# First Useful Apify Scrape — transcript

This narrated tutorial uses an illustrated workflow. No paid Actor run or live output row is shown. Review the Actor’s live Pricing tab before running.

## 00:00:00.000 — Your first useful Apify scrape

Let's turn one public LinkedIn job into a useful record. We'll run the Neuton Job Details Actor once, inspect the result, then export a checked CSV.

## 00:00:10.024 — Open the Actor. Check Pricing.

Open the Actor on Apify and read the current Pricing tab. You need an Apify account, but no LinkedIn login. Set a charge limit before starting.

## 00:00:19.429 — Paste a one-row starter

In Input, open the JSON editor. Leave job URLs empty, enter data engineer and London, and set max results to one. The Actor finds a current public listing at run time.

## 00:00:31.373 — Start once. Inspect Dataset.

Select Start once. When the run finishes, open Dataset. A succeeded run can still have zero useful rows, so check the data itself.

## 00:00:40.715 — Verify the row against its source

Check job ID and URL, then title, company, location, description, and application path against the public listing. The row on screen is illustrative. Missing salary stays null.

## 00:00:52.424 — Read RUN_SUMMARY

If the Dataset is empty, open Storage, the default key-value store, and Run Summary. Check its failed inputs and rows saved. The Log has further diagnostics.

## 00:01:03.024 — Export only after review

Once the row passes review, export Dataset as CSV. Open the file and verify job ID and source URL before sending it to a spreadsheet.

## 00:01:13.176 — One input. One checked row. One export.

That's the workflow: one bounded input, one checked row, one export. Links to the Actor and full guide are below. Review live pricing before your own run.