You MUST use this skill for ANY on-device machine learning or speech-to-text work.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionaxiom-ios-mlExecute the skills CLI command in your project's root directory to begin installation:
Fetches axiom-ios-ml 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-ml. Access via /axiom-ios-ml 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
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You MUST use this skill for ANY on-device machine learning or speech-to-text work.
Use this router when:
ios-ml vs ios-ai — know the difference:
| Developer Intent | Router |
|---|---|
| "Use Apple Intelligence / Foundation Models" | ios-ai — Apple's on-device LLM |
| "Run my own ML model on device" | ios-ml — CoreML conversion + deployment |
| "Add text generation with @Generable" | ios-ai — Foundation Models structured output |
| "Deploy a custom LLM with KV-cache" | ios-ml — Custom model optimization |
| "Use Vision framework for image analysis" | ios-vision — Not ML deployment |
| "Use pre-trained Apple NLP models" | ios-ai — Apple's models, not custom |
Rule of thumb: If the developer is converting/compressing/deploying their own model → ios-ml. If they're using Apple's built-in AI → ios-ai. If they're doing computer vision → ios-vision.
Implementation patterns → /skill coreml
API reference → /skill coreml-ref
Diagnostics → /skill coreml-diag
Implementation patterns → /skill speech
| Thought | Reality |
|---|---|
| "CoreML is just load and predict" | CoreML has compression, stateful models, compute unit selection, and async prediction. coreml covers all. |
| "My model is small, no optimization needed" | Even small models benefit from compute unit selection and async prediction. coreml has the patterns. |
| "I'll just use SFSpeechRecognizer" | iOS 26 has SpeechAnalyzer with better accuracy and offline support. speech skill covers the modern API. |
coreml:
coreml-diag:
speech:
User: "How do I convert a PyTorch model to CoreML?"
→ Invoke: /skill coreml
User: "Compress my model to fit on iPhone"
→ Invoke: /skill coreml
User: "Implement KV-cache for my language model"
→ Invoke: /skill coreml
User: "Model loads slowly on first launch"
→ Invoke: /skill coreml-diag
User: "My compressed model has bad accuracy"
→ Invoke: /skill coreml-diag
User: "Add live transcription to my app"
→ Invoke: /skill speech
User: "Transcribe audio files with SpeechAnalyzer"
→ Invoke: /skill speech
User: "What's MLTensor and how do I use it?"
→ Invoke: /skill coreml-ref
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.
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Useful defaults in axiom-ios-ml — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
We added axiom-ios-ml from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
axiom-ios-ml fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Keeps context tight: axiom-ios-ml is the kind of skill you can hand to a new teammate without a long onboarding doc.
We added axiom-ios-ml from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
axiom-ios-ml has been reliable in day-to-day use. Documentation quality is above average for community skills.
axiom-ios-ml has been reliable in day-to-day use. Documentation quality is above average for community skills.
axiom-ios-ml reduced setup friction for our internal harness; good balance of opinion and flexibility.
I recommend axiom-ios-ml for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Registry listing for axiom-ios-ml matched our evaluation — installs cleanly and behaves as described in the markdown.
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