# ml-expert

Guides agents through training machine learning models, writing PyTorch loops, and deploying FastAPI services. Triggers on ML, data science, and MLOps tasks.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 365 installs · ★ 44
- Author: personamanagmentlayer
- License: Apache-2.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/personamanagmentlayer/pcl/tree/HEAD/stdlib/ai/ml-expert/SKILL.md
- Updated: 2026-10-12
- HTML: https://affpapa.org/en/nevibecode/skills/ml-expert

## What it does

The skill instructs the agent on supervised learning, neural network architectures, and model serving. Inside are implementation patterns for data scaling, feature engineering, and cross-validation using scikit-learn. Code templates cover practical tabular workflows. For deep learning tasks, it provides PyTorch network definitions with dropout layers alongside complete training loops.

MLOps patterns are included as well. The skill details model persistence using `joblib`, experiment tracking concepts, and a minimal FastAPI server that loads saved weights to serve predictions. In its frontmatter metadata, it declares access to `Read`, `Write`, `Edit`, and `Bash(python:*)`.

Terminal execution is strictly constrained to `python:*` commands. That means the agent cannot run random system utilities. Keep in mind that executing these scripts requires `torch`, `scikit-learn`, and `fastapi` in your Python environment, or the runs will fail on missing modules.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add personamanagmentlayer/pcl --skill ml-expert -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/ml-expert/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/ml-expert/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add personamanagmentlayer/pcl --skill ml-expert -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/ml-expert/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add personamanagmentlayer/pcl --skill ml-expert -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/ml-expert/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install personamanagmentlayer/pcl ml-expert
```

2. **Manually: put the files in place**
- File: `.github/skills/ml-expert/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add personamanagmentlayer/pcl --skill ml-expert -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/personamanagmentlayer/pcl --path stdlib/ai/ml-expert
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/ml-expert/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add personamanagmentlayer/pcl --skill ml-expert -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/personamanagmentlayer/pcl/tree/HEAD/stdlib/ai/ml-expert/SKILL.md

## When to use it
- build a data preprocessing and classification pipeline with scikit-learn
- implement a PyTorch neural network with a custom training loop
- persist trained models and scalers using joblib
- deploy an inference API endpoint with FastAPI

## Example prompts
- Create a scikit-learn pipeline with standard scaling and random forest cross-validation.
- Write a PyTorch neural network class with dropout and an epoch training loop.
- Build a FastAPI prediction service that loads weights from a joblib file.

## Source text · SKILL.md · Apache-2.0

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 4a5108142e272fee.

BEGIN THIRD-PARTY FILE 4a5108142e272fee
````text
---
name: ml-expert
version: 1.1.0
description: >-
  Expert-level machine learning, deep learning, model training, and MLOps. Use when the
  user mentions machine learning, deep learning, neural networks, MLOps, or data science,
  or when the task involves Machine Learning Fundamentals, Data Preparation, or Model
  Training.
category: ai
tags: [machine-learning, deep-learning, neural-networks, mlops, data-science]
allowed-tools:
  - Read
  - Write
  - Edit
  - Bash(python:*)
---

# Machine Learning Expert

Expert guidance for machine learning systems, deep learning, model training, deployment, and MLOps practices.

## Core Concepts

### Machine Learning Fundamentals

- Supervised learning (classification, regression)
- Unsupervised learning (clustering, dimensionality reduction)
- Reinforcement learning
- Feature engineering
- Model evaluation and validation
- Hyperparameter tuning

### Deep Learning

- Neural networks (CNNs, RNNs, Transformers)
- Transfer learning
- Fine-tuning pre-trained models
- Attention mechanisms
- GANs (Generative Adversarial Networks)
- Autoencoders

### MLOps

- Model versioning and tracking
- Experiment management
- Model deployment and serving
- Monitoring and retraining
- CI/CD for ML pipelines
- A/B testing for models

## Supervised Learning

```python
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, confusion_matrix
import joblib

class MLPipeline:
    def __init__(self):
        self.scaler = StandardScaler()
        self.model = None
        self.feature_names = None

    def prepare_data(self, X: pd.DataFrame, y: pd.Series, test_size: float = 0.2):
        """Split and scale data"""
        X_train, X_test, y_train, y_test = train_test_split(
            X, y, test_size=test_size, random_state=42, stratify=y
        )

        # Scale features
        X_train_scaled = self.scaler.fit_transform(X_train)
        X_test_scaled = self.scaler.transform(X_test)

        self.feature_names = X.columns.tolist()

        return X_train_scaled, X_test_scaled, y_train, y_test

    def train_classifier(self, X_train, y_train, n_estimators: int = 100):
        """Train random forest classifier"""
        self.model = RandomForestClassifier(
            n_estimators=n_estimators,
            max_depth=10,
            random_state=42,
            n_jobs=-1
        )

        self.model.fit(X_train, y_train)

        # Cross-validation
        cv_scores = cross_val_score(self.model, X_train, y_train, cv=5)

        return {
            "cv_mean": cv_scores.mean(),
            "cv_std": cv_scores.std(),
````
END THIRD-PARTY FILE 4a5108142e272fee

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