Firecrawl Data Handling Skill

Firecrawl Data Handling

Overview

Process scraped web content from Firecrawl pipelines. Covers markdown cleaning, structured data extraction with Zod validation, content deduplication, chunking for LLM/RAG, and storage patterns for crawled content.

Instructions

Step 1: Content Cleaning

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

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

// Scrape with clean output settings
async function scrapeClean(url: string) {
  const result = await firecrawl.scrapeUrl(url, {
    formats: ["markdown"],
    onlyMainContent: true,   // strips nav, footer, sidebar
    excludeTags: ["script", "style", "nav", "footer", "iframe"],
    waitFor: 2000,
  });

  return {
    url: result.metadata?.sourceURL || url,
    title: result.metadata?.title || "",
    markdown: cleanMarkdown(result.markdown || ""),
    scrapedAt: new Date().toISOString(),
  };
}

function cleanMarkdown(md: string): string {
  return md
    .replace(/\n{3,}/g, "\n\n")                    // collapse multiple newlines
    .replace(/\[.*?\]\(javascript:.*?\)/g, "")      // remove JS links
    .replace(/!\[.*?\]\(data:.*?\)/g, "")           // remove inline data URIs
    .replace(/<!--[\s\S]*?-->/g, "")                // remove HTML comments
    .replace(/<script[\s\S]*?<\/script>/gi, "")     // remove script tags
    .trim();
}

Step 2: Structured Extraction with Validation

import { z } from "zod";

const ArticleSchema = z.object({
  title: z.string().min(1),
  author: z.string().optional(),
  publishedDate: z.string().optional(),
  content: z.string().min(50),
  wordCount: z.number(),
});

async function extractArticle(url: string) {
  const result = await firecrawl.scrapeUrl(url, {
    formats: ["extract"],
    extract: {
      schema: {
        type: "object",
        properties: {
          title: { type: "string" },
          author: { type: "string" },
          publishedDate: { type: "string" },
          content: { type: "string" },
        },
        required: ["title", "content"],
      },
    },
  });

  if (!result.extract) throw new Error(`Extraction failed for ${url}`);

  return ArticleSchema.parse({
    ...result.extract,
    wordCount: (result.extract.content || "").split(/\s+/).length,
  });
}

Step 3: Content Deduplication

import { createHash } from "crypto";

function contentHash(text: string): string {
  return createHash("sha256")
    .update(text.trim().toLowerCase())
    .digest("hex");
}

function deduplicatePages(pages: Array<{ url: string; markdown: string }>) {
  const seen = new Map<string, string>(); // hash -> first URL
  const unique: typeof pages = [];
  const duplicates: Array<{ url: string; duplicateOf: string }> = [];

  for (const page of pages) {
    const hash = contentHash(page.markdown);
    if (seen.has(hash)) {
      duplicates.push({ url: page.url, duplicateOf: seen.get(hash)! });
    } else {
      seen.set(hash, page.url);
      unique.push(page);
    }
  }

  console.log(`Dedup: ${pages.length} input, ${unique.length} unique, ${duplicates.length} duplicates`);
  return { unique, duplicates };
}

Step 4: Chunk for LLM / RAG

interface ContentChunk {
  url: string;
  title: string;
  chunkIndex: number;
  content: string;
  wordCount: number;
}

function chunkForRAG(
  url: string,
  title: string,
  markdown: string,
  maxWords = 800
): ContentChunk[] {
  // Split by headings to preserve semantic boundaries
  const sections = markdown.split(/\n(?=#{1,3}\s)/);
  const chunks: ContentChunk[] = [];
  let current = "";
  let index = 0;

  for (const section of sections) {
    const combined = current ? `${current}\n\n${section}` : section;
    if (combined.split(/\s+/).length > maxWords && current) {
      chunks.push({
        url, title, chunkIndex: index++,
        content: current.trim(),
        wordCount: current.split(/\s+/).length,
      });
      current = section;
    } else {
      current = combined;
    }
  }

  if (current.trim()) {
    chunks.push({
      url, title, chunkIndex: index,
      content: current.trim(),
      wordCount: current.split(/\s+/).length,
    });
  }

  return chunks;
}

Step 5: Crawl and Store Pipeline

import { writeFileSync, mkdirSync } from "fs";
import { join } from "path";

async function crawlAndStore(baseUrl: string, outputDir: string, opts?: {
  maxPages?: number;
  paths?: string[];
}) {
  mkdirSync(outputDir, { recursive: true });

  const crawlResult = await firecrawl.crawlUrl(baseUrl, {
    limit: opts?.maxPages || 50,
    includePaths: opts?.paths,
    scrapeOptions: { formats: ["markdown"], onlyMainContent: true },
  });

  const pages = (crawlResult.data || []).map(page => ({
    url: page.metadata?.sourceURL || baseUrl,
    markdown: cleanMarkdown(page.markdown || ""),
  }));

  // Deduplicate
  const { unique } = deduplicatePages(pages);

  // Write files + manifest
  const manifest = unique.map(page => {
    const slug = new URL(page.url).pathname
      .replace(/\//g, "_").replace(/^_|_$/g, "") || "index";
    const filename = `${slug}.md`;
    writeFileSync(join(outputDir, filename), page.markdown);
    return { url: page.url, file: filename, size: page.markdown.length };
  });

  writeFileSync(join(outputDir, "manifest.json"), JSON.stringify(manifest, null, 2));
  return manifest;
}

Error Handling

| Issue | Cause | Solution | |-------|-------|----------| | Empty content | JS not rendered | Increase waitFor, use onlyMainContent | | Garbage in markdown | Bad HTML cleanup | Add excludeTags for problematic elements | | Duplicate pages | URL aliases or redirects | Content-hash deduplication | | Oversized chunks | Long single sections | Add word limit to chunking logic | | Extract returns null | Page too complex for LLM | Simplify schema, use shorter prompt |

Examples

Documentation Scraper with RAG Output

const docs = await crawlAndStore("https://docs.example.com", "./scraped-docs", {
  maxPages: 50,
  paths: ["/docs/*", "/api/*"],
});

// Generate RAG-ready chunks
for (const doc of docs) {
  const content = readFileSync(`./scraped-docs/${doc.file}`, "utf-8");
  const chunks = chunkForRAG(doc.url, doc.file, content);
  console.log(`${doc.url}: ${chunks.length} chunks`);
  // Feed chunks to vector store (Pinecone, Weaviate, pgvector, etc.)
}

Resources

Next Steps

For access control, see firecrawl-enterprise-rbac.

Agent Skills: Firecrawl Data Handling

Install this agent skill to your local

Skill Files