scrub-pii-from-textlisted

# Scrub PII from text Observability captures prompts and completions, which in a regulated app can mean shipping account numbers or PII to a third-party backend. This skill ships a scrubber you can run in the export path so raw values never leave your process. ## Use the bundled script [`scripts/pii_scrub.py`](scripts/pii_scrub.py) is pure Python, no install needed: ```python from pii_scrub import scrub, find_pii scrub("email a@b.com, card 4111 1111 1111 1111") # -> "email <email>, card <card>" find_pii(text) # [("email", "a@b.com"), ("card", ...)] ``` It masks emails, credit cards (**Luhn-validated**, so random 16-digit order ids are not flagged), US SSNs, phone numbers, IPv4 addresses, and IBANs. Run it directly: `python scripts/pii_scrub.py`. ## How to apply it 1. **Redact before export**, not after: call `scrub()` in your span processor / logging hook so no path bypasses it (see `redact-pii-for-tracing` for where that hook goes). 2. **Redact both directions:** user input and model output (models echo PII back). 3. **Layer a real detector for high stakes:** this is a strong default, but for finance/health, add Presidio or LLM Guard plus a domain ruleset. Regex alone misses context-dependent PII. ## Validation Run the tests: `pytest skills/scrub-pii-from-text/tests/`. They confirm emails/SSNs/valid cards are masked, that a Luhn-invalid number is left alone (false-positive guard), that clean text is untouched, and that `find_p