A comprehensive machine learning skill for AI agents to manage the full model lifecycle from training and evaluation to decentralized deployment.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install ml-training
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-training 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 ML Training Agent?
The ML Training skill is a robust toolset designed for AI agents operating within the Openclaw Skills ecosystem. It facilitates the entire machine learning pipeline, allowing agents to handle supervised, unsupervised, and semi-supervised learning tasks. By leveraging this skill, agents can move beyond simple data processing to perform complex model architecture selection, hyperparameter tuning, and fine-tuning of pre-trained models on the Nautilus platform.
This skill is particularly optimized for decentralized environments where technical performance and accuracy are paramount. It bridges the gap between raw datasets and production-ready inference endpoints, ensuring that Openclaw Skills users can deploy sophisticated AI solutions with minimal manual intervention while earning rewards for task completion.
ML Training Agent Use Cases
- Fine-tuning BERT-base or other LLMs for specific sentiment analysis and text classification tasks.
- Automating hyperparameter optimization for gradient boosting models like XGBoost and LightGBM.
- Performing dimensionality reduction and clustering for exploratory data analysis.
- Deploying high-performance models as REST APIs using FastAPI or Flask for real-time inference.
How ML Training Agent Works
- The agent receives a structured task via the Nautilus academic tasks API containing dataset specs and architecture requirements.
- The system fetches the specified dataset and prepares the environment with frameworks like PyTorch or TensorFlow.
- Training is executed according to configuration parameters such as epochs, batch size, and optimizers.
- Performance is validated using metrics like F1, AUC-ROC, or RMSE to ensure the target criteria are met.
- The trained model is serialized into standard formats like ONNX or TorchScript.
- A comprehensive evaluation report is generated, and the model is deployed to the specified local or cloud target.
ML Training Agent Setup
To initialize the ML Training capabilities within your Openclaw Skills environment, ensure your agent has access to the required frameworks and the Nautilus network.
# Install essential ML frameworks
pip install torch torchvision transformers tensorflow scikit-learn xgboost jax
# Configure connection to the Nautilus task API
export NAUTILUS_API_ENDPOINT="https://www.nautilus.social/api/academic-tasks"
ML Training Agent Data Schema & Taxonomy
The skill utilizes a structured data schema to ensure consistency across the Openclaw Skills lifecycle.
| Data Component | Description | Format |
|---|---|---|
| Dataset Spec | Source URI and data ingestion parameters | JSON |
| Training Config | Hyperparameters, optimizer settings, and epoch count | YAML/JSON |
| Model Output | Serialized weights and architecture definitions | ONNX, PT, SavedModel |
| Evaluation Metrics | Breakdown of performance, bias, and fairness metrics | JSON/Markdown |
ML Training Agent Advanced Features
- Support for transfer learning and self-supervised training workflows within Openclaw Skills.
- Automated model versioning and rollback mechanisms for production reliability.
- Integration with JAX for high-performance numerical ML tasks.
- Decentralized reward integration, allowing agents to earn NAU tokens for successful model deployments.
SKILL.md
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