eBay Data Collection and Analysis with Talos

*01 Scraping preview*

E-commerce product selection, competitor price comparison, and market sampling research all rest on the ability to collect data in bulk. This session used Talos to complete four tasks in a single conversation: bulk-collect 120 OLED panel listings from eBay, add 60 more with zero overlap against previous batches, merge all 180 rows and produce a 5-page PDF analysis report, and highlight the pagination control and then collect 30 quotes to validate the human-in-the-loop mechanism.

*02 Raw data*

Data work has three hard parts: cross-batch deduplication is hard to maintain, there is a gap between collection and analysis, and failures tend to happen at the handoffs in the workflow. This session used Talos to test whether all four tasks could be completed in one conversation.

*03 Incremental deduplication*

Task 1: Bulk-collect 120 OLED panel listings from eBay

Goal: Search eBay for OLED panel on-site, capture 120 product listings, and save them as a CSV.

Process: Talos first opened the search results page to probe its structure and returned 5 real records for the user to confirm the schema. In the preview, the first two rows exposed a problem: the title read Shop on eBay, the seller and shipping fields were empty, and the listingid was a fabricated number. Talos flagged these two rows as eBay's top ad placeholders and noted that real products start at the third row.

After the user confirmed, collection began. The first run returned 119 rows and stopped, because a single extraction has a row limit. Talos reported the nature of this limit clearly, explained that the page data was still there, and then continued scraping and paging to reach 121 rows. After removing the ad placeholder rows and topping up from the supplementary run, it delivered 120 rows.

Key results:

Highlights:

• Schema reuse: this page type already had a confirmed scraping recipe (eight fields: condition, link, listingid, location, price, seller, shipping, title). Talos recognized it and reused it directly, without asking for confirmation again.
• Honest disclosure of failures: it stated plainly that the row limit was a capacity limit on the extraction side and that the page data itself was complete.
• Proactive data cleaning: identifying and removing the ad placeholder rows, and topping up to 120 after removal, were not actions specified in the instructions.

Task 2: Collect 60 more with zero overlap against historical batches

Goal: Capture 60 more on top of what already existed, with an explicit requirement of no duplicates against any previous batch.

Process: The first run included a condition that excluded every historical batch. The system reported that 242 rows of previously collected data had been excluded and 0 new rows added — because everything on the first page was already in the dataset. Talos did not stop at 0; it moved to the third page and kept collecting, this time excluding 480 historical rows and adding 58 new ones. One more continuation run brought the count to 60.

Key results:

Highlights:

• Verifiable deduplication: Talos did not merely report that deduplication was complete; it gave the specific number of excluded rows. In the end, listingid was used to compare the new batch one by one against the three previous collection files, and overlap was 0 in every case, matching the tool's report.
• Correct response to an empty result: when the first run returned 0 new records, Talos judged the problem to be the page number rather than the number of products, and switched pages on its own.

Task 3: Merge 180 rows, unify the currency, and produce an analysis report

Goal: Combine the 120 rows and the 60 rows into one table, convert all prices to RMB, and produce a PDF analysis report based on the data.

Process: Talos first parsed every price prefix and got a result different from the earlier judgment — all 180 prices were in HKD; there was no USD. On that basis it corrected the earlier statement in Task 1 about mixed currencies, looked up a public mid-market rate, and converted at 1 HKD = 0.8542 RMB.

In the merge stage it added 6 columns to the original 8 fields: category classification, screen size, price in RMB, shipping in HKD, shipping in RMB, and total landed price. Shipping was parsed separately from the logistics text on each product page. The analysis report became a 5-page PDF covering seven parts: price analysis, product mix, seller landscape, logistics and origin, condition mix, conclusions and recommendations, and data limitations.

Key results:

Highlights:

• Correcting its own misjudgment: after finding that all 180 prices were in HKD, it explicitly corrected the Task 1 error in both the report and the delivery notes.
• Structural finding about mixed keywords: on eBay the keyword OLED panel points to two entirely different kinds of products. Of the 180 rows, 125 are laptop replacement screens (median price 1508 RMB) and 47 are embedded OLED modules (median price 33.1 RMB) — roughly a 46× difference. The overall average price of 1136 RMB is therefore meaningless on its own. No instruction asked for this conclusion.
• Two iterations of the classifier: the first version split by screen size and left 61% unclassified; the second used keyword rules and ended with 0 unclassified rows.
• Non-obvious insights in the report: 106 titles mention both OLED and LCD — 87 of the 125 rows in the replacement-screen group — which suggests sellers mix both panel technologies to widen search reach; the top five sellers hold 54% of the replacement-screen segment, but their median price is above the segment median, which suggests competition is not on price.

