && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "pathml" agent skill from https://github.com/davila7/claude-code-templates/tree/main/cli-tool/components/skills/scientific/pathml into .cursor/skills/pathml/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pathml", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/davila7/claude-code-templates.git --path cli-tool/components/skills/scientific/pathml--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add davila7/claude-code-templates --skill pathml -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install davila7/claude-code-templates pathml --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/davila7/claude-code-templates.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/cli-tool/components/skills/scientific/pathml .gemini/skills/pathml && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "pathml" agent skill from https://github.com/davila7/claude-code-templates/tree/main/cli-tool/components/skills/scientific/pathml into .gemini/skills/pathml/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pathml", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install davila7/claude-code-templates pathmlInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add davila7/claude-code-templates --skill pathml -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/davila7/claude-code-templates.git skills-src && mkdir -p .github/skills && cp -r skills-src/cli-tool/components/skills/scientific/pathml .github/skills/pathml && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "pathml" agent skill from https://github.com/davila7/claude-code-templates/tree/main/cli-tool/components/skills/scientific/pathml into .github/skills/pathml/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pathml", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add davila7/claude-code-templates --skill pathml -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install davila7/claude-code-templates pathml --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/davila7/claude-code-templates.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/cli-tool/components/skills/scientific/pathml .opencode/skills/pathml && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "pathml" agent skill from https://github.com/davila7/claude-code-templates/tree/main/cli-tool/components/skills/scientific/pathml into .opencode/skills/pathml/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pathml", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
pathml- GitHub stars
- 32k
- Used in
- 12 other repos
- Token cost
- ~1.9k tokens
- SKILL.md length
- 698 words
- Files
- 7 (incl. references)
- Skills in repo
- 477
- Repo updated
- First seen
- Licence
- MIT
At a glance
Computational pathology toolkit for analyzing whole-slide images (WSI) and multiparametric imaging data.
- Works in 6 steps: Image Loading & Formats → Preprocessing Pipelines → Graph Construction → …
- Working with histopathology slides
- SKILL.md covers Overview, When to Use This Skill, Core Capabilities and Quick Start, plus 2 more sections
- Calls uv
What it does
Pathml is an agent skill from davila7/claude-code-templates. 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, or training ML models on pathology data. Supports 160+ slide formats including Aperio SVS, NDPI, DICOM, OME-TIFF for digital pathology workflows.
Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including reference files (for example `references/data_management.md`, `references/graphs.md` and `references/image_loading.md`).
It sits in Documents & Office, covering Slides and decks, Machine learning and Bioinformatics. The repository describes itself as: CLI tool for configuring and monitoring Claude Code. The licence is MIT.
When your agent uses it
- Working with histopathology slides
- H&E stained images
- Multiplex immunofluorescence (CODEX
- Spatial proteomics
Example prompts
- “/pathml”
Requirements
- Python 3
Workflow steps
6 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 4c82aba. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
uv
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use uv, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Pathml loads about 1.9k tokens when it runs, and up to ~28k if it reads all its reference files. Until then it costs about 115 tokens; SKILL.md has 698 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from davila7/claude-code-templates at commit 4c82aba, republished under its MIT licence (© davila7). 698 words, ~1,858 tokens.
Download SKILL.mdSave it as
.claude/skills/pathml/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.- 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, or training ML models on pathology data. Supports 160+ slide formats including Aperio SVS, NDPI, DICOM, OME-TIFF for digital pathology workflows.
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 deep learning models, and analyzing multiparametric imaging data from technologies like CODEX and multiplex immunofluorescence.
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 normalizationTissueDetectionHE,NucleusDetectionHE- Tissue/nucleus segmentationMedianBlur,GaussianBlur- Noise reductionLabelArtifactTileHE- Quality control for artifacts
See: references/preprocessing.md for complete transform catalog, pipeline construction, and preprocessing workflows.
3. Graph Construction
Construct spatial graphs representing cellular and tissue-level relationships. Extract features from segmented objects to create graph-based representations suitable for graph neural networks and spatial analysis.
See: references/graphs.md for graph construction methods, feature extraction, and spatial analysis workflows.
4. Machine Learning
Train and deploy deep learning models for nucleus detection, segmentation, and classification. PathML integrates PyTorch with pre-built models (HoVer-Net, HACTNet), custom DataLoaders, and ONNX support for inference.
