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".
npx skills add https://github.com/glebis/claude-skills --skill session-anonymizer
Three-layer PII detection and anonymization for therapy session transcripts. Supports Russian and English. Fully local by default — no data leaves the machine.
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.
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.
python3 ~/.claude/skills/therapy-anonymizer/scripts/anonymize.py session.txt
cat session.txt | python3 ~/.claude/skills/therapy-anonymizer/scripts/anonymize.py
python3 ~/.claude/skills/therapy-anonymizer/scripts/anonymize.py --batch ~/sessions/ -o ~/sessions_clean/
python3 ~/.claude/skills/therapy-anonymizer/scripts/anonymize.py session.txt --json
python3 ~/.claude/skills/therapy-anonymizer/scripts/anonymize.py session.txt --pseudonyms
# 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
python3 ~/.claude/skills/therapy-anonymizer/scripts/anonymize.py session.txt -o clean.txt --encrypt "password"
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.
> FHIR REST endpoints (Patient, Observation, Encounter, Condition, MedicationRequest), (2) Validating FHIR resources and returning proper HTTP status codes and error responses, (3) Implementing SMART on FHIR authorization and OAuth scopes, (4) Working with Bundles, transactions, batch operations, or search pagination. Covers FHIR R4 resource structures, required fields, value sets (status codes, gender, intent), coding systems (LOINC, SNOMED, RxNorm, ICD-10), and OperationOutcome error handling.
Interact with ClawDirect, a directory of social web experiences for AI agents. Use this skill to browse the directory, like entries, or add new sites. Requires ATXP authentication for MCP tool calls. Triggers: browsing agent-oriented websites, discovering social platforms for agents, liking/voting on directory entries, or submitting new agent-facing sites to ClawDirect.
Shared audit integrity framework for all AppSec agents — enforces output quality, intellectual honesty, and continuous improvement through anti-rationalization guards, self-critique loops, retry protocols, non-negotiable behaviors, self-reflection quality gates (1-10 scoring, ≥8 threshold), and a self-learning system with lesson/memory governance for security analysis agents.
Opt out of the OneCLI gateway and supply Anthropic credentials from .env instead. For users who want simple .env-based credential management without the OneCLI agent vault. Reads the API key or OAuth token from .env and injects it into the container's API requests.
Cross-product Zoom reference skill. Use after the workflow is clear when you need shared platform guidance, app-model comparisons, authentication context, scopes, marketplace considerations, or API-vs-MCP routing.
>- Static source-code vulnerability scan. Reads a target directory (and THREAT_MODEL.md if present), spawns parallel review subagents per focus area, and writes VULN-FINDINGS.json + .md for /triage to consume. Read-only — no building, running, or network. For execution-verified crashes, use vuln-pipeline instead. Use when asked to "scan for vulns", "review this code for security issues", "find bugs in <dir>", or as the step between /threat-model and /triage.
Hunt Session Management vulnerabilities — session fixation (no regeneration on login), insufficient invalidation on logout / password-change / email-change, predictable or low-entropy session IDs, JWT-as-session with no exp/revocation, refresh-token rotation/reuse-detection gaps, OAuth/SSO session linkage, device-bound-session (DBSC) downgrade, and cookie attribute issues (Secure/HttpOnly/SameSite/__Host-). Validate with TWO real sessions (attacker A + victim B), body-diff every 200, and OOB confirmation for theft chains. Medium to Critical (fixation→admin hijack, no-invalidation→persistent ATO).
> Use this skill when the user is doing hands-on DOCA AES-GCM work on a BlueField DPU or ConnectX NIC — configuring `doca_aes_gcm_task_encrypt` / `_task_decrypt`, querying `doca_aes_gcm_cap_*` for per-key-type (only `DOCA_AES_GCM_KEY_128` / `_256` — AES-192 not supported) and per-task support, sizing plaintext against the max-buf cap, setting source / destination mmap permissions, validating with a NIST GCMVS or RFC 5288 vector, or debugging DOCA_ERROR_* including the security-critical tag-verification-failed outcome on decrypt. Trigger even when the user does not explicitly mention "DOCA AES-GCM" or IO_FAILED", "auth tag isn't verifying", "NOT_PERMITTED on my encrypt buffer", "is AES-192-GCM on this BlueField" (no), or "encrypted record came back tampered". Refuse and route elsewhere for non-GCM AES modes (CBC / CTR / XTS — CPU OpenSSL), key management (KMS / HSM / rotation), SHA (doca-sha), or general AEAD background.
Take glebis/session-anonymizer from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
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.
The instructions reference pip.
Without those the skill loads but fails at the first command.