scraperapi-scraper-builder Skill
Erstellt vollständige, produktionsreife Python- oder Node.js-Webscraping-Skripte mit ScraperAPI, inklusive integriertem Error Handling und Pagination.
- GitHub Stars
- 10
- Dateien
- 2
- Letzter Commit
- 7. Aug. 2026
- Lizenz
- MIT
- Erlaubte Tools
- —
Was macht der scraperapi-scraper-builder Skill?
scraperapi-scraper-builder ist ein Agent Skill. Erstellt vollständige, produktionsreife Python- oder Node.js-Webscraping-Skripte mit ScraperAPI, inklusive integriertem Error Handling und Pagination.
Zusammenfassung erzeugt aus der SKILL.md des Skills.
Über den scraperapi-scraper-builder skill
Entwickler, die Datenextraktion automatisieren müssen, können diesen Skill nutzen, um produktionsreife Webscraping-Skripte in Python oder Node.js zu generieren. Er automatisiert den gesamten Entwicklungszyklus, indem er zuerst eine Site-Reconnaissance durchführt, um das Verhalten der Zielwebsite zu analysieren. Durch das Abrufen von Beispielseiten via ScraperAPI identifiziert das Tool, ob eine Seite Anti-Bot-Schutzmaßnahmen wie Cloudflare nutzt oder JavaScript-Rendering benötigt, um Inhalte anzuzeigen.
Sobald das Website-Profil erstellt ist, trifft der Skill architektonische Entscheidungen bezüglich der Proxy-Tiers, wie zum Beispiel Premium- oder Ultra-Premium-Einstellungen, und bestimmt die beste Pagination-Strategie. Er erstellt ein vollständiges, ausführbares Skript, das integriertes Error Handling, eine Exponential-Backoff-Retry-Logik und eine Credit-Schätzung enthält, um Budgetüberschreitungen zu vermeiden. Der resultierende Code ist auf das spezifische Ziel zugeschnitten und nutzt die identifizierten CSS-Selektoren oder internen API-Endpoints, um eine zuverlässige Datenextraktion zu gewährleisten.
Was ein agent damit machen kann
- 1Einen eigenen E-Commerce-Scraper bauenGeneriere ein Python-Skript, um Produktdetails und Preise von einem bestimmten Online-Händler zu extrahieren.
- 2Komplexes JavaScript-Rendering handhabenErstelle einen Scraper für eine Single-Page-Application, die JavaScript benötigt, um ihre Datencontainer zu laden.
- 3Großflächige Datensammlung automatisierenImplementiere einen asynchronen Batch-Job, um hunderte von URLs effizient zu scrapen.
Geschrieben basierend auf dem SKILL.md des skills. Jeder use case wurde gegen ein zitat daraus geprüft.
So installierst du den scraperapi-scraper-builder Skill
Jeder Befehl kopiert den Skill-Ordner in das Skills-Verzeichnis des Clients. Starte den Client neu oder beginne eine neue Session, um ihn zu laden.Den scraperapi-scraper-builder Skill in Claude Code installieren
~/.claude/skills/scraperapi-scraper-builder/SKILL.md
$ npx degit scraperapi/scraperapi-skills/skills/scraperapi-scraper-builder ~/.claude/skills/scraperapi-scraper-builderDen scraperapi-scraper-builder Skill in Claude Code (project) installieren
.claude/skills/scraperapi-scraper-builder/SKILL.md
$ npx degit scraperapi/scraperapi-skills/skills/scraperapi-scraper-builder .claude/skills/scraperapi-scraper-builderDen scraperapi-scraper-builder Skill in Codex installieren
~/.agents/skills/scraperapi-scraper-builder/SKILL.md
$ npx degit scraperapi/scraperapi-skills/skills/scraperapi-scraper-builder ~/.agents/skills/scraperapi-scraper-builderDen scraperapi-scraper-builder Skill in Gemini CLI installieren
~/.gemini/skills/scraperapi-scraper-builder/SKILL.md
$ npx degit scraperapi/scraperapi-skills/skills/scraperapi-scraper-builder ~/.gemini/skills/scraperapi-scraper-builderDen scraperapi-scraper-builder Skill in Claude.ai installieren
npx degit scraperapi/scraperapi-skills/skills/scraperapi-scraper-builder scraperapi-scraper-builder && zip -r scraperapi-scraper-builder.zip scraperapi-scraper-builder # claude.ai → Customize → Skills → + → Create skill → Upload a skill → scraperapi-scraper-builder.zip
SKILL.md
Auf GitHub ansehenScraperAPI Scraper Builder
Build production-quality scrapers using ScraperAPI. Work through four phases: gather requirements, probe the target site, make architecture decisions, then generate a complete runnable script.
