| name | axiom-ai |
| description | Use when implementing ANY Apple Intelligence or on-device AI feature. Covers Foundation Models, @Generable, LanguageModelSession, structured output, Tool protocol, iOS 26 AI integration. |
| license | MIT |
Apple Intelligence & AI
You MUST use this skill for ANY Apple Intelligence or Foundation Models work.
When to Use
Use this router when:
- Implementing Apple Intelligence features
- Using Foundation Models
- Working with LanguageModelSession
- Generating structured output with @Generable
- Debugging AI generation issues
- iOS 26 on-device AI
AI Approach Triage
First, determine which kind of AI the developer needs:
| Developer Intent | Route To |
|---|
| On-device text generation (Apple Intelligence) | Stay here → Foundation Models skills |
| Custom ML model deployment (PyTorch, TensorFlow) — classic Core ML | See skills/ios-ml.md (hub) → conversion / compression / training files |
| Custom LLM-scale / transformer model on-device (27-cycle) | See skills/core-ai.md → Core AI conversion, runtime, specialization |
| Computer vision (image analysis, OCR, segmentation) | /skill axiom-vision → Vision framework |
| Cloud API integration (OpenAI, generic HTTP) | /skill axiom-networking → URLSession patterns |
| Cloud Claude integration (Anthropic SDK, Messages API, Claude Agent SDK) | See claude-api skill (external) → includes automated Opus 4.6 → 4.7 migration |
| System AI features (Writing Tools, Genmoji) | No custom code needed — these are system-provided |
Key boundary: Foundation Models vs ML (custom models)
- Foundation Models = Apple's on-device LLM framework (LanguageModelSession, @Generable)
- ML = Custom model deployment (CoreML conversion, quantization, MLTensor, speech-to-text)
- If developer says "run my own model" → skills/ios-ml.md. If "use Apple Intelligence" → stay here.
Training Path Boundaries
When developers say "I need to train / fine-tune / personalize a model," four distinct paths exist. They are often conflated; each has different output, lifecycle, and runtime compatibility.
| Path | Trains | Output | Lifecycle | Routes to |
|---|
| FM custom adapter (26-cycle only — runtime obsoleted in 27.0) | Apple's frozen on-device 3B LLM (rank-32 LoRA) | .fmadapter package, ~160 MB | Build-time per OS version, delivered via Background Assets | skills/foundation-models-adapters.md (discipline) + skills/foundation-models-adapters-ref.md (toolkit + runtime) + skills/foundation-models-adapters-diag.md (failure modes); delivery via axiom-integration (skills/background-assets.md) |
Core ML MLUpdateTask | Your NN-spec model's fully-connected and convolutional layers | Updated .mlmodelc saved to disk | Runtime, per-user (on-device personalization) | skills/coreml-training.md |
| Create ML | A new Core ML model from scratch / transfer learning | .mlmodel | Build-time, on Mac or iOS (per type) | skills/coreml-training.md |
MLX LM (mlx_lm.lora) | Open-source LLMs on Apple silicon | adapters/adapters.safetensors — NOT loadable by Foundation Models | Build-time; not an iOS distribution path | External — outside Axiom scope; treat as adjacent research tool |
| Server LLM fine-tune | Cloud-hosted model (e.g., vendor fine-tunes) | Cloud artifact, accessed via API | Build-time; runs in cloud | /skill axiom-networking for the API integration; the fine-tune workflow is the vendor's domain |
Critical distinctions:
- MLX LM output (
.safetensors) cannot be loaded into a LanguageModelSession. Different toolchain, different deployment target.
MLUpdateTask is NN-spec only — does not support ML Program (.mlpackage) models from modern PyTorch / TensorFlow conversion. This is the main reason it's rarely used in new projects.- FM custom adapters are pinned per-base-model version (per-OS). One adapter does NOT serve every device in your install base — see the Approach Triage section in
skills/foundation-models.md for the deflection ladder.
For the full "which path applies to me?" disambiguation (decision tree, the three week-costing mistakes, per-path routing) → skills/training-paths.md.
