An automated training pipeline for machine learning models that handles everything from hyperparameter tuning to model export via API.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install ml-model-trainer
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install ml-model-trainer using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Machine Learning Model Trainer?
The Machine Learning Model Trainer is a comprehensive utility designed to simplify the development lifecycle of predictive models. As a key component of the Openclaw Skills library, it allows developers to programmatically train algorithms like Random Forest, XGBoost, and Linear Regression without managing local infrastructure. It bridges the gap between raw data and deployable models by automating the pipeline and offering high-performance outputs.
This skill is particularly valuable for those who need to iterate quickly on data experiments. By leveraging this tool, you can focus on data strategy while the underlying Openclaw Skills infrastructure manages the computational heavy lifting of training and optimization.
Machine Learning Model Trainer Use Cases
- Rapid prototyping of various machine learning models to identify the best performing algorithm.
- Integrating automated training steps into larger software applications via Openclaw Skills.
- Small-scale model experimentation with custom datasets and specific target variables.
- Educational demonstrations of model training lifecycles without complex local setups.
How Machine Learning Model Trainer Works
- Host your dataset at a publicly accessible URL in a standard format like CSV.
- Define your training parameters including the target column and preferred algorithm (e.g., random_forest).
- Trigger the training process using the Openclaw Skills interface or SDK.
- The system processes the dataset and performs automated hyperparameter tuning to optimize performance.
- Upon completion, receive a unique model ID and accuracy metrics, with the option to export the model.
Machine Learning Model Trainer Setup
To get started with this skill, ensure you have your developer environment configured to interact with Openclaw Skills. You will need a valid account integrated with SkillPay.me to handle the 0.001 USDT per call fee.
# Install the necessary client for Openclaw Skills
npm install @openclaw/core-sdk
Ensure your API credentials are set in your environment variables before making the first training request.
Machine Learning Model Trainer Data Schema & Taxonomy
The skill expects a structured JSON input and returns a detailed training report. Metadata is organized as follows:
| Field | Description | Type |
|---|---|---|
| dataset | URL to the raw CSV/JSON data | String |
| algorithm | ML algorithm choice (e.g., xgboost, random_forest) | String |
| target | The column name intended for prediction | String |
| model_id | The unique identifier for the generated model | UUID |
| training_accuracy | The performance metric resulting from the session | String |
Machine Learning Model Trainer Advanced Features
- Automated hyperparameter tuning to ensure optimal model weights without manual intervention.
- Multi-format model export supporting ONNX and PKL for cross-platform production use.
- Seamless payment integration using SkillPay.me for low-friction access to Openclaw Skills.
- Scalable API-driven training sessions suitable for parallel model experimentation.
SKILL.md
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