Pipeline Medic
Diagnose pipeline failures in minutes, not hours
When a pipeline fails, paste the error log and get a plain-English diagnosis with the likely root cause, affected downstream systems, and a suggested fix. Helps you get from stack trace to next step fast.
INGREDIENTS
PROMPT
Create a skill called "Pipeline Medic". When I paste an error log, stack trace, or failure notification from a data pipeline, analyze it and produce: (1) A one-sentence summary of what failed. (2) The root cause, categorized as: schema change, credential/auth issue, resource limit (memory/disk/timeout), data quality issue, code bug, or infrastructure problem. (3) The specific line or component that failed, with context. (4) A suggested fix with actual commands or code changes. (5) A list of likely downstream impacts (which dashboards or reports might be affected). (6) A stakeholder-friendly summary I can paste into Slack ("The daily revenue dashboard may show yesterday's numbers because..."). Support Airflow, dbt, Prefect, Dagster, Fivetran, and generic Python/bash pipeline logs.
How It Works
Airflow task failures produce 500-line stack traces. dbt errors reference
model files three levels deep. The actual root cause is usually one thing —
a schema change, an expired credential, a resource limit — buried in noise.
This skill extracts the signal.
What You Get
- Plain-English diagnosis of what failed and why
- Root cause identification (schema drift, credential expiry, resource limits, data issues, code bugs)
- Affected downstream systems and dashboards
- A specific fix suggestion (not just "check the logs")
- Incident summary formatted for stakeholder communication
- Pattern recognition across recurring failures
Setup Steps
- Ask your Claw to create a "Pipeline Medic" skill with the prompt below
- Paste any error log, stack trace, or failure notification
- Optionally describe the pipeline context (Airflow DAG, dbt model, etc.)
- Get back a diagnosis with suggested fix
Tips
- Works with Airflow, dbt, Prefect, Dagster, Fivetran, and custom pipeline logs
- The stakeholder-formatted summary saves you from writing "why the dashboard is broken" emails
- Over time, it recognizes recurring failure patterns and suggests preventive measures
- Pair with Freshness Alarm to auto-trigger diagnosis when an anomaly is detected