Cross-Domain Routing
Foundation Models + concurrency (session blocking main thread, UI freezes):
- Foundation Models sessions are async — blocking likely means missing
await or running on @MainActor
- Fix here first using async session patterns in foundation-models skill
- If concurrency issue is broader than Foundation Models → also invoke axiom-concurrency
Foundation Models + data (@Generable decoding errors, structured output issues):
- @Generable output problems are Foundation Models-specific, NOT generic Codable issues
- Stay here → foundation-models-diag handles structured output debugging
- If developer also has general Codable/serialization questions → also invoke axiom-data
Foundation Models + security (prompt injection, securing agent tools, confirmation gating):
- Threat modeling and mitigations for agentic features (
.onToolCall confirmation, .historyTransform spotlighting/redaction, lock-screen intent policy) → axiom-security (skills/agentic-security.md)
- Stay here for the API surface itself (DynamicProfile, tools, sessions)
Routing Logic
Custom Core ML Work (your own models, not Apple's LLM)
skills/ios-ml.md is the hub (deployment, runtime, speech-to-text). The lifecycle stages have dedicated files:
- Convert a trained PyTorch/TF/Keras model →
skills/coreml-conversion.md (coremltools.convert, ML Program vs NN-spec, parity validation)
- Compress it →
skills/coreml-compression.md (the PTQ-vs-QAT decision, palettization/quantization/pruning)
- Train from scratch / personalize on-device →
skills/coreml-training.md (Create ML; MLUpdateTask and its NN-spec-only limitation)
Core AI — the 27-cycle path for LLM-scale on-device models (OS27)
skills/core-ai.md covers Core AI, the on-device inference framework that powers Apple Intelligence and is now open to your apps. Route here (not skills/ios-ml.md) when the model is LLM-scale / a transformer, or when the developer needs custom Metal kernels, multi-function assets, ahead-of-time compilation, KV-cache states, or the specialization/caching deployment model. Covers the Python toolchain (coreai-torch/coreai-opt), the Swift runtime (import CoreAI → AIModel/InferenceFunction/NDArray), specialization discipline, and the Foundation Models bridge (CoreAILanguageModel from the open-source coreai-models package — not a system-framework type).
Foundation Models Work
Implementation patterns → skills/foundation-models.md
- LanguageModelSession basics
- @Generable structured output
- Tool protocol integration
- Streaming with PartiallyGenerated
- Dynamic schemas
- Private Cloud Compute model + multimodal image input (
OS27)
- WWDC 2025 + 2026 code examples
API reference → skills/foundation-models-ref.md
- Complete API documentation
- All @Generable examples
- Tool protocol patterns
- Streaming generation patterns
OS27: Private Cloud Compute, multimodal Attachment + ImageReference tool args, LanguageModel protocol + capabilities, reasoning + token usage, Dynamic Profiles (full modifier surface + @SessionProperty), Dynamic Instructions, custom model providers (LanguageModelExecutor), LanguageModelError migration, built-in system tools, improved Foundation Models Instrument
Diagnostics → skills/foundation-models-diag.md
- AI response blocked
- Generation slow
- Guardrail violations
- Context limits exceeded
- Model unavailable
Guardrails & safety decisions → skills/foundation-models-guardrails.md
- When to use
permissiveContentTransformations vs .default
- False-positive triage (correct refusal vs over-restrictive)
- Custom safety eval / red-team methodology
- Adapter × guardrail interaction (safety erosion)
Measuring feature quality (Evaluations framework, OS27) → skills/foundation-models-evaluations-ref.md
- Building a regression suite for an AI feature (
Evaluation, Metric, Evaluator, run via Swift Testing .evaluates)
- Datasets (
ModelSample/ArrayLoader) + synthesizing more (makeSamples/SampleGenerator)
- Model-as-judge for open-ended output (
ModelJudgeEvaluator, ScoringScale)
- Agentic tool-call/trajectory evaluation (
ToolCallEvaluator, TrajectoryExpectation)
- Hill-climbing a prompt/instruction change against an optimization-target metric
Custom adapter training (after Approach Triage rungs 1-4) → skills/foundation-models-adapters.md
- Decision discipline (when adapter training is justified vs. rungs 1-4)
