PII & Business Data Anonymization
Detect and anonymize PII + sensitive business data using ML-powered detection (Scrubadub + spaCy NER).
Instructions
0. Dependency Check
SKILL_DIR="$(dirname "$(realpath "$0")" 2>/dev/null || echo /home/mat/dev/agent-skills/dstoic/skills/anonymize-doc)"
pip install -q -r "$SKILL_DIR/requirements.txt" && python -m spacy download -q en_core_web_sm 2>/dev/null
1. Ask Mode
Question: "Check for PII/business data (detection only) or anonymize?"
2. Detection
- Run:
python "$SKILL_DIR/scripts/detect.py" <file_path> - Parse JSON output → format report (type, line/col, severity, category)
- Ask: "Proceed with anonymization?"
3. Anonymization
Ask strategy:
| Strategy | Use Case | Reversible | GDPR |
|---|---|---|---|
mask |
Max privacy, redaction | No | ✅ Full |
hash |
Analytics, tracking | No | ✅ Full |
pseudo |
Demos, case studies | Yes | ⚠️ Partial |
token |
Financial, vault-backed | Yes | ⚠️ Partial |
mixed |
Complex docs (auto per severity) | Mixed | ⚠️ Partial |
Run: python "$SKILL_DIR/scripts/anonymize.py" <file_path> --strategy <choice>
Outputs: <file>-anonymized.<ext> + <file>-audit-log.json
4. Safety Rules
- Preserve original file — never overwrite
- Never commit audit logs to git — check
.gitignorehas*-audit-log.json - Warn: reversible strategies need secure mapping storage
- GDPR requires irreversible (mask/hash) for full compliance
See reference.md for entity types, severity tiers, compliance details.
See examples.md for before/after samples.
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