ml-supply-chain
When the user wants to apply machine learning to supply chain problems, build ML models, or use AI for predictions. Also use when the user mentions "machine learning," "deep learning," "neural networks," "AI supply chain," "predictive models," "classification models," "regression models," "anomaly detection," "computer vision," or "NLP for supply chain." For traditional forecasting, see demand-forecasting. For optimization, see optimization-modeling.
npx skills add majiayu000/claude-skill-registry --skill ml-supply-chain --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Guides an agent to assist with applying machine learning to supply chain problems, including building ML models and using AI for predictions across forecasting, classification, and decision-making tasks.
How it works
- Starts with an Initial Assessment covering: Business Problem, Data Availability, ML Problem Type, and Technical Environment.
- Presents ML Problem Types in Supply Chain: Supervised Learning (Regression and Classification), Unsupervised Learning (Clustering and Dimensionality Reduction), and Reinforcement Learning for sequential decisions.
- Includes a Demand Forecasting with ML section that provides feature engineering steps (time-based features, lag features, rolling statistics, EWM, trend features, and cyclical encoding) and two modeling approaches: Gradient Boosting for Demand Forecasting (XGBoost/LightGBM) with a train/validate split, training, evaluation, and plotting utilities; and an outlined Deep Learning with LSTM approach that builds an LSTM-based forecaster, including sequence creation, model building, and training with early stopping and learning rate scheduling. Code blocks illustrate example implementations.
- Offers example usage demonstrating how to prepare data, train, evaluate, and visualize results, including feature importance and prediction plots. The skill emphasizes traditional forecasting alignment under demand-forecasting and provides concrete code patterns for feature engineering, gradient-boosting models, and LSTM workflows.
When to use it
Use when the user wants to apply ML to supply chain problems, build ML models, or use AI for predictions. Also applicable when user mentions machine learning, deep learning, neural networks, AI supply chain, predictive models, classification models, regression models, anomaly detection, computer vision, or NLP for supply chain. For traditional forecasting, refer to demand-forecasting.
What it can touch
- Tools: claude-code is declared for use.
- It references code samples in Python (pandas, numpy, xgboost, lightgbm, scikit-learn, tensorflow/keras, matplotlib) and imports typical ML libraries and plotting utilities.
Caveats
- License: MIT.
- Declared scope focuses on modeling, feature engineering, and evaluation; it does not guarantee deployment specifics beyond provided training and plotting utilities.
- The material includes example usage that assumes data availability and quality consistent with the described features and time-series splits.
# Machine Learning for Supply Chain You are an expert in applying machine learning and artificial intelligence to supply chain problems. Your goal is to help build, train, and deploy ML models that improve forecasting, classification, optimization, and decision-making across supply chain operations. ## Initial Assessment Before applying ML to supply chain problems, understand: 1. **Business Problem** - What problem needs solving? (demand forecasting, quality prediction, route optimization) - What decisions will ML model support? - Current approach and its limitations? - Expected improvement and ROI? 2. **Data Availability** - What data is available? (structured, unstructured, images, text) - Historical data quantity? (ML typically needs 1000+ samples) - Data quality? (missing values, outliers, noise) - Feature availability? (predictive variables) - Real-time data access? 3. **ML Problem Type** - Supervised learning? (labeled data available) - Unsupervised learning? (clustering, anomaly detection) - Reinforcement learning? (sequential decision-making) - Time series forecasting? 4. **Technical Environment** - ML expertise in team? - Computational resources? (CPU, GPU, cloud) - Deploy
- Initial Assessment
- ML Problem Types in Supply Chain
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Demand Forecasting with ML
- Feature Engineering for Demand Forecasting
- Gradient Boosting for Demand Forecasting
- Deep Learning with LSTM
- Classification Problems
- Shipment Delay Prediction
- Clustering & Segmentation
- Customer Segmentation
- Computer Vision for Supply Chain
What does the ml-supply-chain skill do?
When the user wants to apply machine learning to supply chain problems, build ML models, or use AI for predictions. Also use when the user mentions "machine learning," "deep learning," "neural networks," "AI supply chain," "predictive models," "classification models," "regression models," "anomaly detection," "computer vision," or "NLP for supply chain." For traditional forecasting, see demand-forecasting. For optimization, see optimization-modeling.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill ml-supply-chain --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.