Axiom axiom-ai

Use when implementing ANY Apple Intelligence or on-device AI feature. Covers Foundation Models, @Generable, LanguageModelSession, structured output, Tool protocol, iOS 26 AI integration.

install

source · Clone the upstream repo

git clone https://github.com/CharlesWiltgen/Axiom

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/CharlesWiltgen/Axiom "$T" && mkdir -p ~/.claude/skills && cp -r "$T/axiom-codex/skills/axiom-ai" ~/.claude/skills/charleswiltgen-axiom-axiom-ai-0be6c9 && rm -rf "$T"

manifest: axiom-codex/skills/axiom-ai/SKILL.md

source content

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)	See skills/ios-ml.md → CoreML conversion, compression
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.

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

Routing Logic

Foundation Models Work

Implementation patterns →

skills/foundation-models.md

LanguageModelSession basics
@Generable structured output
Tool protocol integration
Streaming with PartiallyGenerated
Dynamic schemas
26 WWDC code examples

API reference →

skills/foundation-models-ref.md

Complete API documentation
All @Generable examples
Tool protocol patterns
Streaming generation patterns

Diagnostics →

skills/foundation-models-diag.md

AI response blocked
Generation slow
Guardrail violations
Context limits exceeded
Model unavailable

Automated scanning → Launch

foundation-models-auditor

agent or

/axiom:audit foundation-models

(missing availability checks, main thread blocking, manual JSON parsing, session lifecycle issues)

Decision Tree

Custom ML model / CoreML / PyTorch conversion? → See skills/ios-ml.md
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
Want automated Foundation Models code scan? → foundation-models-auditor (Agent)

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.

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

(CoreML conversion, not Foundation Models)