ondevice-ml

Run Core ML inference on-device for NLP, Vision, and SoundAnalysis tasks.

1|Updated Aug 19, 2025

Search Tags:#mobile #nlp #vision #privacy #on-device #machine-learning #coreml

Ready to run

npx skills add https://github.com/Maduixa/Glasstic --skill ondevice-ml

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ondevice-ml
Source: https://github.com/Maduixa/Glasstic/tree/main/.claude/skills/ondevice-ml
Command: npx skills add https://github.com/Maduixa/Glasstic --skill ondevice-ml

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Privacy-preserving on-device ML pipelines enable inference without sending user data to external services by leveraging Core ML for NLP, Vision, and SoundAnalysis tasks, reducing data exposure and regulatory risk.

Core Features & Use Cases

On-device Core ML integration for NLP, Vision, and SoundAnalysis pipelines.
Privacy and compliance guardrails to govern data handling and offline operation.
Deployment-ready structure with Load/Output guidance and Swift/SwiftPM compatibility.

Quick Start

Run a sample on-device Core ML pipeline to validate NLP, Vision, and SoundAnalysis workflows offline.

Frequently Asked Questions about ondevice-ml

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I run machine learning inference entirely on-device for privacy compliance?▼

To run machine learning inference entirely on-device for privacy compliance, use Core ML integration to execute NLP, Vision, and SoundAnalysis pipelines offline. This ensures data never leaves the device, reducing data exposure and regulatory risk.

What's the best way to handle offline NLP and Vision tasks in mobile apps?▼

The best way to handle offline NLP and Vision tasks in mobile apps is deploying on-device Core ML pipelines. These pipelines process inference locally, ensuring strict data governance and operation without external service connectivity.

Can I use Core ML for SoundAnalysis tasks while maintaining strict data governance?▼

Yes, you can use Core ML for SoundAnalysis tasks while maintaining strict data governance. The Skill applies privacy guardrails to on-device pipelines, ensuring audio data processing remains completely local and never leaves the device.

Does on-device machine learning require external server connections to process user data?▼

On-device machine learning does not require external server connections. By leveraging Core ML integration, NLP, Vision, and SoundAnalysis workflows execute locally with privacy guardrails, ensuring user data never leaves the device.

How do I set up Swift and SwiftPM compatibility for on-device Core ML pipelines?▼

To set up Swift and SwiftPM compatibility for on-device Core ML pipelines, follow the deployment-ready structure with Load and Output guidance. This structure validates NLP, Vision, and SoundAnalysis workflows offline.

When do I need privacy guardrails for on-device machine learning pipelines?▼

You need privacy guardrails for on-device machine learning pipelines when mobile apps require offline operation and strict data governance. These guardrails govern data handling during local Core ML inference to prevent external data exposure.