SkillsAdd

glebis/claude-skills

session-anonymizer

Three-layer PII anonymization for session transcripts (therapy, coaching, consulting, mentoring).

First seen May 6, 2026

Installation

$ npx skills add glebis/claude-skills --skill session-anonymizer
View on skills.sh ↗ GitHub repository ↗

Summary

  • 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".

Also in this package

Other skills from glebis/claude-skills · top by installs.

npx skills add glebis/claude-skills
google-image-search 636 pdf-generation 472 health-data 385 firecrawl-research 372 deep-research 314 presentation-generator 314 decision-toolkit 307 elevenlabs-tts 298 telegram 270 brand-agency 264

Browse all from glebis/claude-skills

More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Repository health

Stars 372
License MIT
Default branch main
Open issues 6
Status Active

Skill metadata

Parsed from SKILL.md frontmatter.

Declared agents claude-code

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 3,374 B
  • docs SUMMARY.md 510 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 147 installs

SKILL.md

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