Privacy Parser Pii Extraction

This reverses OpenAI's 1.5B Privacy Filter model to extract structured PII spans instead of masking them. You get label, text, and character offsets for emails, phones, names, account numbers, secrets, and five other types. The hybrid backend combines model predictions with regex backstops and hits 0.929 F1 on their fixtures, which is solid for production use. Runs on CPU in about 600ms per parse, faster on GPU. The architecture is clean: BIOES tagging into Viterbi decoding, then span merging to handle multi-token names, then regex catch for things the model misses like API tokens. First run downloads 3GB of weights. If you need to find PII before you redact it, or audit what's hiding in logs and databases, this does the job without reinventing NER from scratch.

skills.sh installs: 201
GitHub stars: 26
last updated: Jun 3, 2026

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Install

$ npx -y skills add aradotso/trending-skills --skill privacy-parser-pii-extraction --agent claude-code

Installs into .claude/skills of the current project.

Files

Privacy Parser — PII Span Extraction

Skill by ara.so — Daily 2026 Skills collection.

privacy-parser is the inverse of OpenAI's Privacy Filter. Where the filter masks PII with <REDACTED>, this library returns structured spans — label, text, and character offsets — using the same 1.5B opf model weights and label taxonomy.

Installation

# Clone the repo (includes both subpackages)
git clone https://github.com/chiefautism/privacy-parser
cd privacy-parser

uv venv
uv pip install -e ./privacy-filter   # installs the opf model + weights loader
uv pip install -e ./pii_parser       # installs the parser library

First run downloads the opf 1.5B checkpoint (~3 GB) to ~/.opf/privacy_filter/.

Quick Start

from pii_parser.hybrid import HybridPIIParser

parser = HybridPIIParser(device="cpu")  # or "cuda" / "mps"
result = parser.parse(
    "Hi Quindle Testwick (quindle.testwick@openai.com / +1-415-555-0102), "
    "account 40702810500001234567, 14 Beautiful Ct, Anytown USA, "
    "password Priv4cy-Filt3r-2026."
)

for span in result.spans:
    print(f"{span.label:18}  {span.text}")

SKILL.md on GitHub

Details

Repository: github.com/aradotso/trending-skills
First seen: Jun 3, 2026