Key models:
- HoVer-Net - Simultaneous nucleus segmentation and classification
- HACTNet - Hierarchical cell-type classification
See: references/machine_learning.md for model training, evaluation, inference workflows, and working with public datasets.
5. Multiparametric Imaging
Analyze spatial proteomics and gene expression data from CODEX, Vectra, MERFISH, and other multiplex imaging platforms. PathML provides specialized slide classes and transforms for processing multiparametric data, cell segmentation with Mesmer, and quantification workflows.
See: references/multiparametric.md for CODEX/Vectra workflows, cell segmentation, marker quantification, and integration with AnnData.
Show full SKILL.md (288 more words)Show less
6. Data Management
Efficiently store and manage large pathology datasets using HDF5 format. PathML handles tiles, masks, metadata, and extracted features in unified storage structures optimized for machine learning workflows.
See: references/data_management.md for HDF5 integration, tile management, dataset organization, and batch processing strategies.
Quick Start
Installation
# Install PathML
uv pip install pathml
# With optional dependencies for all features
uv pip install pathml[all]Basic Workflow Example
from pathml.core import SlideData
from pathml.preprocessing import Pipeline, StainNormalizationHE, TissueDetectionHE
# Load a whole-slide image
wsi = SlideData.from_slide("path/to/slide.svs")
# Create preprocessing pipeline
pipeline = Pipeline([
TissueDetectionHE(),
StainNormalizationHE(target='normalize', stain_estimation_method='macenko')
])
# Run pipeline
pipeline.run(wsi)
# Access processed tiles
for tile in wsi.tiles:
processed_image = tile.image
tissue_mask = tile.masks['tissue']Common Workflows
H&E Image Analysis:
- Load WSI with appropriate slide class
- Apply tissue detection and stain normalization
- Perform nucleus detection or train segmentation models
- Extract features and build spatial graphs
- Conduct downstream analysis
Multiparametric Imaging (CODEX):
- Load CODEX slide with
CODEXSlide - Collapse multi-run channel data
- Segment cells using Mesmer model
- Quantify marker expression
- Export to AnnData for single-cell analysis
Training ML Models:
- Prepare dataset with public pathology data
- Create PyTorch DataLoader with PathML datasets
- Train HoVer-Net or custom models
- Evaluate on held-out test sets
- Deploy with ONNX for inference
References to Detailed Documentation
When working on specific tasks, refer to the appropriate reference file for comprehensive information:
- Loading images:
references/image_loading.md - Preprocessing workflows:
references/preprocessing.md - Spatial analysis:
references/graphs.md - Model training:
references/machine_learning.md - CODEX/multiplex IF:
references/multiparametric.md - Data storage:
references/data_management.md
Resources
This skill includes comprehensive reference documentation organized by capability area. Each reference file contains detailed API information, workflow examples, best practices, and troubleshooting guidance for specific PathML functionality.
references/
Documentation files providing in-depth coverage of PathML capabilities:
image_loading.md- Whole-slide image formats, loading strategies, slide classespreprocessing.md- Complete transform catalog, pipeline construction, preprocessing workflowsgraphs.md- Graph construction methods, feature extraction, spatial analysismachine_learning.md- Model architectures, training workflows, evaluation, inferencemultiparametric.md- CODEX, Vectra, multiplex IF analysis, cell segmentation, quantificationdata_management.md- HDF5 storage, tile management, batch processing, dataset organization
Load these references as needed when working on specific computational pathology tasks.
© davila7, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 6 other files (references) in cli-tool/components/skills/scientific/pathml of davila7/claude-code-templates.
- SKILL.md
- references/data_management.md
- references/graphs.md
- references/image_loading.md
- references/machine_learning.md
- references/multiparametric.md
- references/preprocessing.md
Open the folder on GitHubat commit 4c82aba
Used in 12 other repositories
We found 39 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 12 other GitHub owners. This page covers the copy in davila7/claude-code-templates, which our catalogue first saw on October 7, 2026.
