PhoneClaw is a local AI agent framework that runs on phones and edge devices, using on-device models to understand requests and perform mobile tasks through native skills. It is for people who want an AI assistant on an iPhone or similar device with access to functions such as calendars, reminders, contacts, health data, voice, and image understanding, while optionally using web search or a Mac Gateway for selected tasks.
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 kellyvv/PhoneClaw --skill mlx-swift-lmgit clone --depth 1 https://github.com/kellyvv/PhoneClawWrote 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/kellyvv/phoneclaw/mlx-swift-lm)<a href="https://agentmods.dev/skills/kellyvv/phoneclaw/mlx-swift-lm"><img src="https://agentmods.dev/badge/skills/kellyvv/phoneclaw/mlx-swift-lm/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/kellyvv/phoneclaw/mlx-swift-lm"><img src="https://agentmods.dev/badge/skills/kellyvv/phoneclaw/mlx-swift-lm.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00052 | $0.03596 |
| Opus 5 | $0.00026 | $0.01798 |
| Sonnet 5 | $0.00010 | $0.00719 |
| Haiku 4.5 | $0.00005 | $0.00360 |
Grade A, and why
swift-mlx-lm 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 11d 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- swift-mlx-lm — 100% identical, 14 lines differ
- swift-mlx-lm — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 432 lines — stays where its author put it; the contents beside it link to each section on GitHub.
mlx-swift-lm Skill
1. Overview & Triggers
mlx-swift-lm is a Swift package for running Large Language Models (LLMs) and Vision-Language Models (VLMs) on Apple Silicon using MLX. It supports local inference, streaming generation, wired-memory coordination, tool calling, LoRA/DoRA fine-tuning, and embeddings.
When to Use This Skill
- Running LLM/VLM inference on macOS/iOS with Apple Silicon
- Streaming text generation from local models
- Coordinating concurrent inference with wired-memory policies and tickets
- Tool calling / function calling with models
- LoRA adapter training and fine-tuning
- Text embeddings for RAG/semantic search
- Porting model architectures from Python MLX-LM to Swift
Architecture Overview
MLXLMCommon - Core infra (ModelContainer, ChatSession, Evaluate, KVCache, wired memory helpers)
MLXLLM - Text-only LLM support (Llama, Qwen, Gemma, Phi, DeepSeek, etc.)
MLXVLM - Vision-Language Models (Qwen-VL, PaliGemma, Gemma3, etc.)
MLXEmbedders - Embedding models and pooling utilities
2. Key File Reference
| Purpose | File Path |
|---|---|
| Thread-safe model wrapper | Libraries/MLXLMCommon/ModelContainer.swift |
| Simplified chat API | Libraries/MLXLMCommon/ChatSession.swift |
| Generation & streaming APIs | Libraries/MLXLMCommon/Evaluate.swift |
| KV cache types | Libraries/MLXLMCommon/KVCache.swift |
| Wired-memory policies | Libraries/MLXLMCommon/WiredMemoryPolicies.swift |
| Wired-memory measurement helpers | Libraries/MLXLMCommon/WiredMemoryUtils.swift |
| Model configuration | Libraries/MLXLMCommon/ModelConfiguration.swift |
| Chat message types | Libraries/MLXLMCommon/Chat.swift |
| Tool call processing | Libraries/MLXLMCommon/Tool/ToolCallFormat.swift |
| Concurrency utilities | Libraries/MLXLMCommon/Utilities/SerialAccessContainer.swift |
| LLM factory & registry | Libraries/MLXLLM/LLMModelFactory.swift |
| VLM factory & registry | Libraries/MLXVLM/VLMModelFactory.swift |
| LoRA configuration | Libraries/MLXLMCommon/Adapters/LoRA/LoRAContainer.swift |
| LoRA training | Libraries/MLXLLM/LoraTrain.swift |
What ships with it
12 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.
- references/concurrency.md 8.3 KB
- references/embeddings.md 8.0 KB
- references/generation.md 3.9 KB
- references/kv-cache.md 7.1 KB
- references/lora-adapters.md 7.6 KB
- references/model-container.md 8.7 KB
- references/model-porting.md 13 KB
- references/supported-models.md 7.2 KB
- references/tokenizer-chat.md 7.4 KB
- references/tool-calling.md 8.9 KB
- references/training.md 9.0 KB
- references/wired-memory.md 3.7 KB
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.
- 11d ago First seen · 432 lines · 52 tokens per session scan A 050a1a81cd31
swift-mlx-lm is a skill published in the GitHub repository kellyvv/PhoneClaw (1,239 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 52 tokens to every session and 3,596 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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