Task 4: Highlight the pagination control and collect 30 quotes

Goal: Open the first page of quotes.toscrape.com, first highlight the pagination control for the user to see, and after confirmation capture 30 records.

Process: Talos first opened the page to probe its structure and found that a scraping recipe for this page type had already been confirmed (author, tags, text). To give accurate highlight candidates, Talos fetched the page source to verify the structure and confirmed that list items are div.quote containers, the pager area is ul.pager, and the Next link is ul.pager li.next a.

It then placed 4 highlight candidates on the page: the whole ul.pager container, the ul.pager li.next a Next link itself, the outer nav ul.pager navigation, and all links inside ul.pager a. The user chose number 4. After confirmation the collection ran, and 30 records were completed across 3 pages.

Key results:

Highlights:

• Reading the source instead of guessing: highlighting depends on CSS selectors, and writing them from scratch easily frames the wrong element. Talos read the source to confirm the real tag structure before offering candidates.
• Record-by-record verification of pagination before delivery: it fetched the raw HTML of pages 1 through 3 and checked each of the 30 records against the page it came from, verifying that records 1–10 came from page 1, 11–20 from page 2, and 21–30 from page 3. Pagination did work, and there were no duplicates.

*04 Merged table*

The four tasks form one continuous chain: Task 1 handles a single collection, Task 2 handles the cross-batch boundary, Task 3 handles the processing from data to conclusions, and Task 4 handles human confirmation along the way.

*05 Report findings*

• Single task: from opening the website to delivering the CSV, everything happened in one conversation; the user never had to take over midway or supply code.
• Cross-batch work: incremental collection automatically excludes historical data, and the deduplication result is verifiable. For continuous monitoring that scrapes competitor prices daily, this is the deciding factor for long-term use.
• From data to conclusions: raw data was carried all the way to a PDF report ready to be handed to someone else, within the same conversation, with no switching between applications.
• Human-in-the-loop: where the system is uncertain, the decision goes to the user. Confirming the pagination control costs very little and avoids a class of failure that is expensive.

*06 Pagination confirmation*

1. Schema reuse removes repeated confirmation: for frequently repeated collection needs, a schema confirmed once should be reused directly, rather than asking the user to confirm what has already been confirmed.
2. Report failures honestly: row limits, empty results, and the need to change pages are not errors but characteristics of the process. Report their nature clearly so the user knows what to do next.
3. Make deduplication verifiable: don't just say deduplication is complete — give the specific number of excluded rows so the user can check it.
4. Correct your own misjudgments: in data work, self-correction is more trustworthy than a smooth run.
5. Let the data speak: the mixed-keyword conclusion was not required by any instruction; the data presented it. The value of analysis is finding non-obvious conclusions.
6. Verify record by record before delivery: on questions like whether pagination worked, a line saying collection succeeded carries no weight. Fetch the raw pages, compare each record's origin, and answer what the user actually cares about.

Q: How does Talos ensure field completeness when collecting data? A: Talos first probes the page structure and returns a small number of real records for the user to confirm the schema. Collection starts only after confirmation. If this page type already has a confirmed scraping recipe, it is reused directly, which keeps fields consistent.

Q: How is cross-batch deduplication done? A: Talos applies a condition that excludes historical batches during collection and reports the number of excluded rows. In the end, listingid can be used to compare the new batch one by one against historical files, verifying that overlap is 0.

Q: Can the collected data be used for analysis directly? A: Yes. Talos can complete merging, cleaning, currency conversion, classification, and report generation within the same conversation. In this case, after merging the 180 rows, it added 6 columns and produced a 5-page PDF report.

Q: What happens if the collection volume is very large? A: A single extraction has a row limit. The 120 rows were actually assembled from several continuation runs. Talos reports the nature of the limit clearly so the user knows to keep collecting.

Q: How does human-in-the-loop confirmation work? A: Where the system is uncertain, Talos hands the decision to the user. For the pagination control, for example, Talos first places highlight candidates on the page, and collection runs after the user confirms.