How do I install the Session Anonymizer skill?

Run npx skillmds@latest add glebis/session-anonymizer in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Session Anonymizer safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: executes scripts, makes network calls, reads secrets. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Session Anonymizer?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Session Anonymizer free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Session Anonymizer?

glebis (@glebis) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Session Anonymizer by glebis — Agent Skill

Name: Session Anonymizer
Rating: 4.2 (5 reviews)
Author: 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".

glebis 6e62256 2 files · 16.2 KB Updated Sep 9, 2026

File contents

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:

Layer	Tool	Catches	Size	Speed
1	Natasha	Russian names, locations, organizations	27 MB	instant
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

---name: session-anonymizerdescription: 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".--- # 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: | Layer | Tool | Catches | Size | Speed | |-------|------|---------|------|-------| | 1 | Natasha | Russian names, locations, organizations | 27 MB | instant | | 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 ```bash 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 ```bash python3 ~/.claude/skills/therapy-anonymizer/scripts/anonymize.py session.txt ``` ### Stdin pipe ```bash cat session.txt | python3 ~/.claude/skills/therapy-anonymizer/scripts/anonymize.py ``` ### Batch processing ```bash python3 ~/.claude/skills/therapy-anonymizer/scripts/anonymize.py --batch ~/sessions/ -o ~/sessions_clean/ ``` ### JSON report ```bash python3 ~/.claude/skills/therapy-anonymizer/scripts/anonymize.py session.txt --json ``` ### Pseudonyms instead of tags ```bash python3 ~/.claude/skills/therapy-anonymizer/scripts/anonymize.py session.txt --pseudonyms ``` ### Select layers / model ```bash # 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) ```bash 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: ```bash 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

Frequently asked questions