Agent skill · data analytics · jeremylongshore
serpapi-cost-tuning
Reduce SerpAPI search consumption through measured demand, exact-query caching, admission budgets, and current account pricing evidence. Use when forecasting or controlling search spend. Trigger with "optimize SerpAPI cost".
Why this skill is useful
Adds executable scripts for caching and cost estimation that optimize SerpApi usage and reduce costs significantly.
What it needs
Requires jq, redis installed locally. Requires serpapi account access. About 2k tokens when loaded. Last updated 2026-08-07. 2,604 stars on the source repository.
What this skill does
SerpAPI Usage and Cost Governance Overview Forecast from observed search demand and the account's current contract, then reduce waste without inventing static plan economics. Prerequisites Current pricing page and Account API snapshot Search attempts, server/application cache hits, engine mix, retries, and business outcome volume Product freshness requirements and owners for finance, product, and the SerpAPI account Tool Discipline Use Read, Glob, and Grep to inventory callers, cache behavior, and retry amplification, WebFetch to verify current public pricing and cache rules, and Write or Edit for forecasts, budgets, dashboards, and safe optimizations. Current Contract Plans, prices, included searches, and hourly throughput can change. Account API exposes the subscribed plan, searches left, usage, renewal date, and throughput. Exactly matching cached searches can be served from SerpAPI's one-hour cache for free; nocache=true opts out and can increase consumption. Authentication Read account facts with the server-side SERPAPIKEY, but do not expose the key, account identity, or commercial details in public dashboards or receipts. Instructions 1. Enumerate every producer and separate user-valued searches from retries, duplicate requests, tests, previews, and abandoned work. 2. Capture a dated Account API snapshot and current pricing evidence; do not embed public list prices as durable code constants. 3. Forecast searches through the next renewal using observed volume, seasonality, cache hit rate, pagination, and failure amplification. 4. Rank optimizations: remove duplicate calls, normalize exact-match parameters, enable application caching, stop unnecessary pagination, gate low-value work, and eliminate uncontrolled retries. 5. Preserve freshness and correctness requirements; never claim that reducing result count reduces the number of searches without current evidence. 6. Model base, expected, and peak scenarios with headroom and an explicit assumption register. 7. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills jeremylongshore/serpapi-cost-tuning--9f3b5e