firecrawl-data-handling
Process, validate, and store Firecrawl scraped content with deduplication and chunking. Use when handling scraped markdown, implementing content pipelines, building RAG knowledge bases, or processing crawl results for downstream consumption. Trigger with phrases like "firecrawl data", "firecrawl content processing", "firecrawl markdown cleaning", "firecrawl storage", "firecrawl RAG pipeline".
Best use case
firecrawl-data-handling is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Process, validate, and store Firecrawl scraped content with deduplication and chunking. Use when handling scraped markdown, implementing content pipelines, building RAG knowledge bases, or processing crawl results for downstream consumption. Trigger with phrases like "firecrawl data", "firecrawl content processing", "firecrawl markdown cleaning", "firecrawl storage", "firecrawl RAG pipeline".
Teams using firecrawl-data-handling should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/firecrawl-data-handling/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How firecrawl-data-handling Compares
| Feature / Agent | firecrawl-data-handling | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Process, validate, and store Firecrawl scraped content with deduplication and chunking. Use when handling scraped markdown, implementing content pipelines, building RAG knowledge bases, or processing crawl results for downstream consumption. Trigger with phrases like "firecrawl data", "firecrawl content processing", "firecrawl markdown cleaning", "firecrawl storage", "firecrawl RAG pipeline".
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# 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
```typescript
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
```typescript
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
```typescript
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, ${duplicates.length} duplicates`);
return { unique, duplicates };
}
```
### Step 4: Chunk for LLM / RAG
```typescript
interface ContentChunk {
url: string;
title: string;
chunkIndex: number;
content: string;
wordCount: number;
}
function chunkForRAG(
url: string,
title: string,
markdown: string,
maxWords = 800
): ContentChunk[] {
// Split by headings to preserve semantic boundaries
const sections = markdown.split(/\n(?=#{1,3}\s)/);
const chunks: ContentChunk[] = [];
let current = "";
let index = 0;
for (const section of sections) {
const combined = current ? `${current}\n\n${section}` : section;
if (combined.split(/\s+/).length > maxWords && current) {
chunks.push({
url, title, chunkIndex: index++,
content: current.trim(),
wordCount: current.split(/\s+/).length,
});
current = section;
} else {
current = combined;
}
}
if (current.trim()) {
chunks.push({
url, title, chunkIndex: index,
content: current.trim(),
wordCount: current.split(/\s+/).length,
});
}
return chunks;
}
```
### Step 5: Crawl and Store Pipeline
```typescript
import { writeFileSync, mkdirSync } from "fs";
import { join } from "path";
async function crawlAndStore(baseUrl: string, outputDir: string, opts?: {
maxPages?: number;
paths?: string[];
}) {
mkdirSync(outputDir, { recursive: true });
const crawlResult = await firecrawl.crawlUrl(baseUrl, {
limit: opts?.maxPages || 50,
includePaths: opts?.paths,
scrapeOptions: { formats: ["markdown"], onlyMainContent: true },
});
const pages = (crawlResult.data || []).map(page => ({
url: page.metadata?.sourceURL || baseUrl,
markdown: cleanMarkdown(page.markdown || ""),
}));
// Deduplicate
const { unique } = deduplicatePages(pages);
// Write files + manifest
const manifest = unique.map(page => {
const slug = new URL(page.url).pathname
.replace(/\//g, "_").replace(/^_|_$/g, "") || "index";
const filename = `${slug}.md`;
writeFileSync(join(outputDir, filename), page.markdown);
return { url: page.url, file: filename, size: page.markdown.length };
});
writeFileSync(join(outputDir, "manifest.json"), JSON.stringify(manifest, null, 2));
return manifest;
}
```
## Error Handling
| Issue | Cause | Solution |
|-------|-------|----------|
| Empty content | JS not rendered | Increase `waitFor`, use `onlyMainContent` |
| Garbage in markdown | Bad HTML cleanup | Add `excludeTags` for problematic elements |
| Duplicate pages | URL aliases or redirects | Content-hash deduplication |
| Oversized chunks | Long single sections | Add word limit to chunking logic |
| Extract returns null | Page too complex for LLM | Simplify schema, use shorter prompt |
## Examples
### Documentation Scraper with RAG Output
```typescript
const docs = await crawlAndStore("https://docs.example.com", "./scraped-docs", {
maxPages: 50,
paths: ["/docs/*", "/api/*"],
});
// Generate RAG-ready chunks
for (const doc of docs) {
const content = readFileSync(`./scraped-docs/${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
- [Firecrawl Scrape Options](https://docs.firecrawl.dev/features/scrape)
- [Firecrawl Extract](https://docs.firecrawl.dev/features/llm-extract)
- [Zod Validation](https://zod.dev/)
## Next Steps
For access control, see `firecrawl-enterprise-rbac`.Related Skills
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