Step 1
Pull the failed and pending payment attempts with their timestamps, recorded reason codes, linked order IDs and the current state of each order.
Failed and pending payment attempts look alike in the admin, so the team chases cases that resolve on their own. In this case, the immediate task is to detect payment failures that require follow-up. The workflow should produce a follow-up queue split by the recorded reason between cases needing customer action and cases that do not.
First replace every [bracketed] placeholder with real store scope and constraints, then add any actual records or data you have, and paste it into your preferred AI assistant or Spark agent. Spark will follow this page's workflow to produce structured output and verification steps.
Act as a Shopify operations specialist.
Help me detect payment failures that require follow-up.
My store context is [describe the business, products and target audience].
My scope is [specify the affected products, pages, customers or reporting period]. Use these inputs: [failed and pending payment attempts with their timestamps, recorded reason codes, linked order IDs, and each order's current state]. Split the cases by the recorded reason into those needing customer action and those expected to resolve without it, keeping pending attempts out of the follow-up queue until the window the provider states has passed.
Return the follow-up queue and resolution steps for the specified task.
Show the relevant evidence or before-and-after examples and explain how I can verify the result.
If essential data is missing, ask for it and mark unsupported conclusions as unknown.
Do not invent metrics, product claims or customer facts.
For any publication, message, account or store change, use only connected tools within my explicitly authorized scope and report success only after verification; otherwise provide the draft or proposed steps.Split the work into input, judgment, and deliverable phases. Verify each step's output before moving to the next.
Pull the failed and pending payment attempts with their timestamps, recorded reason codes, linked order IDs and the current state of each order.
Split the cases by the recorded reason into those needing customer action and those expected to resolve without it, keeping pending attempts out of the follow-up queue until the window the provider states has passed.
Deliver a follow-up queue with one resolution step and one owner per case, then confirm each case against the supplied records and log every retry with its result so a repeated failure stays visible.
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
No, judge the order by its recorded state rather than by the attempt. A failure may still be retried or settled later, so canceling on the first failure loses orders that would have completed on their own.
Only where the recorded reason shows customer action is needed, such as a new payment method. Use approved wording and channels, and never include details the merchant's handling rules keep out of messages.
Follow the merchant's stated retry rule and log every attempt with its timestamp and result. If the same reason repeats after the allowed attempts, stop retrying and route the case to an owner.
Start with scenarios under the same topic, then expand into cross-topic matches suggested by similarity.
Ciwi Spark builds Shopify-native AI tools and agents around the workflows merchants actually run: research, ads, translation, and customer support. Stop rewriting the same prompts by hand.