- jimmc414/Kosmoskosmos-claude-scientific-skills/scientific-skills/pathml
- Microck/ordinary-claude-skillsskills_all/claude-scientific-skills/scientific-skills/pathmlskills_categorized/scientific-computing/claude-scientific-skills/scientific-skills/pathml
- aiskillstore/marketplaceskills/davila7/pathmlskills/k-dense-ai/pathml
- majiayu000/claude-skill-registryskills/ai-ml/pathmlskills/ai-ml/sci-pathml+24 more
- lamm-mit/scienceclawskills/pathml
- agent-skills-hub/agent-skills-hubskills/pathml
- foryourhealth111-pixel/Vibe-Skillsbundled/skills/pathml
- LeonChaoX/qinyan-academic-skillsskills/07-临床医学与精准医疗/pathml
- wu-yc/LabClawskills/bio/pathml
- FreedomIntelligence/OpenClaw-Medical-Skillsskills/pathml
- BioTender-max/awesome-bio-agent-skillsskills/kdense/pathml
- infometa/workbuddyskillsplugins/codebuddy-plugins-official/external_plugins/scientific-skills/pathml
Compare with similar skills
Pathml next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Pathml this skilldavila7/claude-code-templates | 32k | 12 repos | ~1.9k | Automated safety check: Pass | MIT | |
| PyhealthK-Dense-AI/scientific-agent-skills | 48k | 1 repos | ~2.1k | Automated safety check: Pass | MIT | |
| Bio Spatial Transcriptomics Spatial MultiomicsFreedomIntelligence/OpenClaw-Medical-Skills | 3.1k | 1 repos | ~1.6k | Automated safety check: Pass | None | |
| Bio Clip Seq M6a ClipGPTomics/bioSkills | 1.2k | 2 repos | ~5.7k | Automated safety check: Pass | MIT | |
| External Model Validationaipoch/medical-research-skills | 2k | — | ~3.2k | Automated safety check: Pass | MIT | |
| Bio Imaging Mass Cytometry Data PreprocessingGPTomics/bioSkills | 1.2k | 1 repos | ~4.2k | Automated safety check: Pass | MIT |
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Related
Questions about Pathml
What does Pathml do?
Computational pathology toolkit for analyzing whole-slide images (WSI) and multiparametric imaging data. Pathml is an agent skill from davila7/claude-code-templates. Computational pathology toolkit for analyzing whole-slide images (WSI) and multiparametric imaging data.
When should I use Pathml?
Pathml fits situations like: working with histopathology slides; H&E stained images; multiplex immunofluorescence (CODEX; spatial proteomics.
How do I install Pathml in Claude Code?
Run `npx skills add davila7/claude-code-templates --skill pathml -a claude-code`. Or copy the skill folder (cli-tool/components/skills/scientific/pathml in davila7/claude-code-templates) into .claude/skills/pathml in your project. Claude Code loads it when a task matches its description.
How do I install Pathml in Codex?
Run `npx skills add davila7/claude-code-templates --skill pathml -a codex`. Or copy the skill folder (cli-tool/components/skills/scientific/pathml in davila7/claude-code-templates) into .agents/skills/pathml in your project. Codex loads it when a task matches its description.
Can I use Pathml in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add davila7/claude-code-templates --skill pathml -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/pathml, .gemini/skills/pathml, .github/skills/pathml and .opencode/skills/pathml in your project.
What does Pathml need to run?
Going by SKILL.md and its folder, Pathml needs the command-line tools its instructions call (uv). Our summary lists: Python 3.
Does Pathml access the network?
SKILL.md contains no URLs. Its commands use uv, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Is Pathml safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Pathml use?
Pathml is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Pathml use?
About 1.9k tokens (SKILL.md is roughly 7.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 26k tokens, read only when the agent opens those files.
What are the alternatives to Pathml?
Skills that share tags, products or a category with Pathml: Pyhealth (K-Dense-AI/scientific-agent-skills, 48k stars), Bio Spatial Transcriptomics Spatial Multiomics (FreedomIntelligence/OpenClaw-Medical-Skills, 3.1k stars), Bio Clip Seq M6a Clip (GPTomics/bioSkills, 1.2k stars) and External Model Validation (aipoch/medical-research-skills, 2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Pathml?
davila7 (a GitHub user) maintains it in davila7/claude-code-templates, which has 32,432 GitHub stars. The repository holds 477 skills in this directory. The repository was last updated on October 7, 2026.
Source: davila7/claude-code-templates on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.