deidentify

name	deidentify
description	Detect and redact PII in the bundled document, then report the privacy/utility tradeoff. Usage /deidentify [path]

/deidentify [path]

Run the full Detect → Redact → Measure pipeline on a document (defaults to the bundled data/customer_support_ticket.txt) and save a grounded report.

1. Detect (detector agent): python -m tools.deidentify --json > reports/_scan.json Read it; note the entity counts per type (EMAIL, SSN, PHONE, PERSON, ACCOUNT).
2. Redact (redactor agent): save the redacted field from reports/_scan.json to reports/<doc>.redacted.txt. Confirm each placeholder appears.
3. Measure (privacy-analyst agent): python -m tools.metrics > reports/_metrics.json Read the privacy and utility numbers.
4. Gate: if privacy < 1.0, an identifier leaked — name the type and re-run detection before reporting. If utility is low, redaction was too aggressive — flag the over-redacted span. Loop at most 3 times.
5. Save to reports/<doc>.md:
   • the per-type entity counts,
   • the privacy and utility numbers (quote them exactly from reports/_metrics.json),
   • one sentence on the tradeoff (what was removed vs. what readable content remains),
   • the path to the redacted file.

Cite only the tool outputs — entity counts and the two metrics. Do not claim PII was removed without the privacy number, and never estimate a score by hand.

Try these to start:

• /deidentify ← the bundled support ticket.
• Add a new identifier to data/customer_support_ticket.txt and re-run; watch the counts change.
• Add a benign sentence with capitalised words and confirm utility stays at 1.0 — the detector should not redact it.