Deidentifying Multilingual Text

What it does

When your agent uses it

• The user has Spanish
• Turkish medical notes
• Needs locale-aware fake surrogates
• Must handle language-specific national IDs (DNI

Example prompts

• “/deidentifying-multilingual-text”

Requirements

• Python 3

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

1. 1Confirm the language is supported by checking SUPPORTED_LANGUAGES.
2. 2Pass lang= to deidentify/extract_pii. This selects the
3. 3Set locale= for surrogates when method="replace". If omitted, the
4. 4Let accent normalization happen. For models trained on accent-free text
5. 5Keep surrogates stable across a document with consistent=True, seed=....

What it can do on your machine

Read from SKILL.md and the folder at commit 9dca507. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  No scripts in the folder and no shell commands in SKILL.md (its code samples are python).

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  Links to these hosts (documentation or services it may open):

  • hhs.gov

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Deidentifying Multilingual Text loads about 1.7k tokens when it runs. Until then it costs about 168 tokens; SKILL.md has 551 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~168

When it runs · the whole SKILL.md, loaded when a task matches

~1.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from maziyarpanahi/openmed at commit 9dca507, republished under its Apache-2.0 licence (© maziyarpanahi). 551 words, ~1,695 tokens.

name

deidentifying-multilingual-text

description

De-identify non-English clinical text on-device with OpenMed by passing lang= and locale= to deidentify(). Use when the user has Spanish, German, French, Italian, Portuguese, Dutch, Hindi, Telugu, Arabic, Japanese, or Turkish medical notes, needs locale-aware fake surrogates, must handle language-specific national IDs (DNI, NIR, Steuer-ID, codice fiscale, BSN, CPF, TCKN, Aadhaar), or asks which languages OpenMed PII supports. Covers SUPPORTED_LANGUAGES, get_pii_models_by_language, get_patterns_for_language, LANG_TO_LOCALE, and accent normalization. Pairs with OpenMed deidentifying-clinical-text and generating-synthetic-surrogates.

license

Apache-2.0

metadata.project

OpenMed

metadata.category

de-identification

metadata.pairs

adjacent

metadata.version

1.0

import openmed
from openmed.core.pii_i18n import SUPPORTED_LANGUAGES, get_patterns_for_language

print(sorted(SUPPORTED_LANGUAGES))   # query it; the set is the source of truth
# Language-appropriate default model for a code:
models = openmed.get_pii_models_by_language("es")
# Language-specific regex patterns (national IDs, phones, etc.):
patterns = get_patterns_for_language("de")

import openmed

nota = (
    "El paciente Carlos Hernández (DNI 12345678Z), nacido el 11/04/1979, "
    "vive en Calle Mayor 5, Madrid. Teléfono 612 345 678."
)

result = openmed.deidentify(
    nota,
    lang="es",                # selects the Spanish PII model + ES patterns
    method="replace",         # locale-native fake values
    locale="es_ES",           # Faker locale (defaults from lang via LANG_TO_LOCALE)
)
print(result.deidentified_text)
# El paciente [surrogate name] (DNI [surrogate]), nacido el [date], ...

befund = "Patientin Anna Müller, geb. 11.04.1979, Steuer-ID 12 345 678 901."
result = openmed.deidentify(befund, lang="de", method="replace")

© maziyarpanahi, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/deidentifying-multilingual-text of maziyarpanahi/openmed.

Open the folder on GitHubat commit 9dca507