Skill ML.NET

Create machine learning models that run inside FrameworX using Script Classes with ML.NET. The AI writes the C# code, connects it to live tags, creates output tags for predictions, and configures model persistence — all within the FrameworX scripting engine.

Guiding Principle: Always Build Complete

Always generate the full production-ready implementation. Every ML Script Class includes model persistence (SaveModel), startup reload (LoadModel), and the ServerStartup wiring — no stripped-down versions.

One model per interaction. Always create exactly one Script Class ML model per session, targeting one sensor or one prediction goal. Do NOT create multiple ML classes unprompted — even if the solution has many tags. If the user wants additional models, they will ask in follow-up.

What This Skill Does

Build ML.NET models as FrameworX Script Classes. The AI generates the full C# ML pipeline (data classes, training, prediction, tag integration) based on the user's requirements. Models run server-side, read from input tags, and write predictions to output tags.

Input Tags -> Script Class (ML.NET) -> Output Tags
   |              |                         |
 Live data    Train / Predict           Predictions, scores,
 from UNS     Model persisted to        anomaly flags, forecasts
              solution folder            Alarms / Dashboard

When to Use This Skill

Use when:

• The user wants to add machine learning to a FrameworX solution
• The user mentions "ML", "prediction", "anomaly detection", "forecasting", "regression", "classification", or "ML.NET"
• The user mentions industrial goals like "predictive maintenance", "quality control", "fault prediction", "demand forecasting", or "energy prediction"
• The user wants to run ML models on-premise inside FrameworX scripts
• The user asks "how do I use ML.NET in FrameworX" or "how do I create an ML script"

Do NOT use when:

• The user wants cloud-based AI or LLM integration (not ML.NET)
• The user only needs simple threshold alarms (no ML needed — use Alarms module)
• The user wants to use Python for ML (ML.NET is C# only; Python ML libraries are available for FrameworX, but not covered here)

Prerequisites

• Solution open with input tags already created (the tags the model will read from)
• Tags should have live or simulated data flowing (Value Simulator or real device)
• If starting from scratch, apply the New Solution skill first to create tags and a data source

MCP Tools and Tables

Step 0: Identify the ML Task

Before writing any code, the AI must always ask the user the following questions — no exceptions, regardless of how much context is available. Do not silently choose for the user.

Mandatory questions — always ask all three

1. Which ML algorithm do you want to use?

• Anomaly Detection — SSA Spike — detects sudden outliers, spikes, or abnormal readings
• Anomaly Detection — SSA ChangePoint — detects gradual drift or regime shifts
• Time-Series Forecasting — SSA — predicts future values from historical patterns
• Regression — FastTree — predicts a continuous value from multiple inputs
• Binary Classification — FastTree — predicts yes/no outcomes from multiple inputs

Not sure which to pick? Describe what you want to achieve and I'll recommend the best fit.

After Q1 is answered, adapt Q2 and Q3 based on the chosen algorithm:

Anomaly Detection — SSA Spike or ChangePoint:

2. Which single tag member should be monitored for anomalies?
(e.g., OilGas_Co/WestTexas_Field/WellPad_A/Well_A01.TubingPressure — full path + member name)

3. The output will be AnomalyScore, IsAnomaly, and LastPrediction tags under <AssetPath>/ML/. Confirm the asset path prefix, or suggest a different output folder.

Time-Series Forecasting — SSA:

2. Which single tag member should be forecast?
(e.g., OilGas_Co/.../Tank_01.Level — full path + member name)

3. How many steps ahead should the forecast horizon be? What does the value represent?

Regression — FastTree:

2. Which 2–5 feature tags are the inputs, and which tag is the label (the value to predict)?

3. What does the predicted value represent (unit/context)?

Binary Classification — FastTree:

2. Which 2–5 feature tags are the inputs, and which tag is the boolean label?

3. What does the yes/no outcome represent?

Do not proceed past Step 0 until all three questions are answered.

Goal-to-algorithm mapping (use as suggestions only — always confirm with user)

Required information before proceeding

After Step 0: Load the Specific Sub-Skill

Once the algorithm is chosen, load and follow the corresponding sub-skill for complete implementation steps (output tags, Script Class code, trigger, verification, pitfalls):

Each sub-skill is fully self-contained with output tags, complete C# class examples, MCP write commands, trigger configuration, and pitfalls specific to that ML task.

Decision Guide