Treasure AI Studio provides skills that generate diagnostic queries to help you quickly identify root causes of common real-time issues.
Use these skills when:
- You have a parent segment ID and need to investigate an issue.
- You need a quick read on activation failures, profile merges, or stitching quality.
- You want a structured approach to diagnosing real-time problems.
Install the td-skills package to get started.
The following skills are available for real-time debugging:
| Skill | What It Answers | Expected Output |
|---|---|---|
activations | Did the activation fire? Why did it fail? | Fast summary of activation delivery errors and volume |
identity | Did the event stitch to the right profile? | Quick read on new, updated, and merged real-time profiles |
identify-top-key-values | Are bad stitching values polluting the graph? | Surfaces dominant key values, including null or empty patterns that cause data quality issues |
id-graph-canonical-id-size | Are canonical groups becoming unusually large? | Helps spot possible over-stitching or "mega-profile" patterns |
id-graph-ids-to-canonical-id | Is one ID mapping to multiple canonical IDs? | Helps detect inconsistent stitching and over-stitching cases |
rt-journey-monitor | Is the journey running as expected? Where is it failing? | Journey execution monitoring and activation failure debugging |
Choose the right skills based on the symptom you are investigating.
When a webhook, streaming egress, or downstream action did not execute as expected, start with these skills:
activations— Query activation logs for your parent segment to check whether the activation fired and identify delivery errors.rt-journey-monitor— If the activation is part of a journey, check whether the journey itself is executing correctly and where it might be failing.
When personalization attributes are missing, incomplete, or returning unexpected values, the root cause is often related to identity resolution. Use these skills:
identity— Check recent profile merges and stitching activity for your parent segment.identify-top-key-values— Inspect the distribution of stitching key values to find null, empty, or junk values that degrade data quality.id-graph-canonical-id-size— Analyze canonical ID group sizes to detect over-stitching, where too many IDs collapse into a single profile.id-graph-ids-to-canonical-id— Check whether a single ID maps to multiple canonical IDs, which indicates inconsistent stitching.
To use a skill, provide it with your parent segment ID and the specific question you want to answer. The following examples use parent segment 394649 as a placeholder — replace it with your own.
Use the activations skill to query activation logs for parent segment 394649.Use the identity skill to check recent profile merges for parent segment 394649.Use the identify-top-key-values skill to debug stitching key distributions
for parent segment 394649.Use the id-graph-canonical-id-size skill to analyze canonical ID group sizes
for parent segment 394649.Use the id-graph-ids-to-canonical-id skill to detect IDs mapping to multiple
canonical IDs.Before starting a debugging session, gather the following information:
- Parent segment ID for the real-time configuration you are investigating.
- A specific user or device ID that demonstrates the issue.
- A recent time window when the issue occurred.
- The symptom you are observing:
- Activation did not send
- Personalization returned empty or partial data
- Profile stitched incorrectly
- Event counts or merges look suspicious
Then choose the smallest set of skills that match your symptom:
- Activation problem — Start with
activations, thenrt-journey-monitorif the activation is journey-triggered. - Identity or merge problem — Start with
identity. - Suspected junk IDs or over-stitching — Use
identify-top-key-values,id-graph-canonical-id-size, andid-graph-ids-to-canonical-id. - Journey progression or delivery issue — Use
rt-journey-monitor.