Phase 1 — Gather Requirements
Before writing code, collect the following. Pull from the user's prompt; ask only what is missing.
| Info | Default if not specified |
|---|---|
| Target URL or website | Required — ask if missing |
| Data fields to extract | Ask if vague ("product info" → which fields exactly?) |
| Volume | Infer: single URL / paginated / bulk list of URLs |
| Language | Ask if not clear from context; Python is a reasonable default |
| Output format | stdout JSON |
| Geo-targeting needed? | Infer from site type; confirm for e-commerce pricing |
Phase 2 — Site Reconnaissance
Before making any architecture decisions, fetch 1–2 sample pages from the target site using ScraperAPI to observe actual behavior. This replaces guesswork with evidence and costs at most 2 credits.
What to fetch
Fetch at most two pages:
- A listing or category page (the main target) — e.g., a product grid, search results, or article index
- A detail page (only if the user needs data from individual items) — e.g., a single product, article, or profile
Always start with a standard request (no render, no premium) — the cheapest probe:
GET https://api.scraperapi.com/?api_key=<SCRAPERAPI_API_KEY>&url=<target_url>
What to observe
1. Response status
| Status | Meaning |
|---|---|
| 200 | Proceed to content analysis |
403 + body has "Just a moment", cf-ray, or "Cloudflare" |
Cloudflare detected |
| 403 + body has "DataDome", "PerimeterX", or "Akamai" | Bot manager detected |
| 403 (generic) | Anti-bot protection present; premium proxies likely needed |
| 429 | Rate limited; note for architecture phase |
2. Content completeness (for 200 responses)
Signals that render=true will be needed:
- Empty SPA containers:
<div id="root"></div>,<div id="app"></div>,<div id="__next"></div>with no children - Framework markers in
<script>tags:_next,__nuxt__,react,vue,angular - Response is mostly
<script>and<div>tags with little visible text - Target data fields (from Phase 1) are absent from the HTML
If the target data is visibly present in the raw HTML → standard rendering is sufficient, do not enable render.
3. Pagination structure
Look for the site's pagination scheme in <a> hrefs and link tags:
<a rel="next">or<link rel="next">→ follow-the-next-link pattern?page=N,?p=N,/page/N/in hrefs → page-number pagination?start=Nor?offset=N→ offset-based pagination- No pagination links +
data-infinite-scrollor "Load more" button → JS-driven infinite scroll (requiresrender=true)
Note the exact URL pattern — this feeds directly into the pagination loop in Phase 4.
4. Data structure (when target fields are visible)
- Identify the repeating container element (e.g.,
<li class="product-item">,<div class="listing-card">) - Note CSS selectors for each target field from Phase 1
- Check for embedded structured data:
<script type="application/ld+json">(JSON-LD) ordata-*attributes — these are often cleaner to parse than raw HTML
5. Internal API endpoints (bonus)
Scan inline <script> blocks for fetch( or axios.get( calls pointing to internal paths (e.g., /api/products, /_next/data/). If found, scraping that JSON endpoint is usually simpler and more stable than parsing HTML.