- Maintenance contract (per-OS retrain burden, four-axis eval)
- Per-OS variant strategy and runtime fallback
- Dataset construction discipline
- HIG disclosure for adapter-enhanced features
Adapter toolkit & runtime API → skills/foundation-models-adapters-ref.md
- Python toolkit setup (3.11, 32 GB Apple silicon Mac or Linux GPU)
- Dataset JSONL schema (chat-turn + tool-calling extension)
examples.train_adapter, examples.train_draft_model, examples.generate, export.export_fmadapterSystemLanguageModel.Adapter runtime API and AssetError cases- Per-base-model-version compatibility matrix
com.apple.developer.foundation-model-adapter entitlement
Adapter-specific diagnostics → skills/foundation-models-adapters-diag.md
compatibleAdapterNotFound, invalidAdapterName, invalidAsset- Tool calls don't fire from adapter
- Adapter consumes context window with trivial prompts
- Accuracy drops after OS update (FB18924722)
coremltools.libmilstoragepython missing on export
Automated scanning → Launch foundation-models-auditor agent or /axiom:audit foundation-models
Detects anti-patterns AND architectural gaps:
- Missing availability checks, main-thread
respond(), manual JSON parsing, missing specific error catches (guardrail / contextWindow), session created per-tap, no streaming for long output, missing @Guide constraints, nested non-@Generable types, no fallback UI
- Prompt-injection risk from direct user-text interpolation,
@Generable enums without @frozen (future-case crash), missing Cancel UX, missing transcript trimming, stale availability cache after Settings toggle, partial-output validation gaps, Tool errors indistinguishable from session errors, no retry on transient errors
Scores: PRODUCTION-READY / NEEDS HARDENING / FRAGILE
Decision Tree
- Custom ML model / CoreML? → skills/ios-ml.md hub → convert (
coreml-conversion.md), compress (coreml-compression.md), or train/personalize (coreml-training.md). LLM-scale / transformer / 27-cycle custom model? → skills/core-ai.md (Core AI)
- Computer vision / image analysis / OCR? → /skill axiom-vision
- Cloud AI API integration? → /skill axiom-networking
- Implementing Foundation Models / @Generable / Tool protocol? → foundation-models
- Need API reference / code examples? → foundation-models-ref
- Debugging AI issues (blocked, slow, guardrails)? → foundation-models-diag
- Foundation Models + UI freezing? → foundation-models (async patterns) + also invoke axiom-concurrency if needed
- Considering training a custom adapter? → foundation-models Approach Triage (rungs 1-4) FIRST; only after documented rung-1-4 failures → foundation-models-adapters
- Implementing adapter loading, training pipeline, or runtime selection? → foundation-models-adapters + foundation-models-adapters-ref + axiom-integration (skills/background-assets.md) for delivery
- Debugging adapter-specific failures (compatibleAdapterNotFound, tool calls don't fire from adapter, accuracy regression after OS update)? → foundation-models-adapters-diag
- Want automated Foundation Models code scan? → foundation-models-auditor (Agent — detects 10 anti-patterns AND completeness gaps including prompt injection, frozen-enum discipline, transcript trimming, Cancel UX; scores PRODUCTION-READY / NEEDS HARDENING / FRAGILE)
- Measuring whether an AI feature improved/regressed, or building an eval/regression suite (incl. agentic tool-call eval)? → foundation-models-evaluations-ref (
OS27 Evaluations framework)
Anti-Rationalization
| Thought | Reality |
|---|
| "Foundation Models is just LanguageModelSession" | Foundation Models has @Generable, Tool protocol, streaming, and guardrails. foundation-models covers all. |
| "I'll figure out the AI patterns as I go" | AI APIs have specific error handling and fallback requirements. foundation-models prevents runtime failures. |
| "I've used LLMs before, this is similar" | Apple's on-device models have unique constraints (guardrails, context limits). foundation-models is Apple-specific. |
| "I know the Anthropic SDK already" | Opus 4.7 removed temperature, top_p, top_k, and prefill from the Messages API. Code that worked on 4.6 returns HTTP 400 at runtime. Read claude-api (external) before changing model IDs. |
