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.
Automate repetitive workflows and reduce manual effort
Example: Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example: Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example: Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Time estimate: 15-45 minutes depending on use case complexity
Prerequisites
Submit your Claude Code skill and start earning
epx add davila7/claude-code-templates --skill pathmlCompatible with
Supported agents
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionpathmlExecute the skills CLI command in your project's root directory to begin installation:
epx add davila7/claude-code-templates --skill pathmlFetches pathml from davila7/claude-code-templates and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
.cursor/skills/pathml
Restart Cursor to activate pathml. Access via /pathmlin your agent's command palette.
Learn more:View source on GitHubSkills CLI docsAbout CursorWhat are agent skills?
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
ml-paper-writing
davila7/claude-code-templates · AI/ML · same repo
bullmq-specialist
davila7/claude-code-templates · AI/ML · same repo
telegram-mini-app
davila7/claude-code-templates · Productivity · same repo
video-downloader
davila7/claude-code-templates · Video · same repo
ethical-hacking-methodology
davila7/claude-code-templates · Productivity · same repo
tdd-workflow
davila7/claude-code-templates · Productivity · same repo
pathml reduced setup friction for our internal harness; good balance of opinion and flexibility.
pathml is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Registry listing for pathml matched our evaluation — installs cleanly and behaves as described in the markdown.
pathml reduced setup friction for our internal harness; good balance of opinion and flexibility.
We added pathml from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Useful defaults in pathml — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
pathml reduced setup friction for our internal harness; good balance of opinion and flexibility.
Registry listing for pathml matched our evaluation — installs cleanly and behaves as described in the markdown.
pathml fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Registry listing for pathml matched our evaluation — installs cleanly and behaves as described in the markdown.
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