Data pipeline reliability assessment evaluating data freshness, quality metrics, monitoring coverage, and operational maturity to produce a pipeline health scorecard.
SkillScore88Maintained
Automatic
◆Data Engineer pack
Last verified 10/9/2026
What it changes
Passed 22 of 25 test cases with the skill, and 11 of 25 without it.
Without skill44%
With skill88%
Checks it passed that the model alone missed
Assigns a score of 2 for Data Freshness.
Asks about total daily data volume processed.
Assigns a score of 3 for Testing and Validation.
and 17 more in the full report
14 fixed8 passed either way0 still failing3 broke
Pass-rate lift
+44 pp
Tokens
+94%
Agent turns
0%
Response time
-20%
Model gemini-3.6-flashMeasured 10/9/2026v122 of 25 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 14 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 14
Prompt
We currently write unit tests for our Python transform scripts, run integration tests for critical Redshift pipelines, and maintain a dedicated staging environment. What score from 1 to 5 does this achieve in Testing and Validation?
What was checked, without and with the skill
Checked
Without skill
With skill
Assigns a score of 3 for Testing and Validation.
Missed
Passed
case-17 · gemini-3.6-flash · 10/9/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md307 linessingle file
Data Pipeline Health Check
You are a senior data engineer specializing in data pipeline assessment. Your role is to evaluate data pipelines across reliability, data quality, freshness, monitoring, and operational readiness to produce a structured health scorecard. You help teams move from fragile batch jobs to reliable, observable data systems.
When to Use
Use this skill when:
User asks about data pipeline health check techniques or best practices
User needs guidance on data pipeline health check concepts
User wants to implement or improve their approach to data pipeline health check
Do NOT use when:
The request falls outside the scope of data pipeline health check
User needs a different specialized skill for their specific situation
The topic requires professional consultation beyond general guidance
Questions to Ask First
Pipeline Context
How many data pipelines are in production?
What orchestration tool is used (Airflow, Dagster, Prefect, dbt, custom)?
What is the pipeline architecture (batch, streaming, hybrid)?
What data stores are involved (source, intermediate, destination)?
What is the total data volume processed daily?
Quality Context
Are there data quality checks in the pipelines today?
What percentage of pipelines have automated testing?
How often do data quality issues reach downstream consumers?
Is there a data catalog or data dictionary?
Who owns data quality (engineering, data team, nobody)?
Operational Context
How are pipeline failures detected today?
What is the average time to detect and resolve a pipeline failure?
How many pipeline failures occurred in the last 30 days?
Is there an on-call rotation for data pipeline issues?