How do I install the Mlnet skill?

Run npx skillmds@latest add managedcode/mlnet in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Mlnet safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Mlnet?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Mlnet free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

managedcode (@managedcode) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Mlnet by managedcode — Agent Skill

Name: Mlnet
Author: managedcode

Mlnet

Use ML.NET to train, evaluate, or integrate machine-learning models into .NET applications with realistic data preparation, inference, and deployment expectations. USE FOR: ML.NET integration; local model training or retraining; inference pipelines, model loading, evaluation, and deployment review. DO NOT USE FOR: unrelated stacks; generic tasks that do not need this specific guidance. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.

managedcode c9d2804 4 files · 49.6 KB Updated Sep 17, 2026

File contents

ML.NET

Trigger On

integrating machine learning into a .NET application
training or retraining ML.NET models from local data
reviewing inference pipelines, model loading, or AutoML-generated code

Workflow

Start from the prediction task and data quality, not the algorithm or package list.
Separate training code from inference code so the production path stays lean and predictable.
Review feature engineering, normalization, label quality, and evaluation metrics before trusting model output.
Use Model Builder or the ML.NET CLI when they speed up exploration, but inspect the generated C# before treating it as production architecture.
Plan how the model is loaded, versioned, and refreshed in the application lifecycle.
Validate with representative datasets and explicit evaluation, not only with a sample that happens to run.

Deliver

ML.NET pipelines that fit the prediction task
production-usable inference integration
evaluation evidence tied to the business scenario

Validate

model quality is measured, not assumed
training and inference responsibilities are separated
deployment and versioning expectations are explicit

References

patterns.md - Data loading, training pipelines, evaluation metrics, deployment strategies, and feature engineering patterns
examples.md - Complete examples for sentiment analysis, price prediction, image classification, anomaly detection, recommendations, clustering, fraud detection, text classification, object detection, and AutoML

---name: mlnetdescription: Use ML.NET to train, evaluate, or integrate machine-learning models into .NET applications with realistic data preparation, inference, and deployment expectations. USE FOR: ML.NET integration; local model training or retraining; inference pipelines, model loading, evaluation, and deployment review. DO NOT USE FOR: unrelated stacks; generic tasks that do not need this specific guidance. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.---# ML.NET## Trigger On- integrating machine learning into a .NET application- training or retraining ML.NET models from local data- reviewing inference pipelines, model loading, or AutoML-generated code## Workflow1. Start from the prediction task and data quality, not the algorithm or package list.2. Separate training code from inference code so the production path stays lean and predictable.3. Review feature engineering, normalization, label quality, and evaluation metrics before trusting model output.4. Use Model Builder or the ML.NET CLI when they speed up exploration, but inspect the generated C# before treating it as production architecture.5. Plan how the model is loaded, versioned, and refreshed in the application lifecycle.6. Validate with representative datasets and explicit evaluation, not only with a sample that happens to run.## Deliver- ML.NET pipelines that fit the prediction task- production-usable inference integration- evaluation evidence tied to the business scenario## Validate- model quality is measured, not assumed- training and inference responsibilities are separated- deployment and versioning expectations are explicit## References- [patterns.md](references/patterns.md) - Data loading, training pipelines, evaluation metrics, deployment strategies, and feature engineering patterns- [examples.md](references/examples.md) - Complete examples for sentiment analysis, price prediction, image classification, anomaly detection, recommendations, clustering, fraud detection, text classification, object detection, and AutoML

Frequently asked questions