git:20260930.30387c2 to git:20260930.c8ed96c

---

name: deidentify

description: Use when clinical data may contain PHI and must be de-identified before any LLM-assisted analysis. A local Python script (no network or AI calls) detects identifiers with regex and heuristics in 10 country locale packs, with interactive terminal review.

metadata:

triggers: "deidentify, de-identify, anonymize, 비식별화, 익명화, remove PHI, remove PII, strip patient info"

---

# De-identification Skill

You are guiding a medical researcher through data de-identification. The actual

de-identification is performed by a **standalone Python script** that runs WITHOUT

any LLM. Your role is to explain, guide, and verify — not to see or process raw

PHI data.

## Critical Safety Rules

1. **NEVER ask the user to paste, show, or upload raw data containing PHI.**

The script processes data locally. You never need to see patient-level data.

2. **NEVER read or display the mapping file contents.** It contains original PHI values.

4. **Always communicate in the user's preferred language** about the process, but use

English for technical terms (PHI, HIPAA, Safe Harbor, etc.).

## Reference Files

- `${CLAUDE_SKILL_DIR}/references/hipaa_18_identifiers.md` — HIPAA Safe Harbor checklist

- `${CLAUDE_SKILL_DIR}/references/korean_phi_patterns.md` — Korean-specific regex patterns

- `${CLAUDE_SKILL_DIR}/references/date_shift_guide.md` — Date shifting best practices

Read relevant references before advising the researcher.

## Prerequisites

- `openpyxl` (for .xlsx files): `pip install openpyxl`

- Supported formats: CSV, TSV, Excel (.xlsx)

## Five-Phase Workflow

### Phase 1: Assessment

Ask the researcher:

1. What file format is the data? (CSV, Excel, etc.)

2. What PHI do you expect in the data? (names, dates, IDs, etc.)

3. Does your IRB require specific de-identification documentation?

4. Do you need to re-identify later? (affects mapping file choice)

Based on answers, recommend the appropriate command:

Available locale codes: `kr` (Korea), `us` (USA), `jp` (Japan), `cn` (China), `de` (Germany),

`uk` (United Kingdom), `fr` (France), `ca` (Canada), `au` (Australia), `in` (India).

If `--locale` is omitted, the script shows an interactive country selection menu.

Users can provide a custom locale file via `--locale-file custom.json`.

### Phase 2: Script Execution

Guide the researcher to run the script. The script is located at:

```

${CLAUDE_SKILL_DIR}/deidentify.py

```

**Full pipeline** (recommended for most users):

```bash

--locale kr \

```

**Step-by-step** (for careful review):

```bash

# Step 1: Scan

# Step 2: Review (interactive)

```

**Options:**

- `--locale CODE`: Country locale for PHI patterns (kr, us, jp, cn, de, uk, fr, ca, au, in)

- `--locale-file PATH`: Custom locale JSON file (copy `locales/_template.json` to create one)

- `--output-dir`: Where to save de-identified file, mapping, and audit log

- `-v/--verbose`: Enable debug logging

### Phase 3: Interactive Review Guidance

The script's terminal review has three passes:

2. **Pass 2 — Undecided Items**: Columns that weren't resolved in Pass 1 get a second look

with more sample values displayed.

3. **Pass 3 — Final Summary**: A table of all planned actions. The researcher can edit

individual decisions before confirming.

Coach the researcher. Deliver these prompts in the researcher's preferred language:

- "Columns classified as PHI are anonymized by default. Press 'k' to keep the original value."

### Phase 4: Verify and Document

After the script completes, help the researcher verify:

```bash

cat ./deidentified/audit_log.csv | head -20

```

Verify the number of changes, affected columns, and PHI types.

3. **Check that sensitive columns are actually removed**:

Verify no original names, phone numbers, or RRN values remain.

4. **Mapping file security**:

- Remind the researcher: "mapping.json contains original patient identifiers — treat it as restricted."

- Recommend storing it separately from the de-identified data

- File permissions are automatically set to 0600 (owner-only)

### Phase 5: Documentation

Generate a de-identification methods paragraph for the manuscript or IRB:

Template:

> a rule-based de-identification tool (deidentify.py, medsci-skills) with the [COUNTRY]

> locale pattern pack. The tool scanned column names and cell values using regex patterns

> for country-specific identifiers (e.g., national ID numbers, phone numbers), email

> addresses, dates, and addresses. Each column classification was reviewed by the

> researcher in an interactive terminal session. Names were replaced with pseudonyms

> preserving relative temporal intervals, and direct identifiers (phone numbers, email

> addresses, national ID numbers) were suppressed. A total of [N] cells across [M]

> columns were de-identified. The de-identification mapping file was stored separately

> under restricted access (file permissions 0600).

## Cross-Skill Integration

- **deidentify** sits BEFORE `clean-data` in the research pipeline

- After de-identification, hand off to `/clean-data` for data quality profiling

- `/analyze-stats` can safely process the de-identified output

- `/write-paper` Methods section should reference the de-identification process

- `/write-protocol` can use the HIPAA/PIPA reference files for protocol documentation

## Output Files

| File | Contains PHI? | Safe for Claude? | Purpose |

|------|:------------:|:----------------:|---------|

## Scope and Limitations

**Supported (v1)**:

- Structured tabular data: CSV, TSV, Excel (.xlsx)

- 10 country locales with country-specific PHI patterns:

- Korea (kr): RRN (주민번호), phone, email, address, Hangul names, dates

- USA (us): SSN, US phone, US address, zip codes

- Japan (jp): マイナンバー, Japanese phone, 都道府県 address, Kanji names

- China (cn): 身份证号, Chinese phone, 省市区 address, Chinese names

- Germany (de): Steuer-ID, German phone, Straße address

- UK (uk): NHS Number, NI Number, UK phone, postcodes

- France (fr): NIR/INSEE, French phone, Rue address

- Canada (ca): SIN, Canadian phone, postal codes

- Australia (au): TFN, Medicare number, AU phone

- India (in): Aadhaar, PAN, Indian phone, pin codes

- Universal patterns (all locales): email, ISO dates, high-cardinality numeric IDs (MRN)

- English column names recognized across all locales

- Custom locale support via `--locale-file` with template

- Pseudonymization, date shifting, ID replacement, suppression

**NOT supported (planned for v2)**:

- DICOM image metadata (PS3.15 Annex E) — requires pydicom

- Clinical free-text NER (clinical notes, radiology reports)

- Automated k-anonymity / l-diversity assessment

- SPSS (.sav), SAS (.sas7bdat), or other statistical formats

## Anti-Hallucination

- **Never fabricate file paths, URLs, DOIs, or package names.** Verify existence before recommending.

- **Never invent journal metadata, impact factors, or submission policies** without verification at the journal's website.

- If a tool, package, or resource does not exist or you are unsure, say so explicitly rather than guessing.