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ios-ai-ml

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Updated 10-16 03:41

About this skill

iOS AI/ML

Acknowledgement: Shared by Peter Bamuhigire, techguypeter.com, +256 784 464178.

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
Privacy 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

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.