Make sure there is a blank line after "CONTENT:"? The spec says "CONTENT:" then the full markdown content. Probably we start on next line with markdown.

We must ensure the markdown content includes headings with ## and ###.

We must include the disclaimer near top of content.

We must include the TL;DR section at top of content.

We must include the pricing table exactly as markdown table.

We must include infographic components: Stats Grid and Step Flow maybe.

We must include at least 2 infographic components. Use Stats Grid and Step Flow.

We must embed them as HTML blocks.

We must include code blocks with titles and line highlighting.

We must include Python, Node.js, cURL, Cortex examples.

We must keep length 2000-3500 words. Let's approximate: 2500 words. That's about 15-20 paragraphs of ~150 words each. We'll aim for ~2500 words.

We must ensure we don't exceed the word count. Let's approximate: TL;DR ~30 words. Why collect e-commerce data ~50 words. Technical challenges ~150 words. Quick start ~200 words. Extracting structured data ~150 words. Structured JSON extraction ~200 words. Cost breakdown ~150 words. Best practices ~150 words. Scaling up ~150 words. Key takeaways ~100 words. That's about 1500 words. Need more to reach 2000-3500. We can add more details, maybe more examples, more explanation. We'll write longer sections.

Let's write content with headings.

We must avoid using "it's not about speed, it's about reliability". Avoid that structure. Use active voice.

We must avoid "we built at AlterLab". Avoid.

We must avoid "we're excited". Avoid.

We must avoid "that's fair". Avoid.

We must avoid "great question". Avoid.

We must avoid "here's the thing". Avoid.

We must avoid "leveraging". Avoid.

We must avoid "synergy". Avoid.

We must avoid "optimize your workflow". Avoid.

We must avoid "It's not X, it's Y". Avoid.

We must avoid passive voice where active works better. Use active.

We must avoid em dashes. Use commas.

We must avoid using " — ". Use hyphen? Hyphen is okay but not em dash. We'll just use commas.

We must ensure we don't accidentally use an em dash in the text. We'll just avoid any dash characters longer than hyphen. Use hyphen for ranges? The rule says "Use – in HTML only where a range is needed." But we are not using HTML ranges. So we can avoid dash characters entirely.

We must not use em dash in markdown. So avoid "–". Use "to". Use "and". Use "with". Use commas.

We must not use "–" anywhere. So avoid "T3 — Browser". We'll just write "T3 Browser". Use spaces.

Now write content.

We'll start content with disclaimer near top.

We'll include TL;DR section.

We'll include internal links.

We'll include pricing table.

We'll include infographic components: Stats Grid and Step Flow.

We'll include code blocks with titles and line highlighting.

We need to embed the infographics as HTML blocks.

We must include the pricing table exactly as markdown.

We must include the code blocks with titles and line highlighting.

We must include the Cortex structured extraction example.

We must include the cost breakdown note about auto-escalation.

We must keep mentions of AlterLab limited to maybe 4 times in prose.

We must not mention "AlterLab" in code examples (they are code, not prose). That's okay.

Now let's draft.

Word count target: 2500 words. Let's approximate.

We'll write fairly long sections.

Now produce final output.

We must output exactly as format.

Let's start constructing.

First lines:

TITLE: How to Scrape Sephora Data: Complete Guide for 2026 EXCERPT: Learn how to scrape sephora data with Python Node js and AlterLab API. This guide shows code examples pricing and best practices. CATEGORY: tutorials TAGS: Anti-Bot, Automation, Cloudflare, Python, JavaScript, Node.js, Playwright, Puppeteer, Selenium, Proxies, Data Extraction, APIs, Scraping, Monitoring, E-Commerce, AI, AI Agents, LLM, RAG, MCP, Data Pipelines, Rate Limiting, Headless Browsers, CAPTCHA SEO_TITLE: How to Scrape Sephora Data in 2026 | AlterLab SEO_DESCRIPTION: How to scrape sephora data with Python Node js and AlterLab API. Includes pricing code and best practices for e‑commerce data. FAQ: Q: Is it legal to scrape sephora? A: Scraping publicly accessible data is generally legal if you respect robots.txt rate limits and the site terms of service. Q: What are the technical challenges of scraping sephora? A: Sephora uses anti bot protections such as rate limiting header checks and CAPTCHAs which require proxy rotation and browser rendering. Q: How much does it cost to scrape sephora at scale? A: Cost starts at $0.0002 per request for static pages and rises to $0.004 per request for full browser rendering with AlterLab auto escalation meaning you only pay for the tier that succeeds. CONTENT:

TL;DR

Scrape sephora.com with AlterLab API using Python Node js or cURL. Extract structured JSON with Cortex. Pay per request starting at $0.0002. Respect robots.txt and rate limits.

Why collect e‑commerce data from Sephora?

You may need product pricing inventory availability or rating information for market research price monitoring or competitive analysis. Public pages such as product listings or category overviews provide this data without requiring login. Collecting it at scale helps you build price index models or trend reports. The data is openly displayed so you can retrieve it responsibly.

Technical challenges

E‑commerce sites like sephora.com employ several anti bot measures. They check request headers for known scraper user agents they enforce rate limits and they serve CAPTCHAs when request patterns appear automated. Static HTTP requests often trigger blocks. To get reliable results you need to rotate IP addresses manage headers and sometimes render JavaScript. AlterLab handles these challenges through automatic proxy rotation headless browser support and smart rendering via the Smart Rendering API.

Quick start with AlterLab API

Create an API key on the AlterLab dashboard. Install the SDK for your language. Use the following Python example to scrape a public product page.

import alterlab

client = alterlab.Client("YOUR_API_KEY")
response = client.scrape("https://www.sephora.com/product/perfect-skin-balm")
print(response.text)

Use the Node js example for the same page.

import { AlterLab } from "alterlab";

const client = new AlterLab({ apiKey: "YOUR_API_KEY" });
const response = await client.scrape("https://www.sephora.com/product/perfect-skin-balm");
console.log(response.text);

Or run the cURL command from a terminal.

curl -X POST https://api.alterlab.io/v1/scrape \
  -H "X-API-Key: YOUR_KEY" \
  -d '{"url": "https://www.sephora.com/product/perfect-skin-balm"}'

All three examples hit a public product page that does not require authentication. Follow the Getting started guide for installation details.

Extracting structured data

Public product pages expose data such as title price rating and description in predictable HTML patterns. Identify the CSS selectors that wrap each field. For the example product the title lives in a h1 element with class product-title the price appears in a span with class price-current and the rating shows in a span with class rating-average. Use these selectors to parse the response text.

Structured JSON extraction with Cortex

Cortex lets you extract typed JSON directly from a page without writing parsing logic. Define a schema that lists the fields you want. The API returns a Python dictionary that matches the schema. This approach reduces errors and makes downstream processing easier.

import alterlab

client = alterlab.Client("YOUR_API_KEY")
result = client.extract(
    url="https://www.sephora.com/product/perfect-skin-balm",
    schema={
        "type": "object",
        "properties": {
            "title": {"type": "string"},
            "price": {"type":