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name: agentomics-ml description: An autonomous agentic system for supervised machine learning model development, specifically tailored for biomedical data. keywords:

• agentomics
• automl
• biomedical-ml
• supervised-learning
• autonomous-agents measurable_outcome: Autonomously train, evaluate, and validate a supervised machine learning model on a biomedical dataset (e.g., omics data) with a performance report in under 1 hour. license: MIT metadata: author: BioGeMT source: "https://github.com/BioGeMT/agentomics-ml" version: "2026.04" compatibility:
• system: Python 3.9+ allowed-tools:
• run_shell_command
• python_repl
• read_file

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Agentomics ML

An autonomous agentic system built specifically for the unique challenges of biomedical machine learning. Traditional AutoML systems often struggle with domain-specific complexities like high-dimensional omics data, class imbalances, and complex feature relationships. Agentomics addresses this by leveraging LLM-driven agents to guide the ML lifecycle.

When to Use This Skill

• You need to develop predictive models on structured biomedical data (transcriptomics, proteomics, clinical tables).
• You want an autonomous system to handle data preprocessing, feature selection, model tuning, and evaluation.
• You require interpretable machine learning pipelines that are tailored to the nuances of biological datasets.

Core Capabilities

• Domain-Aware Preprocessing: Handles missing values, scaling, and feature encoding with an understanding of biological data types.
• Intelligent Model Selection: Agents reason about the dataset characteristics to select and tune the most appropriate algorithms (e.g., Random Forests, Gradient Boosting, SVMs).
• Automated Evaluation: Generates comprehensive performance reports including cross-validation metrics and feature importance.

Example Workflow

1. Provide a labeled biomedical dataset (e.g., a CSV of gene expression data for cancer vs. normal samples).
2. Invoke

   agentomics-ml

   with the dataset and target variable.
3. The multi-agent system autonomously explores data processing strategies, trains multiple models, and optimizes hyperparameters.
4. Review the final generated model and performance report.

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