Step 1
Start with event logs, test journey, browser and consent conditions, pixel configuration and order identifiers.
Marketing reports are missing or duplicating events, making campaign measurement unreliable. In this case, the immediate task is to diagnose missing Facebook pixel events. The workflow should produce a tracking diagnosis and repeatable test plan, using the merchant's actual records and constraints.
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 diagnose missing Facebook pixel events.
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: [provide event logs, test journey, browser and consent conditions, pixel configuration and order identifiers]. Trace expected events through the journey, compare event IDs and timestamps and separate consent or access limitations from implementation failures.
Return a tracking diagnosis and repeatable test plan 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.Start with the business context, the trigger, and the expected output before moving into the execution steps.
Marketing reports are missing or duplicating events, making campaign measurement unreliable. In this case, the immediate task is to diagnose missing Facebook pixel events. The workflow should produce a tracking diagnosis and repeatable test plan, using the merchant's actual records and constraints.
Split the work into input, judgment, and deliverable phases. Verify each step's output before moving to the next.
Start with event logs, test journey, browser and consent conditions, pixel configuration and order identifiers.
Trace expected events through the journey, compare event IDs and timestamps and separate consent or access limitations from implementation failures.
Deliver a tracking diagnosis and repeatable test plan; verify the result against the supplied records and identify unresolved inputs.
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
Multiple implementations or retries may send the same event without effective deduplication. Compare event identifiers before changing the setup.
No. Tracking can be incomplete while an order exists. Reconcile measurement evidence with actual order records.
Start with event logs, test journey, browser and consent conditions, pixel configuration and order identifiers. These inputs establish the relevant scope and help separate an actual issue from missing information or an unsuitable comparison. Without them, the result should remain a proposed approach rather than a confirmed diagnosis.
Start with scenarios under the same topic, then expand into cross-topic matches suggested by similarity.
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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.