Agent skill · DevOps & Cloud

running-openmed-ondevice

Run OpenMed models fully on-device with the MLX (Apple Silicon), CoreML (iOS/macOS), or ONNX/WebGPU (cross-platform/browser) backends, including convert-quantize-run workflows. Use when the user wants to deploy OpenMed at the edge, run NER/de-id on Apple Silicon, target iPhone/iPad/Mac, export to ONNX or WebGPU, quantize a clinical model to int8/4-bit, run with no network, or pick between MLX/CoreML/ONNX. Covers the mlx/coreml/onnx extras, the convert() functions and python -m convert CLIs, quantization, loading a local MLX artifact through analyze_text, OpenMedMLXLanguageModel/generate_text,

maziyarpanahigithub.com/maziyarpanahiGitHub ↗
claude-codeApache-2.0
Install
npx skills add maziyarpanahi/openmed --skill running-openmed-ondevice --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 1
SKILL.md size: 8 KB
Bundled scripts: none
Version: 1.0
Path: skills/running-openmed-ondevice/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 4,851
Language: Python
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Running OpenMed on-device OpenMed runs **fully on-device** by design. These three backends let you take it further at the edge: **MLX** (Apple Silicon acceleration), **CoreML** (iOS/macOS / Neural Engine), and **ONNX / WebGPU** (cross-platform and in-browser). The flow is the same: **convert → (quantize) → run locally**. Because inference is local, raw PHI never leaves the device — the strongest privacy posture OpenMed offers. ## When to use this skill When you need OpenMed where there is no server: an iOS/macOS app (CoreML), fast NER/de-id on an Apple Silicon Mac (MLX), or a portable/browser deployment (ONNX/WebGPU). For a hosted endpoint use `serving-openmed-rest-api`; for an agent tool use `deploying-openmed-mcp`; for corpora use `batch-processing-clinical-text`. ## Pick a backend | Backend | Extra | Best for | Quantization | | --- | --- | --- | --- | | **MLX** | `openmed[mlx]` | Apple Silicon Macs; fastest local NER/de-id; on-device LLMs | 4-bit / 8-bit weights | | **CoreML** | `openmed[coreml]` | iOS/iPadOS/macOS apps, Neural Engine | int8 palettization | | **ONNX / WebGPU** | `openmed[onnx]` | cross-platform runtimes, browser (transformers.js) | fp16 (WebGPU); int8 via ORT

What's inside
Steps it walks through
  1. When to use this skill
  2. Pick a backend
  3. Quick start — MLX (Apple Silicon)
  4. On-device LLM generation (MLX)
  5. Quick start — CoreML (iOS/macOS)
  6. Quick start — ONNX / WebGPU
  7. Workflow
  8. Hand-off to / from OpenMed
  9. Edge cases & gotchas
  10. Standards & references
Commands it runs
pip install "openmed[mlx]"
Convert a HF token-classification model to an OpenMed MLX artifact, 8-bit:
python -m openmed.mlx.convert --model OpenMed/<some-ner-model> --output ./mlx_ner --quantize 8
pip install "openmed[coreml]"
python -m openmed.coreml.convert --model OpenMed/<some-ner-model> --output model.mlpackage --quantize int8
pip install "openmed[onnx]"
python -m openmed.onnx.convert --model OpenMed/<some-ner-model> --output ./onnx_out
More from openmed
All skills →
auditing-deid-leakageAdversarially scan already-de-identified clinical text for residual identifiers and emit a leakage…Data & Analyticsauditing-deidentification-runsProduce a signed, reproducible, no-PHI audit trail for an OpenMed de-identification run via…Securityauthoring-model-cardsGenerate a model card for an OpenMed clinical NER or de-identification model documenting intended…Documentationbatch-processing-clinical-textRun large-scale batch NER, PII extraction, or de-identification over many clinical notes on-device…Data & Analyticsbenchmark-pii-recallBenchmark an OpenMed PII model with synthetic gold spans and report label-aware exact-span and…Backend & APIassembling-fhir-bundlesPackage multiple FHIR R4 resources produced from OpenMed output into a single valid transaction…Backend & API
About this skill
What does the running-openmed-ondevice skill do?

Run OpenMed models fully on-device with the MLX (Apple Silicon), CoreML (iOS/macOS), or ONNX/WebGPU (cross-platform/browser) backends, including convert-quantize-run workflows. Use when the user wants to deploy OpenMed at the edge, run NER/de-id on Apple Silicon, target iPhone/iPad/Mac, export to ONNX or WebGPU, quantize a clinical model to int8/4-bit, run with no network, or pick between MLX/CoreML/ONNX. Covers the mlx/coreml/onnx extras, the convert() functions and python -m convert CLIs, quantization, loading a local MLX artifact through analyze_text, OpenMedMLXLanguageModel/generate_text,

How do I install it?

Run `npx skills add maziyarpanahi/openmed --skill running-openmed-ondevice --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From maziyarpanahi/openmed, a repository with 4,851 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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