Skill

anonymize-doc

Detect and anonymize PII (SSN, cards, emails, phones, names) and business data (companies, revenue, costs, pricing). Use when handling sensitive files or user requests anonymization. Check-only or full anonymization with 5 strategies (mask/hash/pseudo/token/mixed). GDPR/HIPAA aware.

Install

npx claudepluginhub digital-stoic-org/agent-skills --plugin dstoic

Tool Access

This skill is limited to using the following tools:

ReadBashWrite

Preview

Detect and anonymize PII + sensitive business data using ML-powered detection (Scrubadub + spaCy NER).

Supporting Assets

PHASE1_IMPROVEMENTS.mdexamples.mdreference.mdrequirements.txtscripts/anonymize.pyscripts/detect.pytest_names.txttest_names_fr.txt

SKILL.md

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Stats

Parent Repo Stars5

Parent Repo Forks2

Last CommitFeb 11, 2026

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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

✅ Full

hash

Analytics, tracking

✅ 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 .gitignore has *-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.

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 .gitignore has *-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.