Site Profile — output of this phase
Summarize findings as a table before proceeding. Every row maps to a specific decision in Phase 3.
| Signal | Observed |
|-------------------------|---------------------------------------|
| HTTP status (standard) | 200 / 403 / ... |
| Anti-bot protection | None / Cloudflare / DataDome / ... |
| JS rendering needed | Yes / No / Uncertain |
| Pagination pattern | ?page=N / rel=next / load-more / ... |
| Target data visible | Yes / No (skeleton HTML) |
| Data container selector | e.g. div.product-card |
| Structured data (LD+J) | Present / Not found |
| Internal API detected | Yes (<path>) / No |
Phase 3 — Architecture Decisions
Use the Site Profile from Phase 2 as primary evidence for each decision. Work through all six decisions in order, citing specific observations from the profile. Summarize all decisions as a table (see end of this section) before writing any code.
Decision 1 — Is there a Structured Data endpoint?
Check if the target site is one of the supported verticals. If yes, use it — structured endpoints return clean JSON with no HTML parsing, and they handle rendering and anti-bot automatically. Skip Decisions 2 and 3.
Supported verticals (verify against docs):
- Amazon:
product,search,offers,reviews - Google:
search,news,jobs,shopping,maps - Walmart:
product,search,category - eBay:
product,search - Redfin:
listing,search - Indeed:
jobs,job - LinkedIn:
profile,jobs(availability may vary)
Endpoint pattern: GET https://api.scraperapi.com/structured/<site>/<type>?api_key=...&<required_param>=...
Decision 2 — Does the page require JavaScript rendering?
Enable render=true only when the Site Profile confirms that target data was absent in the standard response. Cost: ~10 credits/req.
- Site Profile says "Target data visible: No" or "JS rendering needed: Yes" → enable
render=true - Site Profile says "Target data visible: Yes" → do not enable
render=true - Site Profile says "Uncertain" → fetch one more sample with
render=trueand compare
Do not enable render=true speculatively — the Phase 2 probe is the evidence.
Decision 3 — Does the site have anti-bot protection?
Use the Site Profile's "Anti-bot protection" row as the starting point. Escalate gradually — never start at the highest tier.
- Site Profile says "None" → start with standard
- Site Profile says "Cloudflare" or any bot manager → start with
premium=true(~10 credits) premium=truestill returns 403 → escalate toultra_premium=true(~30 credits)
ultra_premium=true and premium=true are mutually exclusive — never set both.
Decision 4 — What is the volume?
| Volume | Approach |
|---|---|
| 1–10 URLs | Sync loop, no special handling |
| 10–100 URLs | Sync with 1 req/sec rate limiting |
| >100 URLs | Async Jobs API (POST https://async.scraperapi.com/batchjobs) |
For async batches, the script submits all URLs, then polls until all jobs complete. See references/code-templates.md for the async pattern.
Decision 5 — Is session state needed?
Add session_number=<int> when:
- The scraper must follow a login flow
- Multi-step checkout or auth-gated pages
- Pagination state must be maintained across requests
Pick any integer as the session ID. Requests with the same session_number are routed through the same proxy IP.
Decision 6 — Is geo-targeting needed?
Add country_code (ISO-3166, e.g., us, gb, de) when:
- Scraping prices that vary by region
- Target site redirects or blocks based on geography
- SERP results should be localized
Architecture summary table
Before generating code, print this table:
| Parameter | Value | Reason |
|----------------|------------|-----------------------------------|
| url | ... | target |
| render | true/false | (why) |
| premium | true/false | (why) |
| ultra_premium | true/false | (why) |
| country_code | us / – | (why) |
| session_number | N / – | (why) |
| volume mode | sync/async | (why) |
Phase 4 — Generate Code
Ask for language if not already known (default: Python). Generate a complete, runnable script — not pseudocode or snippets. See references/code-templates.md for the base templates to adapt.
Use the Site Profile from Phase 2 to pre-fill the parsing logic: if a data container selector was identified (e.g., div.product-card), write it directly into the extract() function rather than leaving a TODO. If JSON-LD was found, parse <script type="application/ld+json"> instead of navigating the HTML tree. If an internal API path was found, target that endpoint instead of the page HTML.
