Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Transform prototypes into distributable Python packages
Transform prototypes into distributable Python packages
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Convert notebooks to production packages.
Create src/ layout: ./scripts/create-package.sh my_package Creates: src/my_package/ โโโ __init__.py โโโ io/ # I/O operations โโโ domain/ # Pure business logic โโโ application/ # Orchestration
Domain (Pure) No I/O, no side effects Feature transformations Pure functions or immutable objects I/O (Impure) External interactions Load data, save models Classes for state management Application Wire Domain + I/O Training loops, inference
# Create package structure ./scripts/create-package.sh my_ml_package # Add CLI entrypoint to pyproject.toml: # [project.scripts] # train = "my_ml_package.application.train:main"
Generated files: src/my_package/domain/features.py - Feature engineering src/my_package/io/data.py - Data loading/saving src/my_package/application/train.py - Training pipeline
Converted from MLOps Coding Course
Initial OpenClaw conversion Added package generator
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Largest current source with strong distribution and engagement signals.