You MUST use this skill for ANY Apple Intelligence or Foundation Models work.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionaxiom-ios-aiExecute the skills CLI command in your project's root directory to begin installation:
Fetches axiom-ios-ai from charleswiltgen/axiom and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate axiom-ios-ai. Access via /axiom-ios-ai in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
0
total installs
0
this week
767
GitHub stars
0
upvotes
Run in your terminal
0
installs
0
this week
767
stars
You MUST use this skill for ANY Apple Intelligence or Foundation Models work.
Use this router when:
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) | Route to ios-ml → CoreML conversion, compression |
| Computer vision (image analysis, OCR, segmentation) | Route to ios-vision → Vision framework |
| Cloud API integration (OpenAI, etc.) | Route to ios-networking → URLSession patterns |
| System AI features (Writing Tools, Genmoji) | No custom code needed — these are system-provided |
Key boundary: ios-ai vs ios-ml
Foundation Models + concurrency (session blocking main thread, UI freezes):
await or running on @MainActorFoundation Models + data (@Generable decoding errors, structured output issues):
Implementation patterns → /skill axiom-foundation-models
API reference → /skill axiom-foundation-models-ref
Diagnostics → /skill axiom-foundation-models-diag
| 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. |
foundation-models:
foundation-models-diag:
User: "How do I use Apple Intelligence to generate structured data?"
→ Invoke: /skill axiom-foundation-models
User: "My AI generation is being blocked"
→ Invoke: /skill axiom-foundation-models-diag
User: "Show me @Generable examples"
→ Invoke: /skill axiom-foundation-models-ref
User: "Implement streaming AI generation"
→ Invoke: /skill axiom-foundation-models
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"
→ Invoke: /skill axiom-foundation-models (async patterns) + also invoke ios-concurrency if needed
User: "I want to run my PyTorch model on device"
→ Route to: ios-ml router (CoreML conversion, not Foundation Models)
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
charleswiltgen/axiom
wondelai/skills
davila7/claude-code-templates
intellectronica/agent-skills
am-will/codex-skills
sickn33/antigravity-awesome-skills
axiom-ios-ai reduced setup friction for our internal harness; good balance of opinion and flexibility.
We added axiom-ios-ai from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
axiom-ios-ai is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Keeps context tight: axiom-ios-ai is the kind of skill you can hand to a new teammate without a long onboarding doc.
Solid pick for teams standardizing on skills: axiom-ios-ai is focused, and the summary matches what you get after install.
I recommend axiom-ios-ai for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Keeps context tight: axiom-ios-ai is the kind of skill you can hand to a new teammate without a long onboarding doc.
axiom-ios-ai fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
We added axiom-ios-ai from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
We added axiom-ios-ai from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
showing 1-10 of 51