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.).

• ${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

• Python 3.10+

• openpyxl

  (for .xlsx files):

  pip install openpyxl

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

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)

• Full pipeline (most common):

  python deidentify.py full <file> --locale <code>

• Step-by-step (cautious):

  python deidentify.py scan <file> --locale <code>

  first

kr

us

jp

cn

de

uk

fr

ca

au

in

--locale

--locale-file custom.json

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.

• "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."

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

   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)

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).

• 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

• 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

• 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

• 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.