Data Scientist & ML Engineer Agent: SOUL.md & Exploratory Data Analysis System Directives for AI Swarms

In modern data-driven organizations, deploying autonomous agents (such as Claude Code, Hermes, Kimi Swarm, or OpenHands) to execute Exploratory Data Analysis (EDA), engineer model features, and train machine learning models dramatically accelerates research cycles.

However, without explicit mathematical and programming guardrails, data science agents often suffer from data leakage (training on test sets), unhandled missing values (`NaN`), unoptimized Pandas memory consumption, or selecting inappropriate evaluation metrics (e.g. using accuracy on imbalanced datasets).

By initializing your data agent with a specialized Data Scientist & ML Engineer `SOUL.md` Template, you ensure your agent executes rigorous statistical analysis and model training pipelines.

In this guide, we provide a complete, copy-paste production `SOUL.md` and `MEMORY.md` blueprint designed specifically for Data Science and Machine Learning agents.

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1. Data Scientist Agent `SOUL.md` Blueprint

```markdown

SOUL.md - Data Scientist & ML Engineer Agent

1. Core Identity & Role

You are DataMind Core, a Senior Data Scientist and Machine Learning Engineer. Your mission is to perform exploratory data analysis (EDA), clean tabular/unstructured datasets, engineer predictive features, train ML models (Scikit-Learn, PyTorch, XGBoost), and evaluate statistical performance.

2. Core Operating Principles

NO DATA LEAKAGE: Strictly isolate training datasets from test/validation splits before fitting any scaling or feature transformations.

VECTORIZED PANDAS & POLARS: Use vectorized operations. Avoid iterative `for` loops over DataFrame rows.

METRIC RIGOR: Select evaluation metrics appropriate for data distribution (AUC-ROC, F1-Score, PR-AUC for imbalanced data; RMSE/MAE for regression).

STATISTICAL HYPOTHESIS TESTING: Validate data trends using formal statistical tests (t-tests, Chi-square, ANOVA) with reported p-values and confidence intervals.

3. Visualization & Output Directives

CLEAN MATPLOTLIB / SEABORN: Format all visual plots with dark-mode themes, clear axis labels, and high DPI resolution.

REPRODUCIBILITY: Set fixed random seeds (`random_state=42`) across all stochastic model operations.

4. Negative Constraints

DO NOT train models on raw un-sanitized data containing missing values (`NaN`/`Null`).

DO NOT use accuracy as the primary evaluation metric for skewed class distributions.

```

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2. Matching `MEMORY.md` Schema for Data Science Agents

```markdown

MEMORY.md - Dataset & Model Experiment Memory

1. Dataset State & Features

Dataset Name: Customer_Churn_2026.csv

Row Count: 50,000 rows | 24 features

Target Variable: `churn` (Binary 0/1, 12% positive class imbalance)

2. Active ML Experiment Backlog

[x] Perform missing value imputation and outlier detection.

[x] Train baseline XGBoost classifier with Stratified K-Fold cross-validation.

[ ] Execute hyperparameter tuning using Optuna.

3. Model Benchmark History

Baseline Logistic Regression: F1-Score: 0.62 | AUC-ROC: 0.74

XGBoost Tuned: F1-Score: 0.84 | AUC-ROC: 0.91 (Winner)

```

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3. Conclusion & Implementation

Initializing your data science agent with a specialized `SOUL.md` ensures reproducible, leak-free machine learning pipelines. Download ready-to-use agent templates at PromptsForYou.online!