Web Scraper

Overview

Web scraping inteligente multi-estrategia. Extrai dados estruturados de paginas web (tabelas, listas, precos). Paginacao, monitoramento e export CSV/JSON.

When to Use This Skill

• When the user mentions "scraper" or related topics
• When the user mentions "scraping" or related topics
• When the user mentions "extrair dados web" or related topics
• When the user mentions "web scraping" or related topics
• When the user mentions "raspar dados" or related topics
• When the user mentions "coletar dados site" or related topics

Do Not Use This Skill When

• The task is unrelated to web scraper
• A simpler, more specific tool can handle the request
• The user needs general-purpose assistance without domain expertise

How It Works

Execute phases in strict order. Each phase feeds the next.

1. CLARIFY  ->  2. RECON  ->  3. STRATEGY  ->  4. EXTRACT  ->  5. TRANSFORM  ->  6. VALIDATE  ->  7. FORMAT

Never skip Phase 1 or Phase 2. They prevent wasted effort and failed extractions.

Fast path: If user provides URL + clear data target + the request is simple (single page, one data type), compress Phases 1-3 into a single action: fetch, classify, and extract in one WebFetch call. Still validate and format.

Capabilities

• Multi-strategy: WebFetch (static), Browser automation (JS-rendered), Bash/curl (APIs), WebSearch (discovery)
• Extraction modes: table, list, article, product, contact, FAQ, pricing, events, jobs, custom
• Output formats: Markdown tables (default), JSON, CSV
• Pagination: auto-detect and follow (page numbers, infinite scroll, load-more)
• Multi-URL: extract same structure across sources with comparison and diff
• Validation: confidence ratings (HIGH/MEDIUM/LOW) on every extraction
• Auto-escalation: WebFetch fails silently -> automatic Browser fallback
• Data transforms: cleaning, normalization, deduplication, enrichment
• Differential mode: detect changes between scraping runs

Web Scraper

Multi-strategy web data extraction with intelligent approach selection, automatic fallback escalation, data transformation, and structured output.

Phase 1: Clarify

Establish extraction parameters before touching any URL.

Required Parameters

Optional Parameters

Clarification Rules

• If user provides a URL and clear data target, proceed directly to Phase 2. Do NOT ask unnecessary questions.
• If request is ambiguous (e.g. "scrape this site"), ask ONLY: "What specific data do you want me to extract from this page?"
• Default to Markdown table output. Mention alternatives only if relevant.
• Accept requests in any language. Always respond in the user's language.
• If user says "everything" or "all data", perform recon first, then present what's available and let user choose.

Discovery Mode

When user has a topic but no specific URL: 1. Use WebSearch to find the most relevant pages 2. Present top 3-5 URLs with descriptions 3. Let user choose which to scrape, or scrape all 4. Proceed to Phase 2 with selected URL(s)

Example: "find and extract pricing data for CRM tools" -> WebSearch("CRM tools pricing comparison 2026") -> Present top results -> User selects -> Extract

Phase 2: Reconnaissance

Analyze the target page before extraction.

Step 2.1: Initial Fetch

Use WebFetch to retrieve and analyze the page structure:

``` WebFetch( url = TARGET_URL, prompt = "Analyze this page structure and report: 1. Page type: article, product listing, search results, data table, directory, dashboard, API docs, FAQ, pricing page, job board, events, or other 2. Main content structure: tables, ordered/unordered lists, card grid, free-form text, accordion/collapsible sections, tabs 3. Approximate number of distinct data items visible 4. JavaScript rendering indicators: empty c