Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/scraperapi/scraperapi-skills/scraperapi-market-researchnpx skills add scraperapi/scraperapi-skills --skill scraperapi-market-researchgit clone --depth 1 https://github.com/scraperapi/scraperapi-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/scraperapi/scraperapi-skills/scraperapi-market-research)<a href="https://agentmods.dev/skills/scraperapi/scraperapi-skills/scraperapi-market-research"><img src="https://agentmods.dev/badge/skills/scraperapi/scraperapi-skills/scraperapi-market-research.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00119 | $0.08435 |
| Opus 5 | $0.00060 | $0.04217 |
| Sonnet 5 | $0.00024 | $0.01687 |
| Haiku 4.5 | $0.00012 | $0.00843 |
Grade A, and why
scraperapi-market-research scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 650 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Market Research
Live market research powered by ScraperAPI's web scraping infrastructure. Pulls structured data from search engines, product platforms, review sites, job boards, and any public URL — then synthesizes it into research-grade analysis.
You — Claude — execute the tool calls directly. Do not generate code for the user or explain how they could run these calls themselves. Call the MCP tools, read the results, and report findings. The user wants research, not instructions.
Start with the research question, not the data source. Before collecting anything, identify what decision the user is trying to inform. The same market looks completely different depending on whether you're trying to enter it, price against it, or understand why customers leave it.
Never answer market research questions from training knowledge alone. Always gather live data first, then analyze and synthesize. Training data has a cutoff; markets don't.
MCP Tools Available
| Tool | Best used for | Render needed? |
|---|---|---|
mcp__ScraperAPIRemote__google_search |
Discovering sources, mapping player presence, SERP analysis | No |
mcp__ScraperAPIRemote__google_news |
Trend signals, recent market moves, funding and launches | No |
mcp__ScraperAPIRemote__google_shopping |
Category pricing structure, product listings at scale | No |
mcp__ScraperAPIRemote__google_jobs |
Market-level hiring signals, technology bets, expansion patterns | No |
mcp__ScraperAPIRemote__scrape |
Any public URL — homepages, pricing pages, blog indexes, review pages | See note |
mcp__ScraperAPIRemote__amazon_product |
Product detail, ratings, review summaries | No |
mcp__ScraperAPIRemote__amazon_search |
Category structure, bestseller rankings, price distribution | No |
mcp__ScraperAPIRemote__amazon_offers |
Seller landscape, price variation across sellers | No |
mcp__ScraperAPIRemote__walmart_product |
Product detail and pricing | No |
mcp__ScraperAPIRemote__walmart_search |
Category structure and pricing on Walmart | No |
mcp__ScraperAPIRemote__walmart_review |
Consumer sentiment on physical products | No |
mcp__ScraperAPIRemote__ebay_product |
Secondary market pricing, product condition distribution | No |
mcp__ScraperAPIRemote__ebay_search |
Resale market structure and demand signals | No |
mcp__ScraperAPIRemote__crawler_job_start |
Deep research requiring many pages (blog indexes, full review sets) | — |
mcp__ScraperAPIRemote__crawler_job_status |
Check async crawl job status | — |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 650 lines · 119 tokens per session scan A 6978281aa719
scraperapi-market-research is a skill published in the GitHub repository scraperapi/scraperapi-skills (10 stars, last pushed 27d ago), licensed MIT. It adds 119 tokens to every session and 8,435 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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