Python script structure
argparseCLI with--url,--pages,--output,--max-credits, plus flags for all ScraperAPI params decided in Phase 3- API key from
os.environ["SCRAPERAPI_API_KEY"]— never hardcoded scrape()function with exponential backoff retry (5 attempts)- Status code handling per the error table below
- Credit estimate printed to stderr before any requests
- Pagination loop using the pattern identified in the Site Profile, with 1 req/sec rate limit for sync mode
extract()function pre-filled with selectors from the Site Profile (or a TODO if none were identified)- Output as JSON to stdout or
--outputfile
Node.js script structure
Same flags and behavior as above. Use node-fetch@2 + commander. Match the user's existing module format (ESM vs. CommonJS) if known, otherwise default to CommonJS.
Always include at the top of the generated script
# Requirements: pip install requests (Python)
# Requirements: npm install node-fetch@2 commander (Node.js)
# Usage: SCRAPERAPI_API_KEY=your_key python scraper.py --url "https://example.com" --pages 5
Credit Cost Reference
| Configuration | Credits per request |
|---|---|
| Standard | 1 |
render=true |
~10 |
premium=true |
~10 |
ultra_premium=true |
~30 |
| Structured endpoint | 1–10 (varies by vertical) |
Print a cost estimate before executing: # Estimated: 50 pages × 10 credits = ~500 credits
The generated script must include a --max-credits flag that aborts if the estimate exceeds the budget.
Error Handling
| Status | Action |
|---|---|
| 200 | Parse and return |
| 401 | Abort — invalid API key, do not retry |
| 403 | Retry with premium=true; if still blocked, try ultra_premium=true |
| 404 | Skip this URL — page does not exist |
| 429 | Exponential backoff and retry; switch to async for large batches |
| 500/503 | Exponential backoff, max 5 attempts |
Validation Checklist
Before presenting the script:
- Site Profile was produced in Phase 2 and each architecture decision cites it
- API key is read from
$SCRAPERAPI_API_KEYenv var — not hardcoded - Retry logic covers 429, 500, 503 with exponential backoff
-
--max-creditsguard is present and enforced before the first request - Credit estimate is printed to stderr before any requests are made
- If using a structured endpoint: vertical name matches the cheatsheet exactly
- If async: script includes polling loop or webhook handling, not just job submission
-
ultra_premiumandpremiumare not both set - Setup instructions comment is at the top of the script
- A TODO comment marks where the user should add their parsing logic
References
- references/code-templates.md — Python and Node.js base templates (sync + async)
- ScraperAPI docs
- Structured Data endpoints
- Async API
- Parameter reference
- Dashboard
Dateien in diesem Skill
2scraperapi-scraper-builder Skill-Fragen
Wie installiere ich den scraperapi-scraper-builder Skill in Claude Code?
Führe npx degit scraperapi/scraperapi-skills/skills/scraperapi-scraper-builder ~/.claude/skills/scraperapi-scraper-builder aus. Claude Code lädt Skills aus ~/.claude/skills (alle Projekte) oder .claude/skills (ein Projekt) und nutzt einen Skill, wenn seine Beschreibung zur Aufgabe passt.
Welche Clients können den scraperapi-scraper-builder Skill nutzen?
Jeder Client, der das Agent Skills-Format liest: Claude Code, Codex, Gemini CLI und claude.ai (als ZIP hochladen). Skripte im Skill laufen nur dort, wo der Client Codeausführung erlaubt.
Welche Programmiersprachen können die generierten Scraper nutzen?
Der Skill kann vollständige, ausführbare Skripte in Python oder Node.js generieren.
Wie entscheidet das Tool, ob JavaScript-Rendering notwendig ist?
Es führt eine Site-Reconnaissance durch, indem es Beispielseiten abruft, um nach leeren SPA-Containern oder Framework-Markern wie React oder Vue zu suchen.