Session Anonymizer

glebis/session-anonymizer

Three-layer PII anonymization for session transcripts (therapy, coaching, consulting, mentoring). Runs Natasha (Russian NER), OpenAI Privacy Filter, and local LLM (Ollama) in sequence for maximum coverage. Fully local by default. This skill should be used when anonymizing session transcripts, notes, or any text containing client PII before AI analysis. Triggers on "anonymize", "redact PII", "anonymize session", "protect client data", "strip personal data", "anonymize transcript".

4k tokens

context cost

the whole folder, loaded on every use

files

ships runnable scripts

copies elsewhere

how many repositories repackaged it

337

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/glebis/claude-skills --skill session-anonymizer

What comes with it

13 154 bytes besides the instruction

scripts/anonymize.py

The instruction itself

14 sections, as written by the author

Therapy Anonymizer

Three-layer PII detection and anonymization for therapy session transcripts. Supports Russian and English. Fully local by default — no data leaves the machine.

Architecture

Three detection layers run in sequence, each catching what others miss:

|-------|------|---------|------|-------|

| 2 | OpenAI Privacy Filter (opf) | Phones, accounts, addresses, emails | 2.8 GB | ~1.5s |

| 3 | Ollama LLM | Medications, dates, contextual IDs | 2.5-7 GB | ~10s |

Spans from all layers are merged, overlaps resolved, and a unified redacted output is produced.

Prerequisites

pip install natasha setuptools pymorphy2-dicts-ru
pip install 'opf @ git+https://github.com/openai/privacy-filter.git'
ollama pull qwen3:4b

Each layer is optional — the script gracefully skips unavailable layers and warns.

Usage

Single file

python3 ~/.claude/skills/therapy-anonymizer/scripts/anonymize.py session.txt

Stdin pipe

cat session.txt | python3 ~/.claude/skills/therapy-anonymizer/scripts/anonymize.py

Batch processing

python3 ~/.claude/skills/therapy-anonymizer/scripts/anonymize.py --batch ~/sessions/ -o ~/sessions_clean/

JSON report

python3 ~/.claude/skills/therapy-anonymizer/scripts/anonymize.py session.txt --json

Pseudonyms instead of tags

python3 ~/.claude/skills/therapy-anonymizer/scripts/anonymize.py session.txt --pseudonyms

Select layers / model

# Fast — Natasha only
python3 ~/.claude/skills/therapy-anonymizer/scripts/anonymize.py session.txt --layers natasha

# LLM only — maximum coverage
python3 ~/.claude/skills/therapy-anonymizer/scripts/anonymize.py session.txt --layers ollama --model gemma4:e2b

Encrypt output (AES-256)

python3 ~/.claude/skills/therapy-anonymizer/scripts/anonymize.py session.txt -o clean.txt --encrypt "password"

Invoking from Claude Code

To anonymize text already in context, pipe it through the script:

echo '<text>' | python3 ~/.claude/skills/therapy-anonymizer/scripts/anonymize.py --json

For files, pass the path directly. Always recommend manual review after automated anonymization.

Limitations

Contextual identifiers ("the only red-haired architect in Kostroma") are NOT detected by any automated tool
OPF is English-focused — Russian coverage is partial
Medications detected only by Layer 3 (requires Ollama)
Does not assess re-identification risk from combinations of non-PII fields

Guardrails

NEVER send raw transcripts to cloud services
Cloud verification only on already-anonymized text
Always recommend manual review for therapy data
Never log original PII values

How to use it

Copy the folder

Take glebis/session-anonymizer from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.

Install what it needs

The instructions reference pip. Without those the skill loads but fails at the first command.