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-research-agentnpx skills add scraperapi/scraperapi-skills --skill scraperapi-research-agentgit clone --depth 1 https://github.com/scraperapi/scraperapi-skillsWhat 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 | $0.00188 | $0.01369 |
| Opus 5 | $0.00094 | $0.00685 |
| Sonnet 5 | $0.00038 | $0.00274 |
| Haiku 4.5 | $0.00019 | $0.00137 |
Grade A, and why
scraperapi-research-agent 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 2d 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 — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ScraperAPI Research Agent
End-to-end autonomous research: ScraperAPI finds and fetches sources → Anthropic Files API ingests them as cited documents → Claude synthesizes a report.
Run it:
# Install dependencies
pip install requests anthropic
# Set env vars
export SCRAPERAPI_API_KEY=your-key
export ANTHROPIC_API_KEY=your-key
# Run
python skills/scraperapi-research-agent/scripts/research_agent.py \
--question "What are the best practices for rate limiting in web APIs?" \
--max-sources 5 \
--output report.md
See scripts/research_agent.py for the full implementation.
Planning Checklist
Before starting a research run, establish:
- Question clarity — Is the question specific enough to produce useful search queries? Vague questions like "tell me about AI" produce noise. Better: "What are the tradeoffs between RAG and fine-tuning for domain-specific LLMs?"
- Source count — How many sources are needed? 3–5 is usually sufficient for a factual summary; 8–10 for a comparative analysis. More sources = more ScraperAPI credits.
- Recency — Does the answer depend on recent events? Search queries will use recent date filters.
- Credit budget — Each source costs ~1 credit to scrape (more with JS rendering). 5 sources = ~5–10 credits total.
- Stop condition — Define when to stop. The default stop is
--max-sources(5). Do not loop indefinitely.
Research Loop
1. PLAN
↓ Claude decomposes the question into 2–3 targeted search queries
2. DISCOVER
↓ ScraperAPI google/search structured endpoint → list of (url, title, snippet)
3. DEDUPLICATE
↓ Filter to top N unique URLs (default: 5), skipping PDFs and low-quality domains
4. FETCH
↓ ScraperAPI scrape each URL as markdown (output_format=markdown)
↓ Skip pages returning < 200 characters (blocked, error pages)
5. UPLOAD
↓ Upload each scraped page to Anthropic Files API as a text/plain artifact
↓ Store file_id for each source
6. SYNTHESIZE
↓ Claude (claude-opus-4-8, adaptive thinking) reads all document artifacts
↓ Returns structured report with inline citations [1], [2]...
7. CLEAN UP
↓ Delete uploaded file artifacts from Anthropic
↓ Write or print the final report
STOP when: max_sources reached, or all queries exhausted (whichever comes first).
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2d ago First seen · 157 lines · 188 tokens per session scan A f26bc92d6aac
scraperapi-research-agent is a skill published in the GitHub repository scraperapi/scraperapi-skills (10 stars, last pushed 26d ago), licensed MIT. It adds 188 tokens to every session and 1,369 once invoked, about $0.0009 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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