菜单
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'.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/jeremylongshore/claude-code-plugins-plus-skills --skill openrouter-data-privacy命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Implement user sign-up and sign-in flows with Clerk. Use when building authentication UI, customizing sign-in experience, or implementing OAuth social login. Trigger with phrases like "clerk sign-in", "clerk sign-up", "clerk login flow", "clerk OAuth", "clerk social login".
Implement session management and middleware with Clerk. Use when managing user sessions, configuring route protection, or implementing token refresh and custom JWT templates. Trigger with phrases like "clerk session", "clerk middleware", "clerk route protection", "clerk token", "clerk JWT".
Configure enterprise SSO, role-based access control, and organization management. Use when implementing SSO integration, configuring role-based permissions, or setting up organization-level controls. Trigger with phrases like "clerk SSO", "clerk RBAC", "clerk enterprise", "clerk roles", "clerk permissions", "clerk organizations".
正在显示 SKILL.md
基于 SOC 职业分类
| 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 <jeremy@intentsolutions.io> |
| tags | ["saas","openrouter","privacy","security","compliance"] |
| compatibility | Designed for Claude Code, also compatible with Codex and OpenClaw |
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.
sk-or-v1-...) exported as OPENROUTER_API_KEY — see the openrouter-install-auth skill for setuppip install openai) — every pattern in this skill is Pythonpublic / standard / sensitive) so privacy_aware_completion() can route each oneprovider.order with allow_fallbacks: FalsePII_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.PrivacyProxy.anonymize() before the API call, deanonymize() on the model's reply.privacy_aware_completion() maps sensitivity to a model plus a provider block (order: ["Anthropic"], allow_fallbacks: False for standard/sensitive).block_on_pii=True (raise on detection) or auto_redact=True (scrub and continue) per workload.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) > )
import os, uuid
from openai import OpenAI
client = OpenAI(
base_url="https://openrouter.ai/api/v1",
api_key=os.environ["OPENROUTER_API_KEY"],
default_headers={"HTTP-Referer": "https://my-app.com", "X-Title": "my-app"},
)
class PrivacyProxy:
"""Replace PII with placeholders before API, restore after."""
def __init__(self):
self._map: dict[str, str] = {}
def anonymize(self, text: str) -> str:
"""Replace PII with unique placeholders."""
result = scan_and_redact(text)
if not result.has_pii:
return text
# Use deterministic placeholders for consistent replacement
anonymized = text
for pii_type, pattern in PII_RULES:
for match in re.finditer(pattern, anonymized):
original = match.group()
if original not in self._map:
placeholder = f"[{pii_type.upper()}_{len(self._map)}]"
self._map[placeholder] = original
else:
placeholder = next(k for k, v in self._map.items() if v == original)
anonymized = anonymized.replace(original, placeholder, 1)
return anonymized
def deanonymize(self, text: str) -> str:
"""Restore original values from placeholders."""
result = text
for placeholder, original in self._map.items():
result = result.replace(placeholder, original)
return result
# Usage
proxy = PrivacyProxy()
user_input = "Contact john@example.com or call 555-123-4567"
safe_input = proxy.anonymize(user_input)
# safe_input = "Contact [EMAIL_0] or call [PHONE_1]"
response = client.chat.completions.create(
model="anthropic/claude-3.5-sonnet",
messages=[{"role": "user", "content": safe_input}],
max_tokens=200,
)
# Restore PII in the response if model referenced it
result = proxy.deanonymize(response.choices[0].message.content)
# Force specific provider to control data handling
def privacy_aware_completion(messages, sensitivity="standard"):
"""Route to appropriate provider based on data sensitivity."""
PRIVACY_CONFIG = {
"public": {
"model": "openai/gpt-4o-mini",
"provider": None, # Any provider OK
},
"standard": {
"model": "anthropic/claude-3.5-sonnet",
"provider": {"order": ["Anthropic"], "allow_fallbacks": False},
},
"sensitive": {
"model": "anthropic/claude-3.5-sonnet",
"provider": {"order": ["Anthropic"], "allow_fallbacks": False},
# Add PII redaction as mandatory pre-processing
},
}
config = PRIVACY_CONFIG.get(sensitivity, PRIVACY_CONFIG["standard"])
extra = {}
if config["provider"]:
extra["extra_body"] = {"provider": config["provider"]}
return client.chat.completions.create(
model=config["model"],
messages=messages,
max_tokens=1024,
**extra,
)
class PrivacyMiddleware:
"""Enforce privacy policies before every API call."""
def __init__(self, block_on_pii: bool = False, auto_redact: bool = True):
self.block_on_pii = block_on_pii
self.auto_redact = auto_redact
def process(self, messages: list[dict]) -> list[dict]:
"""Scan and optionally redact PII from all messages."""
processed = []
for msg in messages:
content = msg.get("content", "")
if isinstance(content, str):
result = scan_and_redact(content)
if result.has_pii:
if self.block_on_pii:
raise ValueError(f"PII detected: {[f['type'] for f in result.findings]}")
if self.auto_redact:
msg = {**msg, "content": result.clean_text}
processed.append(msg)
return processed
The privacy flows in this skill produce:
PiiScanResult per scan: clean_text with placeholders substituted, findings (PII type + first-4-chars value prefix per match), and a has_pii flag"Contact [EMAIL_0] or call [PHONE_1]" plus the placeholder→original map that deanonymize() uses to restore values in the responseprovider.order + allow_fallbacks: False config is appliedValueError listing the detected PII types when PrivacyMiddleware runs with block_on_pii=TrueScanning a support message before it leaves your infrastructure:
result = scan_and_redact("Contact john@example.com or call 555-123-4567")
print(result.clean_text) # Contact [EMAIL] or call [PHONE]
print(result.has_pii) # True
print(result.findings) # [{'type': 'email', 'value_prefix': 'john...'}, {'type': 'phone', ...}]
To keep the values recoverable, run the same input through PrivacyProxy.anonymize() instead, send the placeholder version to the model, then deanonymize() the reply. More worked examples: references/examples.md.
| Error | Cause | Fix |
|---|---|---|
| PII detected in prompt | User input contains sensitive data | Auto-redact or block and prompt user to remove |
| Provider retained data | Using provider with training-on-API-data | Switch to Anthropic or use BYOK |
| Placeholder in response | Model used placeholder literally | Map it back with deanonymize() |
| False positive PII match | Regex too aggressive | Tune patterns; use NLP-based PII detection for accuracy |
provider.order + allow_fallbacks: false to ensure data only flows to approved providers