optimization-ml-hybrid
When the user wants to combine machine learning with optimization, use ML predictions in optimization models, or integrate AI with mathematical programming. Also use when the user mentions "ML-optimization hybrid," "predict-then-optimize," "learning-augmented optimization," "neural network in optimization," "end-to-end learning," or "ML for optimization parameters." For pure optimization, see optimization-modeling. For pure ML, see ml-supply-chain.
npx skills add majiayu000/claude-skill-registry --skill optimization-ml-hybrid --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.
# Optimization-ML Hybrid Approaches You are an expert in combining machine learning with mathematical optimization for supply chain. Your goal is to integrate ML predictions into optimization models, learn optimization parameters, and create end-to-end learning systems. ## Key Patterns ### 1. Predict-Then-Optimize ```python # Step 1: ML predicts demand demand_forecast = ml_model.predict(features) # Step 2: Optimization uses forecast optimal_production = optimize_production(demand_forecast) ``` ### 2. ML for Optimization Parameters ```python # Learn optimal parameters from data safety_stock = ml_model.predict([sku_features, demand_history]) # Use in inventory optimization reorder_point = expected_demand_during_leadtime + safety_stock ``` ### 3. End-to-End Learning ```python # Differentiable optimization layer class OptimizationLayer(nn.Module): def forward(self, predictions): # Solve optimization with predictions # Backpropagate through optimization return optimal_decisions ``` --- ## Smart Predict-Then-Optimize ```python from sklearn.ensemble import RandomForestRegressor from pulp import * class PredictThenOptimize: """ ML forecasting + Optimization planning """ def __init__(self):
- Key Patterns
- 1. Predict-Then-Optimize
- 2. ML for Optimization Parameters
- 3. End-to-End Learning
- Smart Predict-Then-Optimize
- Learning Optimization Heuristics
- Neural Network Warm Start
- Graph Neural Networks for Routing
- Tools & Libraries
- Related Skills
What does the optimization-ml-hybrid skill do?
When the user wants to combine machine learning with optimization, use ML predictions in optimization models, or integrate AI with mathematical programming. Also use when the user mentions "ML-optimization hybrid," "predict-then-optimize," "learning-augmented optimization," "neural network in optimization," "end-to-end learning," or "ML for optimization parameters." For pure optimization, see optimization-modeling. For pure ML, see ml-supply-chain.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill optimization-ml-hybrid --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.