openrouter-data-privacy

name	openrouter-data-privacy
description	Implement data privacy controls for OpenRouter API usage. Use when handling PII, meeting GDPR/CCPA requirements, or protecting sensitive data in prompts. Triggers: 'openrouter privacy', 'openrouter pii', 'openrouter gdpr', 'openrouter data handling'.
allowed-tools	Read, Write, Edit, Grep, Bash(python3:*)
version	1.20.0
license	MIT
author	Jeremy Longshore <[e-mail]>
tags	["saas","openrouter","privacy","security","compliance"]
compatibility	Designed for Claude Code

OpenRouter Data Privacy

Overview

When sending data through OpenRouter to upstream LLM providers, you're responsible for ensuring prompts don't leak PII inappropriately. OpenRouter itself does not train on API data, but each upstream provider has its own data retention and training policies. This skill covers PII detection and redaction, placeholder substitution, provider selection for privacy, and consent tracking.

Prerequisites

• An OpenRouter API key (sk-or-v1-...) exported as OPENROUTER_API_KEY — see the openrouter-install-auth skill for setup
• Python 3.8+ with the OpenAI SDK (pip install openai) — every pattern in this skill is Python
• A sensitivity classification for your workloads (public / standard / sensitive) so privacy_aware_completion() can route each one
• A list of providers your org approves for sensitive data, to plug into provider.order with allow_fallbacks: False

Instructions

1. Start with PII Detection and Redaction: adapt PII_RULES (email, phone, SSN, credit card, sk-or-v1- API keys, IPs) to your data, then run scan_and_redact() on representative inputs and review the findings for false positives.
2. When downstream code needs the original values back, use the Placeholder Substitution Pattern instead of plain redaction — PrivacyProxy.anonymize() before the API call, deanonymize() on the model's reply.
3. Classify each workload and route it via Provider Selection for Privacy: privacy_aware_completion() maps sensitivity to a model plus a provider block (order: ["Anthropic"], allow_fallbacks: False for standard/sensitive).
4. Wire the Privacy Middleware into every call path, choosing block_on_pii=True (raise on detection) or auto_redact=True (scrub and continue) per workload.
5. Apply the Enterprise Considerations: hash logged prompts (SHA-256) for GDPR right-to-erasure, and use BYOK for the most sensitive workloads.

PII Detection and Redaction

import re
from dataclasses import dataclass
 typing  


 :
    clean_text: 
    findings: []
    has_pii: 

PII_RULES = [
    (, ),
    (, ),
    (, ),
    (, ),
    (, ),
    (, ),
]

REPLACEMENTS = {
    : , : , : ,
    : , : , : ,
}

 () -> PiiScanResult:
    
    findings = []
    clean = text
     pii_type, pattern  PII_RULES:
        matches = re.findall(pattern, clean)
           matches:
            findings.append({: pii_type, : [:] + })
        clean = re.sub(pattern, REPLACEMENTS[pii_type], clean)

     PiiScanResult(clean_text=clean, findings=findings, has_pii=(findings) > )