From serpapi-pack
Optimizes SerpApi costs using Python strategies: Redis caching, archive retrieval, Google Light API, reduced results, and monthly cost calculator.
npx claudepluginhub jeremylongshore/claude-code-plugins-plus-skills --plugin serpapi-packThis skill is limited to using the following tools:
SerpApi charges per search (1 credit each). Plans: Free (100/mo), Developer ($75, 5K/mo), Business ($200, 15K/mo), Enterprise (custom). Key savings: caching, archive retrieval (free), and Google Light API.
Manages SerpApi rate limits and credits with monitoring, Python/Node.js throttling, and cached search retrieval to optimize API usage.
Optimizes Exa search API costs via type selection, result caching, query deduplication, and usage monitoring. For billing analysis, cost reduction, and budget controls.
Optimizes Firecrawl API costs with crawl limits, map-then-scrape, targeted batch scraping, and markdown formats. For billing analysis, cost reduction, and credit budget alerts.
Share bugs, ideas, or general feedback.
SerpApi charges per search (1 credit each). Plans: Free (100/mo), Developer ($75, 5K/mo), Business ($200, 15K/mo), Enterprise (custom). Key savings: caching, archive retrieval (free), and Google Light API.
# Search results rarely change within an hour
# Cache for 1 hour = up to 24x credit reduction for hourly queries
# Cache for 1 day = up to 720x for queries checked every 2 minutes
import hashlib, json, redis, serpapi, os
r = redis.Redis.from_url(os.environ["REDIS_URL"])
client = serpapi.Client(api_key=os.environ["SERPAPI_API_KEY"])
def cached_search(ttl_seconds=3600, **params):
key = f"serpapi:{hashlib.md5(json.dumps(params, sort_keys=True).encode()).hexdigest()}"
cached = r.get(key)
if cached:
return json.loads(cached) # FREE: no credit consumed
result = client.search(**params) # 1 credit
r.setex(key, ttl_seconds, json.dumps(dict(result)))
return result
# Every search result is stored in the archive
# Retrieve by search_id at no cost
archived = client.search(engine="google", search_id="previous_id")
# 0 credits -- use for re-processing or delayed access
# Same 1 credit but faster response (~1s vs 3-5s)
# Good for: organic results only, no knowledge graph needed
result = client.search(engine="google_light", q="query")
# Default num=10 (10 results). If you only need top 3:
result = client.search(engine="google", q="query", num=3)
# Still 1 credit, but faster response
def estimate_monthly_cost(
daily_searches: int,
cache_hit_rate: float = 0.7, # 70% cache hits typical
) -> dict:
actual_api_calls = daily_searches * 30 * (1 - cache_hit_rate)
plans = [
("Free", 100, 0), ("Developer", 5000, 75),
("Business", 15000, 200), ("Enterprise", 50000, 500),
]
for name, limit, price in plans:
if actual_api_calls <= limit:
return {"plan": name, "price": f"${price}/mo",
"api_calls": int(actual_api_calls), "raw_searches": daily_searches * 30}
return {"plan": "Enterprise+", "price": "Custom", "api_calls": int(actual_api_calls)}
# Examples:
# 100 searches/day, 70% cache = 900 API calls/mo = Developer ($75)
# 500 searches/day, 80% cache = 3000 API calls/mo = Developer ($75)
# 1000 searches/day, 50% cache = 15000 API calls/mo = Business ($200)
# Daily credit check
curl -s "https://serpapi.com/account.json?api_key=$SERPAPI_API_KEY" | jq '{
plan: .plan_name,
used: .this_month_usage,
remaining: .plan_searches_left,
daily_avg: (.this_month_usage / ([1, (now | strftime("%d") | tonumber)] | max))
}'
| Issue | Cause | Solution |
|---|---|---|
| Unexpected costs | No caching | Implement Redis/LRU cache |
| Credits exhausted mid-month | Underestimated volume | Upgrade plan or increase cache TTL |
| Cache miss rate high | Short TTL | Increase cache TTL to 1-4 hours |
For architecture patterns, see serpapi-reference-architecture.