Skip to main content الرئيسية المنشئون jeremylongshore tons-of-skills-marketplace perplexity-policy-guardrails
perplexity-policy-guardrails Implement content moderation, model selection policy, citation quality enforcement,
and per-user usage quotas for Perplexity Sonar API.
Trigger with phrases like "perplexity policy", "perplexity guardrails",
"perplexity content moderation", "perplexity usage limits", "perplexity safety".
الانتقال إلى التثبيت سوق المهارات اكتشف واستكشف مهارات الذكاء الاصطناعي التي بناها المجتمع.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
عرض تفاصيل Prompt يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/jeremylongshore/tons-of-skills-marketplace --skill perplexity-policy-guardrailsيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
تحميل Zip جاري التحميل... المزيد من هذا المستودع langchain-deploy-integration Deploy a LangChain 1.0 / LangGraph 1.0 app to Cloud Run, Vercel, or LangServe correctly — with timeouts sized for chain length, cold-start mitigation, SSE anti-buffering headers, and Secret Manager over .env. Use when prepping a first production deploy, debugging a stream that hangs behind a proxy, or diagnosing p99 latency spikes. Trigger with "langchain deploy", "langchain cloud run", "langchain vercel python", "langchain langserve", or "langchain docker".
langchain-langgraph-agents Build a correct LangGraph 1.0 ReAct agent with create_react_agent — typed tools, error propagation, recursion caps, and stop conditions that actually stop. Use when writing a first tool-calling agent, migrating from AgentExecutor or initialize_agent, or diagnosing an agent that loops on vague prompts. Trigger with "langgraph agent", "create_react_agent", "langgraph tool calling", "AgentExecutor migration", or "agent loop cost".
langchain-langgraph-human-in-loop Build LangGraph 1.0 human-in-the-loop approval flows with interrupt_before /
interrupt_after and Command(resume=...) — JSON-serializable state, clean
resume semantics, and UI wiring for approval decisions. Use when adding an
approval gate before an expensive tool call, wiring a Slack/web UI for agent
approvals, or debugging a graph that crashes on interrupt.
Trigger with "langgraph human in loop", "langgraph interrupt_before",
"langgraph approval flow", "Command resume", "langgraph HITL".
name perplexity-policy-guardrails description Implement content moderation, model selection policy, citation quality enforcement,
and per-user usage quotas for Perplexity Sonar API.
Trigger with phrases like "perplexity policy", "perplexity guardrails",
"perplexity content moderation", "perplexity usage limits", "perplexity safety".
allowed-tools Read, Write, Edit, Bash(npx:*) version 1.12.0 license MIT author Jeremy Longshore <jeremy@intentsolutions.io> tags ["saas","perplexity","perplexity-policy"] compatibility Designed for Claude Code
Perplexity Policy Guardrails
Overview
Policy enforcement for Perplexity Sonar API. Since Perplexity performs live web searches, guardrails must address: query content moderation (what users can search for), citation reliability (filtering low-quality sources), cost control (model selection + token limits), and responsible AI usage.
Policy Pipeline
User Query
│
▼
Query Moderation (block harmful queries)
│
▼
PII Sanitization (strip personal data)
│
▼
Quota Check (daily limit by user tier)
│
▼
Model Selection (enforce tier-appropriate model)
│
▼
Perplexity API Call
│
▼
Citation Quality Scoring (filter low-trust sources)
│
▼
Response to User
Prerequisites
Perplexity API configured
Content moderation policy defined
User tier system in place
Redis for quota tracking (optional: in-memory for simple apps)
Instructions
Step 1: Query Content Moderation
const BLOCKED_PATTERNS = [
/\b(write|generate|create)\s+(malware|virus|exploit|ransomware)\b/i ,
/\b(personal|private)\s+(address|phone|ssn)\s+of\s+\w+/i ,
/\b(bypass|circumvent|hack)\s+(security|firewall|authentication)\b/i ,
/\b(how to|tutorial)\s+(stalk|dox|harass)\b/i ,
];
const MAX_QUERY_LENGTH = 2000 ;
class PolicyError extends Error {
constructor (public code : string , message : string super (message);
this .name = "PolicyError" ;
}
}
function moderateQuery (query : string string
if
length
MAX_QUERY_LENGTH
throw
new
PolicyError
"QUERY_TOO_LONG"
`Query exceeds ${MAX_QUERY_LENGTH} characters`
for
const
of
BLOCKED_PATTERNS
if
test
throw
new
PolicyError
"CONTENT_BLOCKED"
"Query blocked by content policy"
return
Step 2: Model Selection Policy interface ModelPolicy {
model : string ;
maxTokens : number ;
costPerRequest : number ;
}
