# Pick A Pii Model

Select an on-device OpenMed PII model from the committed registry by language, runtime...

- Repository: https://github.com/maziyarpanahi/openmed (5755 stars)
- Category: AI and agents
- License: Apache-2.0
- Last updated: 2026-10-31
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add maziyarpanahi/openmed --skill pick-a-pii-model -g -a claude-code -y`
Codex: `npx skills add maziyarpanahi/openmed --skill pick-a-pii-model -g -a codex -y`
Cursor: `npx skills add maziyarpanahi/openmed --skill pick-a-pii-model -g -a cursor -y`
Gemini CLI: `npx skills add maziyarpanahi/openmed --skill pick-a-pii-model -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add maziyarpanahi/openmed`

## Description

Select an on-device OpenMed PII model from the committed registry by language, runtime format, and size budget, then require recall validation before deployment. Use when an agent must choose a local PII detector for CPU, Apple Silicon, or a mobile export without relying on live model discovery.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Pick an on-device PII model

Use the committed registry to build an offline shortlist. Treat the language
default as the safety baseline, but never treat model size or format as proof
of recall.

## Procedure

1. Identify the input language and script before choosing a model.
2. Choose the runtime: `pytorch` for local CPU/GPU and mobile export sources,
   `mlx-fp` or `mlx-8bit` for Apple Silicon.
3. Read `get_default_pii_model(language)` as the baseline.
4. Filter `get_pii_models_by_language(language)` by runtime and device budget.
5. Prefer the baseline when it fits; otherwise select a compatible candidate.
6. Benchmark the candidate on direct identifiers, critical leakage, scripts,
   and the target quantization before shipping.

## Runnable offline shortlist

This snippet reads only the bundled manifest; it does not download weights.

```python
from openmed import get_default_pii_model, get_pii_models_by_language

LANGUAGE = "en"
TARGET_FORMAT = "mlx-fp"  # Use "pytorch" for CPU or as an export source.
MAX_PARAMETERS_M = 150

baseline_id = get_default_pii_model(LANGUAGE)
models = get_pii_models_by_language(LANGUAGE)

shortlist = [
    (key, info)
    for key, info in models.items()
    if TARGET_FORMAT in info.formats
    and info.size_mb is not None
    and info.size_mb <= MAX_PARAMETERS_M
]
shortlist.sort(
    key=lambda item: (
        item[1].model_id != baseline_id,
        item[1].size_mb,
        item[0],
    )
)

if not shortlist:
    raise RuntimeError("No compatible PII model fits the requested budget")

registry_key, selected = shortlist[0]
print(
    {
        "registry_key": registry_key,
        "model_id": selected.model_id,
        "format": TARGET_FORMAT,
        "parameters_m": selected.size_mb,
        "recommended_confidence": selected.recommended_confidence,
        "is_language_default": selected.model_id == baseline_id,
    }
)
print("Benchmark this candidate against the language default before release.")
```

The rest: https://github.com/maziyarpanahi/openmed/blob/HEAD/skills/pick-a-pii-model/SKILL.md