Core ML, Create ML, Vision framework, Natural Language framework, on-device ML integration.
SkillScore86Maintained
Automatic
◆AI Engineer pack
Last verified 8/17/2026
What it changes
Passed 19 of 22 test cases with the skill, and 16 of 22 without it.
Without skill73%
With skill86%
Checks it passed that the model alone missed
The response names MagnitudePruner
The response recommends Apple Intelligence rather than Core ML
The response references searching for CoreML import statements in the codebase
3 fixed16 passed either way3 still failing0 broke
Pass-rate lift
+14 pp
Tokens
+198%
Agent turns
0%
Response time
-11%
Model gemini-3.6-flashMeasured 8/17/2026v119 of 22 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 3 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 3
Prompt
Which module in coremltools handles target sparsity configuration when removing zero-value weights from PyTorch models prior to Core ML conversion?
What was checked, without and with the skill
Checked
Without skill
With skill
The response names MagnitudePruner
Missed
Passed
case-21 · gemini-3.6-flash · 8/17/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md396 linessingle file
Core ML Skills
Combined advisory, generator, and workflow skill for integrating machine learning into Apple platform apps. Covers Core ML model integration, Vision framework image analysis, NaturalLanguage framework text processing, Create ML training, and on-device model optimization.
When This Skill Activates
Use this skill when the user:
Wants to add ML capabilities to their app
Needs to integrate a Core ML model (.mlmodel) into an Xcode project
Wants to use the Vision framework for image analysis (faces, text recognition, body pose, object detection)
Wants to use the NaturalLanguage framework for text processing (sentiment, entities, language detection)
Needs to train a custom model with Create ML
Wants to optimize a model for on-device use (quantization, pruning, palettization)
Needs to choose between Core ML and Foundation Models (Apple Intelligence)
Asks about image classification, object detection, sound classification, or tabular data prediction
Wants real-time camera + ML processing
Decision Guide: Core ML vs Foundation Models
Before generating code, determine which framework is appropriate.
Use Foundation Models (Apple Intelligence) When:
You need general-purpose text generation, summarization, or conversational AI
Target is iOS 26+ / macOS 26+ (Foundation Models requires Apple Silicon + latest OS)
The task is open-ended language understanding or generation
You want @Generable structured output from natural language
See apple-intelligence/foundation-models/ skill for implementation
Use Core ML When:
You need specialized ML: image classification, object detection, sound classification, custom regression/classification
You have a trained model (.mlmodel, .mlpackage) or plan to train one
You need broad device support (iOS 14+ / macOS 11+)