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Core ML Swift Integration · dpearson2699 | DecimalAI

ai mlby dpearson2699

Core ML Swift Integration

dpearson2699/coreml

Source dpearson2699/swift-ios-skills ↗License NOASSERTION

Integrate Core ML models in iOS apps for on-device machine learning inference. Covers model loading (.mlmodel, .mlpackage, .mlmodelc), predictions with auto-generated classes and MLFeatureProvider, compute unit configuration (CPU, GPU, Neural Engine), MLTensor, VNCoreMLRequest, MLComputePlan, multi-model pipelines, and deployment strategies.

SkillScore82Maintained

Automatic

AI Engineer pack

Last verified 8/14/2026

What it changes

Passed 18 of 22 test cases with the skill, and 10 of 22 without it.

Without skill45%

With skill82%

Checks it passed that the model alone missed

Bridges between MLMultiArray and MLTensor via MLShapedArray.
States that MLModel.compileModel(at:) requires iOS 16 or later.
Recommends CoreMLRequest for running Core ML models with Vision on iOS 18+.

and 6 more in the full report

8 fixed10 passed either way4 still failing0 broke

Pass-rate lift: +36 pp
Tokens: +124%
Agent turns: 0%
Response time: -37%

Model gemini-3.6-flashMeasured 8/14/2026v118 of 22 cases passed with skill

Tokens, turns and time describe resource use, not a dollar price.

Use this skill

Save it into your project's skills folder. Claude Code picks it up in its next session.

curl -fsSL --create-dirs -o .claude/skills/dpearson2699-coreml/SKILL.md https://app.decimal.ai/s/dpearson2699-coreml/SKILL.md

Want it pinned and updatable? pip install "decimalai>=0.15", then decimalai skills add dpearson2699-coreml, and start Claude Code with claude --plugin-dir "$(git rev-parse --show-toplevel)/.agents"

1 · One-time — install the CLI (docs ↗)

pip install "decimalai>=0.15"

2 · Get the skill — writes .agents/skills/dpearson2699-coreml/ and pins it in .decimal/skills.lock

decimalai skills add dpearson2699-coreml

No CLI? Get the same file — curl · raw file

curl -fsSL --create-dirs -o .claude/skills/dpearson2699-coreml/SKILL.md https://app.decimal.ai/s/dpearson2699-coreml/SKILL.md

Raw file: /s/dpearson2699-coreml/SKILL.md ↗ · machine-readable summary at /s/dpearson2699-coreml.json ↗

Safety checks: 3 of 3 passed

Code scan CleanIntent PassedContent Clean

See it in action

One of 8 test cases the skill fixed. Same model, same prompt, with and without the skill.

1 of 8

Prompt

I need to compute the standard deviation of an MLTensor and directly initialize an MLTensor from an existing MLMultiArray. What exact methods on MLTensor should I call for standard deviation and multiarray initialization in iOS 18?

What was checked, without and with the skill
Checked	Without skill	With skill
Warns that direct initialization like MLTensor(multiArray) or methods like tensor.std() do not exist on MLTensor.	Missed	Passed
Bridges between MLMultiArray and MLTensor via MLShapedArray.	Missed	Passed

case-11 · gemini-3.6-flash · 8/14/2026 · answers are capped and redacted

What's inside

The instructions your model reads when the skill loads.

SKILL.md414 lines2 files

Core ML Swift Integration

Load, configure, and run Core ML models in iOS apps. This skill covers the Swift side: model loading, prediction, MLTensor, profiling, and deployment.

Scope boundary: Python-side model conversion, optimization (quantization, palettization, pruning), and framework selection live in the apple-on-device-ai skill. This skill owns Swift integration only.

See references/coreml-swift-integration.md for complete code patterns including actor-based caching, batch inference, image preprocessing, and testing.

Loading Models
Model Configuration
Making Predictions
MLTensor (iOS 18+)
Working with MLMultiArray
Image Preprocessing
Multi-Model Pipelines
Vision Integration
Performance Profiling
Model Deployment
Memory Management
Common Mistakes
Review Checklist
References

Loading Models

Auto-Generated Classes

When you add a .mlmodel or .mlpackage to an app target, Xcode generates a Swift class with typed input/output. Use this whenever possible.

import CoreML

let config = MLModelConfiguration()

Track record

Benchmark

18 / 22

test cases passed, vs 10 / 22 without

Adoption

Users: 0
Times used: 0

Safety and source details

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/3/2026

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

dpearson2699 / swift-ios-skills

skills/coreml/SKILL.md

View on GitHub ↗

License NOASSERTIONImported 8/3/2026Version v1Bundled 1 file

More skills from the same source and nearby in the registry, with their headline benchmark lift.

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See it in action

What's inside

Core ML Swift Integration

Contents

Loading Models

Auto-Generated Classes

Track record

Benchmark

Adoption

SkillSafety

Source

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