axiom-ios-ml▌
charleswiltgen/axiom · updated May 22, 2026
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You MUST use this skill for ANY on-device machine learning or speech-to-text work.
iOS Machine Learning Router
You MUST use this skill for ANY on-device machine learning or speech-to-text work.
When to Use
Use this router when:
- Converting PyTorch/TensorFlow models to CoreML
- Deploying ML models on-device
- Compressing models (quantization, palettization, pruning)
- Working with large language models (LLMs)
- Implementing KV-cache for transformers
- Using MLTensor for model stitching
- Building speech-to-text features
- Transcribing audio (live or recorded)
Boundary with ios-ai
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.
Routing Logic
CoreML Work
Implementation patterns → /skill coreml
- Model conversion workflow
- MLTensor for model stitching
- Stateful models with KV-cache
- Multi-function models (adapters/LoRA)
- Async prediction patterns
- Compute unit selection
API reference → /skill coreml-ref
- CoreML Tools Python API
- MLModel lifecycle
- MLTensor operations
- MLComputeDevice availability
- State management APIs
- Performance reports
Diagnostics → /skill coreml-diag
- Model won't load
- Slow inference
- Memory issues
- Compression accuracy loss
- Compute unit problems
Speech Work
Implementation patterns → /skill speech
- SpeechAnalyzer setup (iOS 26+)
- SpeechTranscriber configuration
- Live transcription
- File transcription
- Volatile vs finalized results
- Model asset management
Decision Tree
- Implementing / converting ML models? → coreml
- CoreML API reference? → coreml-ref
- Debugging ML issues (load, inference, compression)? → coreml-diag
- Speech-to-text / transcription? → speech
Anti-Rationalization
| 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. |
Critical Patterns
coreml:
- Model conversion (PyTorch → CoreML)
- Compression (palettization, quantization, pruning)
- Stateful KV-cache for LLMs
- Multi-function models for adapters
- MLTensor for pipeline stitching
- Async concurrent prediction
coreml-diag:
- Load failures and caching
- Inference performance issues
- Memory pressure from models
- Accuracy degradation from compression
speech:
- SpeechAnalyzer + SpeechTranscriber setup
- AssetInventory model management
- Live transcription with volatile results
- Audio format conversion
Example Invocations
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
How to use axiom-ios-ml on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add axiom-ios-ml
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches axiom-ios-ml from GitHub repository charleswiltgen/axiom and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate axiom-ios-ml. Access the skill through slash commands (e.g., /axiom-ios-ml) or your agent's skill management interface.
Security & Verification Notice
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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ 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.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.7★★★★★70 reviews- ★★★★★Li Zhang· Dec 28, 2024
Useful defaults in axiom-ios-ml — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Maya Haddad· Dec 28, 2024
We added axiom-ios-ml from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Zaid Bansal· Dec 24, 2024
axiom-ios-ml fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Zaid Agarwal· Dec 24, 2024
Keeps context tight: axiom-ios-ml is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Pratham Ware· Dec 20, 2024
We added axiom-ios-ml from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Maya Khan· Dec 16, 2024
axiom-ios-ml has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Harper Haddad· Dec 8, 2024
axiom-ios-ml has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Daniel Abebe· Nov 27, 2024
axiom-ios-ml reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Chen Ghosh· Nov 19, 2024
I recommend axiom-ios-ml for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Benjamin Bhatia· Nov 15, 2024
Registry listing for axiom-ios-ml matched our evaluation — installs cleanly and behaves as described in the markdown.
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