deidentify

name	deidentify
description	De-identify clinical research data before LLM-assisted analysis. Standalone Python CLI detects PHI via regex + heuristics with 10 country locale packs (kr, us, jp, cn, de, uk, fr, ca, au, in). Interactive terminal review. No LLM touches raw data — the script runs locally without any network or AI calls.
triggers	deidentify, de-identify, anonymize, 비식별화, 익명화, remove PHI, remove PII, strip patient info
tools	Read, Bash, Glob
model	inherit

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.
3. You may read the scan report (column classifications, no raw values), audit log (SHA-256 hashes only), and de-identified output (PHI already removed).
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

• Python 3.10+
• 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:

• Full pipeline (most common): python deidentify.py full <file> --locale <code>
• Step-by-step (cautious): python deidentify.py scan <file> --locale <code> first

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):

python ${CLAUDE_SKILL_DIR}/deidentify.py full data.xlsx \
    --locale kr \
    --output-dir ./deidentified/ \
    --auto-accept-safe

Step-by-step (for careful review):

# Step 1: Scan
python ${CLAUDE_SKILL_DIR}/deidentify.py scan data.xlsx --locale kr --output-dir ./deidentified/

# Step 2: Review (interactive)
python ${CLAUDE_SKILL_DIR}/deidentify.py review ./deidentified/scan_report.json

# Step 3: Apply
python ${CLAUDE_SKILL_DIR}/deidentify.py apply ./deidentified/reviewed_report.json

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)
• --auto-accept-safe: Skip confirmation for columns classified as SAFE (faster for large datasets)
• --hash-mapping: Store SHA-256 hashes instead of original values in mapping file (one-way, more secure)
• --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:

1. Pass 1 — Column Classification: Each column is shown as PHI / REVIEW_NEEDED / SAFE. The researcher confirms or overrides each classification.
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."
• "REVIEW_NEEDED are columns the script could not classify. Check the sample values and decide."
• "SAFE means no PHI detected. Press 'r' to request re-review if any column looks suspicious."

Phase 4: Verify and Document

After the script completes, help the researcher verify:

1. Read the audit log (safe — contains only hashes):

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

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

2. Spot-check the de-identified file (safe — PHI already removed): Read a few rows to confirm pseudonyms (P0001, etc.), date shifts, and [REDACTED] markers appear where expected.

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:

Protected health information was removed from the dataset prior to analysis using 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 (P0001, P0002, ...), dates were shifted by a random per-patient offset (±365 days) 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).

Customize based on the actual audit log statistics.

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
*_deidentified.xlsx/csv	No	Yes	De-identified data for analysis
mapping.json	YES	No	Original ↔ pseudonym mapping
audit_log.csv	No (hashes only)	Yes	What was changed and where
scan_report.json	No	Yes	Column classification results
reviewed_report.json	No	Yes	Researcher-reviewed classifications

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.