ios-ai-ml

name	ios-ai-ml
description	Use when implementing or reviewing iOS AI and ML features with Apple on-device frameworks, model evaluation, and privacy controls; use ios-architecture for general module boundaries.
metadata	{"portable":true,"compatible_with":["claude-code","codex"]}

iOS AI/ML

Acknowledgement: Shared by Peter Bamuhigire, techguypeter.com, [phone].

Use When

• Building or reviewing iOS, iPadOS, or macOS AI/ML features using Foundation Models, Core AI, Core ML, Vision, NaturalLanguage, Speech, SoundAnalysis, Create ML, Evaluations, Private Cloud Compute, or a third-party Language Model provider.
• The work includes on-device inference, custom models, multimodal prompts, agentic model tool use, AI evaluation, model-provider selection, privacy-sensitive AI, or Apple Intelligence availability gates.

Do Not Use When

• The task is plain iOS implementation with no AI/ML path; use ios-development.
• The task is AI product architecture outside Apple platforms; use ai-app-architecture, ai-llm-integration, or ai-evaluation.
• The task is only AI security, prompt injection, or action authorization; use this skill with ios-security-and-rbac.

Required Inputs

• Feature goal, model/provider choices, target Apple platforms, device/OS floor, privacy constraints, offline expectations, data inputs, tools/actions the model may call, and evaluation criteria.
• Confirm whether the deliverable is design, implementation, migration, review, evaluation plan, or release evidence.

Workflow

1. Load ios-development for Apple-platform implementation rules and availability policy.
2. Load references/apple-intelligence-stack-wwdc26.md for Foundation Models, Core AI, provider routing, Evaluations, and availability gates.
3. Load references/skill-deep-dive.md only when you need legacy Core ML, Vision, NaturalLanguage, Create ML, or model-optimization recipes.
4. Choose the lowest-risk model path that solves the task: deterministic API, Core ML/Core AI owned model, on-device Foundation Models, Private Cloud Compute, or third-party provider.
5. Define privacy, fallback, telemetry, evaluation, and security gates before writing production code.

Quality Standards

• Every AI feature has a provider boundary, availability gate, privacy path, fallback state, and evaluation set.
• On-device claims must be true: no network dependency unless the code path is explicitly cloud or third-party.
• Agentic or tool-calling model features require authorization, audit logging, and security review.
• Model performance work must include device, latency, memory, battery, and thermal evidence.

Anti-Patterns

• Treating Core ML, Core AI, and Foundation Models as interchangeable.
• Sending sensitive user data to a cloud model because the on-device path was harder.
• Shipping prompt-only tests for model behavior that changes by tool, locale, region, model, or data state.
• Logging prompts, model context, OCR text, tool payloads, or generated sensitive content without a privacy review.

Outputs

• AI feature architecture, provider decision matrix, model integration plan, evaluation suite, privacy/fallback checklist, performance budget, or review findings.

Evidence Produced

Category	Artifact	Format	Example
Correctness	AI evaluation plan	Markdown doc covering prompts, providers, tools, unavailable states, and regression cases	docs/ios/ai-evaluations-checkout.md
Performance	On-device inference budget	Markdown doc covering per-device latency, memory, battery, and thermal budget	docs/ios/ai-perf-budget.md
Data safety	AI data-flow record	Markdown doc identifying on-device, PCC, third-party, logs, and retention	docs/ios/ai-data-flow.md

References

• references/apple-intelligence-stack-wwdc26.md for Foundation Models, Core AI, Evaluations, provider routing, availability gates, and security handoffs.
• references/skill-deep-dive.md for Core ML, Vision, NaturalLanguage, Create ML, model updates, optimization, privacy-preserving patterns, and older Apple ML recipes.

Quick Apple AI Stack Map

Inputs

Artefact	Produced by	Required?	Why
Use-case and harm definition	Product and security review	required	Bounds model behaviour and prohibited outcomes
Representative evaluation set	Domain owner	required	Measures quality on real inputs
Device and OS support matrix	ios-quality-and-release	required	Selects framework and fallback paths

Decision Rules

Constraint	Choice
Sensitive input and supported on-device task	On-device framework
Unsupported device or unavailable model	Deterministic non-AI fallback
Safety-critical or irreversible action	Require human confirmation; AI may only propose
Quality cannot be measured on representative data	Stop before release

Capability Contract

Read and search are required; model downloads, network calls, device execution, and edits require task authority. Never upload private input merely because local inference is unavailable.

Domain Anti-Patterns

• Shipping a demo prompt as an evaluation. Fix: use versioned representative cases and failure thresholds.
• Hiding model unavailability behind a spinner. Fix: expose a deterministic fallback state.
• Letting generated output perform irreversible actions. Fix: validate and confirm structured intent.
• Logging prompts containing personal data. Fix: redact or disable payload logging.
• Claiming support from simulator results alone. Fix: test the declared physical-device matrix.

Layer	Use For
Foundation Models	LLM-backed app features using Apple Foundation Models, PCC, Claude, Gemini, or another Language Model provider.
Core AI	Bring-your-own model runtime on Apple Silicon, on-device.
Core ML	Packaged or downloaded .mlmodel inference, typed wrappers, batch prediction, classic ML.
Vision	OCR, barcode, image analysis, camera understanding, and model-callable visual tools.
NaturalLanguage	Deterministic language tagging/classification when an LLM is not needed.
Speech/SoundAnalysis/Music Understanding	Audio transcription, classification, and local audio understanding.