FI

firecrawl-core-workflow-b

Use this skill for LLM-powered data extraction, batch scraping of multiple URLs, and rapid site discovery with Firecrawl.

Install

mkdir -p .claude/skills/firecrawl-core-workflow-b && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/7840" && unzip -o skill.zip -d .claude/skills/firecrawl-core-workflow-b && rm skill.zip

Installs to .claude/skills/firecrawl-core-workflow-b

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

Execute Firecrawl secondary workflow: LLM extraction, batch scraping,
69 charsno explicit “when” trigger
Intermediate

Key capabilities

  • Extract structured data using JSON schemas
  • Perform batch scraping of multiple URLs
  • Discover site structure using map endpoint
  • Execute async batch scraping for large URL sets
  • Filter and scrape site sections intelligently

How it works

It utilizes LLM-powered extraction to convert unstructured web content into typed JSON based on provided schemas. It also provides batching and mapping utilities to handle multiple pages and site discovery efficiently.

Inputs & outputs

You give it
Target URL and extraction schema
You get back
Typed JSON object

When to use firecrawl-core-workflow-b

  • Extract structured product pricing from web pages
  • Batch process a list of known URLs
  • Map entire website structures
  • Convert unstructured HTML to typed JSON

About this skill

Firecrawl Core Workflow B — Extract, Batch & Map

Overview

Secondary workflow complementing the scrape/crawl workflow. Covers LLM-powered structured data extraction with JSON schemas, batch scraping multiple known URLs, and rapid site map discovery. Use this when you need typed data rather than raw markdown.

Prerequisites

  • @mendable/firecrawl-js installed
  • FIRECRAWL_API_KEY environment variable set
  • Understanding of JSON Schema (for extract)

Instructions

Step 1: LLM Extract — Structured Data from Pages

import FirecrawlApp from "@mendable/firecrawl-js";

const firecrawl = new FirecrawlApp({
  apiKey: process.env.FIRECRAWL_API_KEY!,
});

// Extract structured data using an LLM + JSON schema
const result = await firecrawl.scrapeUrl("https://firecrawl.dev/pricing", {
  formats: ["extract"],
  extract: {
    schema: {
      type: "object",
      properties: {
        plans: {
          type: "array",
          items: {
            type: "object",
            properties: {
              name: { type: "string" },
              price: { type: "string" },
              credits_per_month: { type: "number" },
              features: { type: "array", items: { type: "string" } },
            },
            required: ["name", "price"],
          },
        },
      },
    },
  },
});

console.log("Extracted plans:", JSON.stringify(result.extract, null, 2));

Step 2: Extract with Prompt (No Schema)

// Use natural language prompt instead of rigid schema
const result = await firecrawl.scrapeUrl("https://news.ycombinator.com", {
  formats: ["extract"],
  extract: {
    prompt: "Extract the top 5 stories with their title, URL, points, and comment count",
  },
});

console.log(result.extract);

Step 3: Batch Scrape Known URLs

// Scrape multiple specific URLs at once — more efficient than individual calls
const batchResult = await firecrawl.batchScrapeUrls(
  [
    "https://docs.firecrawl.dev/features/scrape",
    "https://docs.firecrawl.dev/features/crawl",
    "https://docs.firecrawl.dev/features/extract",
    "https://docs.firecrawl.dev/features/map",
  ],
  {
    formats: ["markdown"],
    onlyMainContent: true,
  }
);

for (const page of batchResult.data || []) {
  console.log(`${page.metadata?.title}: ${page.markdown?.length} chars`);
}

Step 4: Async Batch Scrape (Large Sets)

// Start async batch scrape for many URLs — returns job ID
const job = await firecrawl.asyncBatchScrapeUrls(
  urls,  // array of 100+ URLs
  { formats: ["markdown"] }
);

// Poll for completion
let status = await firecrawl.checkBatchScrapeStatus(job.id);
while (status.status !== "completed") {
  await new Promise(r => setTimeout(r, 5000));
  status = await firecrawl.checkBatchScrapeStatus(job.id);
}

console.log(`Batch complete: ${status.data?.length} pages`);

Step 5: Map — Rapid URL Discovery

// Discover all URLs on a site in ~2-3 seconds
// Uses sitemap.xml + SERP + cached crawl data
const mapResult = await firecrawl.mapUrl("https://docs.firecrawl.dev");

const urls = mapResult.links || [];
console.log(`Discovered ${urls.length} URLs`);

// Categorize by section
const sections = {
  docs: urls.filter(u => u.includes("/docs/")),
  api: urls.filter(u => u.includes("/api-reference/")),
  features: urls.filter(u => u.includes("/features/")),
  other: urls.filter(u => !u.includes("/docs/") && !u.includes("/api-reference/")),
};

Object.entries(sections).forEach(([name, list]) => {
  console.log(`  ${name}: ${list.length} URLs`);
});

Step 6: Map + Selective Scrape Pipeline

// 1. Map to discover URLs, 2. Filter, 3. Batch scrape relevant ones
async function intelligentScrape(siteUrl: string, pathFilter: string) {
  const map = await firecrawl.mapUrl(siteUrl);
  const relevant = (map.links || []).filter(url => url.includes(pathFilter));

  console.log(`Map found ${map.links?.length} URLs, ${relevant.length} match filter`);

  if (relevant.length === 0) return [];
  if (relevant.length <= 10) {
    return firecrawl.batchScrapeUrls(relevant, { formats: ["markdown"] });
  }

  // For large sets, use async batch
  const job = await firecrawl.asyncBatchScrapeUrls(relevant.slice(0, 100), {
    formats: ["markdown"],
  });
  // ...poll for completion
  return job;
}

await intelligentScrape("https://docs.firecrawl.dev", "/features/");

Output

  • Typed JSON objects extracted from web pages
  • Batch scrape results for multiple URLs
  • Complete site URL map for discovery
  • Filtered scrape pipeline combining map + batch

Error Handling

ErrorCauseSolution
Empty extractPage content too complex for LLMSimplify schema, shorten prompt
Inconsistent extractionPrompt too longKeep prompts short and focused
Batch scrape timeoutToo many URLsUse async batch with polling
Map returns few URLsSite has no sitemap.xmlUse crawlUrl for thorough discovery
402 Payment RequiredCredits exhaustedReduce batch size, check balance

Examples

Extract Products from E-Commerce

const products = await firecrawl.scrapeUrl("https://store.example.com/products", {
  formats: ["extract"],
  extract: {
    schema: {
      type: "object",
      properties: {
        products: {
          type: "array",
          items: {
            type: "object",
            properties: {
              name: { type: "string" },
              price: { type: "number" },
              availability: { type: "string" },
            },
            required: ["name", "price"],
          },
        },
      },
    },
  },
});

Resources

Next Steps

For common errors, see firecrawl-common-errors.

When not to use it

  • When raw markdown is sufficient and structured data is not needed

Prerequisites

@mendable/firecrawl-jsFIRECRAWL_API_KEYJSON Schema knowledge

Limitations

  • LLM extraction adds variable credit costs
  • Large batch sets require async polling

How it compares

This workflow shifts from simple document retrieval to structured data extraction, allowing for direct integration of web content into typed applications.

Compared to similar skills

firecrawl-core-workflow-b side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
firecrawl-core-workflow-b (this skill)127dReviewIntermediate
json-render-core32moNo flagsAdvanced
turborepo612moReviewIntermediate
senior-fullstack357moReviewIntermediate

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