const MODEL_POLICIES : Record <string , ModelPolicy > = {
free : { model : "sonar" , maxTokens : 256 , costPerRequest : 0.005 },
basic : { model : "sonar" , maxTokens : 1024 , costPerRequest : 0.005 },
pro : { model : "sonar-pro" , maxTokens : 2048 , costPerRequest : 0.02 },
enterprise : { model : "sonar-pro" , maxTokens : 4096 , costPerRequest : 0.02 },
};
function enforceModelPolicy (
userTier : string ,
requestedModel ?: string
): ModelPolicy {
const policy = MODEL_POLICIES [userTier] || MODEL_POLICIES .free ;
if (requestedModel === "sonar-pro" && !["pro" , "enterprise" ].includes (userTier)) {
console .warn (`User tier ${userTier} not allowed sonar-pro, using sonar` );
return MODEL_POLICIES .free ;
}
return requestedModel ? { ...policy, model : requestedModel } : policy;
}
Step 3: Per-User Usage Quotas class UsageQuota {
private usage : Map <string , { count : number ; resetAt : number }> = new Map ();
private readonly limits : Record <string , number > = {
free : 50 ,
basic : 200 ,
pro : 1000 ,
enterprise : 5000 ,
};
check (userId : string , tier : string = "free" ): void {
const key = `${userId} :${new Date ().toISOString().slice(0 , 10 )} ` ;
const entry = this .usage .get (key) || { count : 0 , resetAt : this .endOfDay () };
if (Date .now () > entry.resetAt ) {
entry.count = 0 ;
entry.resetAt = this .endOfDay ();
}
const limit = this .limits [tier] || this .limits .free ;
if (entry.count >= limit) {
throw new PolicyError (
"QUOTA_EXCEEDED" ,
`Daily quota exceeded (${entry.count} /${limit} ). Resets at midnight UTC.`
);
}
entry.count ++;
this .usage .set (key, entry);
}
getUsage (userId : string ): { used : number ; limit : number ; remaining : number } {
const key = `${userId} :${new Date ().toISOString().slice(0 , 10 )} ` ;
const entry = this .usage .get (key);
const used = entry?.count || 0 ;
return { used, limit : 50 , remaining : Math .max (0 , 50 - used) };
}
private endOfDay (): number {
const d = new Date ();
d.setUTCHours (23 , 59 , 59 , 999 );
return d.getTime ();
}
}
Step 4: Citation Quality Scoring const TRUSTED_TLDS = new Set (["gov" , "edu" , "org" ]);
const HIGH_QUALITY_DOMAINS = new Set ([
"nature.com" , "science.org" , "arxiv.org" , "wikipedia.org" ,
"nih.gov" , "cdc.gov" , "who.int" ,
]);
const LOW_QUALITY_DOMAINS = new Set ([
"reddit.com" , "quora.com" , "medium.com" , "yahoo.com" ,
]);
interface CitationQuality {
url : string ;
trust : "high" | "medium" | "low" ;
domain : string ;
}
function scoreCitations (citations : string []scored : CitationQuality [];
highTrustPercent : number ;
} {
const scored = citations.map ((url ) => {
const domain = new URL (url).hostname ;
const tld = domain.split ("." ).pop () || "" ;
let trust : "high" | "medium" | "low" = "medium" ;
if (TRUSTED_TLDS .has (tld) || HIGH_QUALITY_DOMAINS .has (domain)) {
trust = "high" ;
} else if (LOW_QUALITY_DOMAINS .has (domain)) {
trust = "low" ;
}
return { url, trust, domain };
});
const highTrust = scored.filter ((s ) => s.trust === "high" ).length ;
return {
scored,
highTrustPercent : citations.length > 0 ? highTrust / citations.length : 0 ,
};
}
Step 5: Full Policy Pipeline const quota = new UsageQuota ();
async function policiedSearch (
query : string ,
userId : string ,
userTier : string = "free" ,
requestedModel ?: string
) {
const moderated = moderateQuery (query);
const { clean } = sanitizeQuery (moderated);
quota.check (userId, userTier);
const policy = enforceModelPolicy (userTier, requestedModel);
const response = await perplexity.chat .completions .create ({
model : policy.model ,
messages : [{ role : "user" , content : clean }],
max_tokens : policy.maxTokens ,
});
const citations = (response as any ).citations || [];
const quality = scoreCitations (citations);
return {
answer : response.choices [0 ].message .content ,
citations : quality.scored ,
citationQuality : quality.highTrustPercent ,
model : response.model ,
tokens : response.usage ?.total_tokens ,
};
}
Error Handling Issue Cause Solution Query blocked Content moderation triggered Review patterns, adjust if false positive Quota exceeded User hit daily limit Upgrade tier or wait for reset Model downgraded User tier restricts access Inform user of tier limitations Low citation quality All sources from forums Add search_domain_filter for trusted sources
Output
Query content moderation with blocked patterns
Model selection enforced by user tier
Per-user daily quotas
Citation quality scoring and filtering
Full policy pipeline combining all layers
Resources
Next Steps For architecture patterns, see perplexity-architecture-variants.
