Scrape data from Fiverr

What I mean by scraping Fiverr

Scraping Fiverr means a bot reads the public gig listings into a sheet. The gig title, the starting price, the rating and review count, the seller's level, the link. Point it at a category or a search and it gathers the same fields across the results, so working out how a service is priced becomes a spreadsheet instead of a dozen open gigs.

Pricing a gig market

The useful thing on Fiverr is the pricing signal. What does this kind of work go for, what do the top sellers charge, where does a new seller sit, how does rating track with price. That is all on the public gig cards, and gathering it into a sheet is how you read a market instead of guessing at it.

Two honest notes. Fiverr's terms restrict scraping, so keep it to research at a reasonable pace. And this is about the gigs, not the sellers, scraping seller names and contacts to pitch them is outreach spam, not research. Read the public listings to understand the market, and leave the people alone.

Who this is for

This is for the person sizing up a Fiverr market. A seller pricing their own gig against the field, a buyer comparing options, a researcher mapping what a service costs. Public gig data, gathered up. If you are looking to harvest sellers for outreach, this is not the tool. No-coders and coders both, since you can build it without code and drop into code when you want.

How I'd approach it

Open the category or search you want to study. Have the bot read the gig title, price, rating, and seller level off each card into a sheet, then page through the results. Keep the pace reasonable, take the public fields, and skip anything that is about the seller as a person rather than the gig.

A category, public gig fields, into a sheet. I would lay out the first draft with Build with description.

Scrape Fiverr from a description

Describe the category in plain words in the Chrome extension and it builds the steps for you. Give it a few short lines, check what it made, and run it. Explore no-code.

Scrape Fiverr in code

Build with code. You do not have to write the script by hand. Describe the scrape to our Claude skill and it generates a ready-to-run Node script you own, then debugs it with you, fixing a selector that stopped matching or a step that stalls, until the run is clean. Prefer to write it yourself? Explore the code tool. Either way, the script looks like this.

1import { AxiomApi } from "axiom-api";2 3const axiom = new AxiomApi(process.env.AXIOM_API_KEY);4 5await axiom.browserOpen();6try {7  await axiom.goto("https://www.fiverr.com/search/gigs?query=logo%20design");8  const gigs = await axiom.scrape(".gig-card-layout");9  console.log(gigs); // write these to your sheet10} finally {11  await axiom.browserClose();12}13 

+

Build with a Claude skill

Build no-code or code bots with a skill.

Add the Claude skill and describe the Fiverr category you want. It builds the bot for you, no-code or code, reading public gig listings into a sheet.

Install the skillLearn more

What can you scrape?

Public gig listings, into a sheet. A couple of lines to stay on the right side of.

What I'd watch out for

Fiverr's terms restrict scraping and the signal is in the gigs, so keep it focused. Here is what I would watch for.

Gigs, not sellers

Read the listings to understand the market, not to build a list of people to pitch. Seller contacts are personal data, and harvesting them is outreach spam, not research.

Keep it to research

Fiverr does not welcome heavy scraping, so keep it modest and for your own study of a market, at a reasonable pace.

Let the cards load

Results fill in as you scroll, so wait for the cards to appear before reading, or you will catch a half-loaded page.

Pick fields reliably

Gig cards change with redesigns, so pick what you read with the selector tool and check it holds across a few pages.