| "We need to train a custom adapter to fix the model's outputs" | Most "we need an adapter" requests resolve via rungs 1-4 of the Approach Triage (prompt engineering, @Generable/@Guide, tool calling, built-in content-tagging adapter). foundation-models has the ladder; foundation-models-adapters is only justified after each rung's failure is documented. |
| "We trained one adapter, ship it for all our users" | Each .fmadapter pins to one base-model version; one adapter does not cover a multi-OS install base. foundation-models-adapters covers per-OS variant strategy and compatibleAdapterIdentifiers(name:) runtime selection. |
| "Skip locale-specific eval, our users are mostly English-speaking" | Apple's 2025 tech report groups eval as English-US / English-outside-US / PFIGSCJK. English-only eval against a multi-locale app ships invisible non-English regressions. foundation-models-adapters covers the four-axis eval requirement. |
| "Just bundle the .fmadapter file in the app" | Apple's docs explicitly prohibit this. Adapters ship via Background Assets onDemand policy. axiom-integration (skills/background-assets.md) covers the delivery half. |
| "We'll add a custom adapter for our iOS 27 app" | The custom-adapter runtime (SystemLanguageModel.Adapter) is obsoleted in 27.0 and does not compile on a 27 deployment target — no replacement in the 27 SDK. foundation-models-adapters covers the pivot: rungs 1-4 or a custom provider (LanguageModelExecutor). |
External Resources
Cloud Claude integration (claude-api skill, ships outside Axiom). Opus 4.7 removed temperature, top_p, top_k, and prefill from the Messages API — code that built successfully on 4.6 returns HTTP 400 at runtime, not compile time. The claude-api skill automates the migration (model ID swap, sampling-param removal, prefill replacement) and enforces prompt caching from day one. Skipping it costs an afternoon of production debugging when the first 400s arrive.
Apple's on-device Foundation Models and Anthropic's cloud Claude are unrelated stacks; use both in parallel when an app needs both, and treat claude-api as mandatory reading before any Claude model-ID change ships.
Critical Patterns
foundation-models:
- LanguageModelSession setup
- @Generable for structured output
- Tool protocol for function calling
- Streaming generation
- Dynamic schema evolution
foundation-models-diag:
- Blocked response handling
- Performance optimization
- Guardrail violations
- Context management
Example Invocations
User: "How do I use Apple Intelligence to generate structured data?"
→ Read: skills/foundation-models.md
User: "My AI generation is being blocked"
→ Read: skills/foundation-models-diag.md
User: "Show me @Generable examples"
→ Read: skills/foundation-models-ref.md
User: "Implement streaming AI generation"
→ Read: skills/foundation-models.md
User: "I want to add AI to my app"
→ First ask: Apple Intelligence (Foundation Models) or custom ML model? Route accordingly.
User: "My Foundation Models session is blocking the UI"
→ Read: skills/foundation-models.md (async patterns) + also invoke axiom-concurrency if needed
User: "Review my Foundation Models code for issues"
→ Invoke: foundation-models-auditor agent
User: "I want to run my PyTorch model on device"
→ Read: skills/ios-ml.md (classic Core ML conversion, not Foundation Models)
User: "I want to run my own LLM / SAM segmentation model on device" / "convert a PyTorch transformer with Core AI" / "my Core AI model stalls on first launch"
→ Read: skills/core-ai.md (Core AI conversion, runtime, specialization & caching)
User: "How do I train a custom adapter for our app's summarization?"
→ Read: skills/foundation-models.md (Approach Triage rungs 1-4 FIRST), then skills/foundation-models-adapters.md only if rung-1-4 failures are documented
User: "Our adapter loaded fine on iOS 26.0 but throws compatibleAdapterNotFound on 26.1"
→ Read: skills/foundation-models-adapters-diag.md (Pattern 1)
User: "What's the toolkit setup for adapter training?"
→ Read: skills/foundation-models-adapters-ref.md (Toolkit Setup)
User: "How do we ship a custom adapter to users?"
→ Read: skills/foundation-models-adapters.md (runtime lifecycle) + axiom-integration (skills/background-assets.md) (delivery)
User: "How do I measure if my prompt change made the tagging feature better?" / "Write an eval suite for my AI feature"
→ Read: skills/foundation-models-evaluations-ref.md (Evaluations framework — Metrics, Swift Testing .evaluates, model-as-judge, tool-call eval)
