Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add rshankras/claude-code-apple-skills --skill core-mlgit clone --depth 1 https://github.com/rshankras/claude-code-apple-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/rshankras/claude-code-apple-skills/core-ml)<a href="https://agentmods.dev/skills/rshankras/claude-code-apple-skills/core-ml"><img src="https://agentmods.dev/badge/skills/rshankras/claude-code-apple-skills/core-ml.svg" alt="Measured on agentmods" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00054 | $0.03514 |
| Opus 5 | $0.00027 | $0.01757 |
| Sonnet 5 | $0.00011 | $0.00703 |
| Haiku 4.5 | $0.00005 | $0.00351 |
Grade A, and why
core-ml scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 406 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Core ML Skills
Combined advisory, generator, and workflow skill for integrating machine learning into Apple platform apps. Covers Core ML model integration, Vision framework image analysis, NaturalLanguage framework text processing, Create ML training, and on-device model optimization.
When This Skill Activates
Use this skill when the user:
- Wants to add ML capabilities to their app
- Needs to integrate a Core ML model (.mlmodel) into an Xcode project
- Wants to use the Vision framework for image analysis (faces, text recognition, body pose, object detection)
- Wants to use the NaturalLanguage framework for text processing (sentiment, entities, language detection)
- Needs to train a custom model with Create ML
- Wants to optimize a model for on-device use (quantization, pruning, palettization)
- Needs to choose between Core ML and Foundation Models (Apple Intelligence)
- Asks about image classification, object detection, sound classification, or tabular data prediction
- Wants real-time camera + ML processing
Decision Guide: Core ML vs Foundation Models
Before generating code, determine which framework is appropriate.
Use Foundation Models (Apple Intelligence) When:
- You need general-purpose text generation, summarization, or conversational AI
- Target is iOS 26+ / macOS 26+ (Foundation Models requires Apple Silicon + latest OS)
- The task is open-ended language understanding or generation
- You want
@Generablestructured output from natural language - See
apple-intelligence/foundation-models/skill for implementation
Use Core ML When:
- You need specialized ML: image classification, object detection, sound classification, custom regression/classification
- You have a trained model (.mlmodel, .mlpackage) or plan to train one
- You need broad device support (iOS 14+ / macOS 11+)
- The task requires domain-specific predictions (medical imaging, product recognition, custom NLP)
- Performance-critical inference on Neural Engine or GPU
Use Vision Framework When (No Custom Model Needed):
- Image classification using Apple's built-in models
- Face detection and facial landmark analysis
- Text recognition (OCR) with
VNRecognizeTextRequest - Body and hand pose detection
- Barcode and QR code scanning
- Image similarity and saliency detection
- Horizon detection, rectangle detection
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 406 lines · 54 tokens per session scan A d12d9afd65b3
core-ml is a skill published in the GitHub repository rshankras/claude-code-apple-skills (710 stars, last pushed 1mo ago), licensed MIT. It adds 54 tokens to every session and 3,514 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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