Strawberry can organise scraping API runs, cloud-browser sessions, screenshots, and structured extraction output. It turns the work open in Scrapfly and the surrounding browser context into a focused review, so researchers, operators, developers, and customer teams can investigate exceptions, prepare handoffs, and keep the underlying evidence close to the decision.
A screenshot can look healthy while an extraction maps the wrong price, availability, or page variant. Review both the capture and the field-level output before a pipeline is promoted.
01
Scrapfly exceptions need their source evidence beside them.
Audit the Scrapfly work visible in this tab.
Separate capture failures, browser-session exceptions, and extraction anomalies into a review queue.
For Scrapfly, the useful outcome is a bounded view of what succeeded, what failed, and which source needs the next human look.
02
A Scrapfly result needs a source check before it becomes a fact.
Use the attached screenshots and returned content to test whether the selected fields still match the live page structure.
That Scrapfly comparison is where a collection pass becomes reliable enough to inform the next team, rather than merely fast enough to fill a table.
03
A Scrapfly handoff should say what changed and what it did not prove.
Prepare a release note for the data pipeline owner explaining what changed, which targets are affected, and what needs a manual retest.
The Scrapfly receiving owner gets links, scope, and unresolved edge cases instead of an opaque status update that has to be reconstructed from scratch.
04
The Scrapfly review cadence should match the cost of missing a change.
Every Friday at 08:45, assemble a Scrapfly capture-quality review from the agreed targets before the weekly pipeline release.
Keep the approved review prompt as a Scrapfly-specific skill, then set a routine at that cadence so the team sees material changes before the downstream meeting, import, release, or response window.
For Scrapfly, it can organise the signed-in views you choose into an evidence-backed review, flag important exceptions, and prepare a clear handoff for the accountable owner.
It can work through the Scrapfly browser context you provide, but consequential changes should remain reviewable before they are completed.
Yes. Keep a proven Scrapfly review as a named skill, then schedule it around the rhythm when new evidence is actually useful to the team.
Scrapfly supplies the web-data infrastructure; Strawberry helps turn its run history into an accountable decision.
Scrapfly Strawberry