Claude-code-plugins-plus-skills perplexity-known-pitfalls
install
source · Clone the upstream repo
git clone https://github.com/jeremylongshore/claude-code-plugins-plus-skills
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/jeremylongshore/claude-code-plugins-plus-skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/plugins/saas-packs/perplexity-pack/skills/perplexity-known-pitfalls" ~/.claude/skills/jeremylongshore-claude-code-plugins-plus-skills-perplexity-known-pitfalls && rm -rf "$T"
manifest:
plugins/saas-packs/perplexity-pack/skills/perplexity-known-pitfalls/SKILL.mdsource content
Perplexity Known Pitfalls
Overview
Real gotchas when integrating Perplexity Sonar API. Perplexity uses an OpenAI-compatible chat endpoint but performs live web searches -- a fundamentally different paradigm from standard LLM completions. These pitfalls come from treating it like a regular chatbot.
Prerequisites
- Perplexity API key configured
- Understanding of OpenAI-compatible chat API format
Pitfalls
1. Using It as a Generic Chatbot
Perplexity searches the web per request. Using it for tasks that don't need web search wastes money.
# BAD: general chatbot (wastes a search query) response = call_perplexity("Write me a haiku about cats") # Costs $0.005+ for something any LLM can do offline # GOOD: leverage web search capability response = call_perplexity( "What are the latest Next.js 15 features released this month?", search_recency_filter="month" )
2. Ignoring Citations
Perplexity returns
[1], [2] markers in text with a separate citations array. Ignoring them loses the key value prop.
data = response.model_dump() # or response.json() for raw HTTP answer = data["choices"][0]["message"]["content"] citations = data.get("citations", []) # NOT in choices — top-level field # BAD: displaying raw markers print(answer) # "According to [1], Node.js 22 adds..." # GOOD: replace markers with links import re for i, url in enumerate(citations, 1): answer = answer.replace(f"[{i}]", f"[{i}]({url})")
3. Using Wrong SDK Import
There is no
@perplexity/sdk or perplexity Python package. Use the standard OpenAI client.
// BAD — this package doesn't exist import { PerplexityClient } from "@perplexity/sdk"; // GOOD — use OpenAI client with Perplexity base URL import OpenAI from "openai"; const client = new OpenAI({ apiKey: process.env.PERPLEXITY_API_KEY, baseURL: "https://api.perplexity.ai", });
4. Not Setting max_tokens
Without
max_tokens, responses can be arbitrarily long, increasing costs unpredictably.
// BAD: no token limit — output cost can spike await client.chat.completions.create({ model: "sonar-pro", // $15/M output tokens! messages: [{ role: "user", content: "Tell me about AI" }], }); // GOOD: always set max_tokens await client.chat.completions.create({ model: "sonar-pro", messages: [{ role: "user", content: "Tell me about AI" }], max_tokens: 1024, });
5. No Recency Filter for Time-Sensitive Queries
Without
search_recency_filter, Perplexity may cite outdated articles.
# BAD: may return articles from any time period response = call_perplexity("current Bitcoin price") # GOOD: constrain to recent results response = call_perplexity( "current Bitcoin price", search_recency_filter="day" # hour | day | week | month )
6. Sending Full Conversation History
Each message in the conversation may trigger new search queries. Sending 20 turns of history is expensive and slow.
# BAD: 20 turns of history = many search queries messages = long_history + [{"role": "user", "content": "summarize"}] # GOOD: summarize context, send focused query messages = [ {"role": "system", "content": "Answer based on web search."}, {"role": "user", "content": f"Context: {summary}\nQuestion: {question}"} ]
7. Using sonar-pro for Simple Queries
sonar-pro costs 3-15x more than sonar. Using it for simple factual lookups wastes budget.
// BAD: sonar-pro for a trivial question await client.chat.completions.create({ model: "sonar-pro", // $3 input + $15 output per M tokens messages: [{ role: "user", content: "What is the capital of France?" }], }); // GOOD: match model to complexity const model = isComplexQuery(query) ? "sonar-pro" : "sonar";
8. Mixing Allowlist and Denylist in Domain Filter
search_domain_filter supports either allowlist (include) or denylist (exclude with - prefix), but not both in the same request.
// BAD: mixing modes search_domain_filter: ["python.org", "-reddit.com"] // ERROR // GOOD: pick one mode search_domain_filter: ["python.org", "docs.python.org"] // Allowlist // OR search_domain_filter: ["-reddit.com", "-quora.com"] // Denylist
9. Not Caching Search Results
Every uncached call performs a web search. At scale, duplicate queries burn budget.
// BAD: same query hits API every time app.get("/search", (req, res) => { const result = await client.chat.completions.create({ ... }); res.json(result); }); // GOOD: cache by query hash const cache = new LRUCache({ max: 1000, ttl: 3600_000 }); app.get("/search", (req, res) => { const key = hash(req.query.q); if (cache.has(key)) return res.json(cache.get(key)); const result = await client.chat.completions.create({ ... }); cache.set(key, result); res.json(result); });
10. Wrong Base URL
The API is at
api.perplexity.ai, not api.perplexity.com.
// BAD baseURL: "https://api.perplexity.com" // Wrong domain // GOOD baseURL: "https://api.perplexity.ai" // Correct
Code Review Checklist
- Uses
package, not fakeopenai@perplexity/sdk - Base URL is
https://api.perplexity.ai -
set on every requestmax_tokens - Citations parsed from
arrayresponse.citations -
used for time-sensitive queriessearch_recency_filter - Caching implemented for repeated queries
- Model routing: sonar for simple, sonar-pro for complex
- Conversation history trimmed before sending
- PII sanitized from queries
- Domain filter uses only allowlist OR denylist, not both
Error Handling
| Pitfall | Impact | Detection |
|---|---|---|
| No caching | 3-5x cost overrun | Check cache hit rate metric |
| Wrong model | Budget waste | Grep for in simple query paths |
| No max_tokens | Unpredictable costs | Grep for calls without |
| PII in queries | Privacy violation | Run sanitization check in CI |
Output
- Identified anti-patterns in existing code
- Applied fixes for each pitfall
- Code review checklist for ongoing quality