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.
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.
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.
Describe a pipeline in English, get a working Airflow DAG
Skip the boilerplate. Describe your data pipeline in plain English and get a complete Airflow DAG with proper dependencies, error handling, retries, and alerting. Also debugs failing DAGs by analyzing task logs.
Correlate logs across 50 microservices in seconds, not hours
Give it a time window and a request ID (or just a symptom) and it hunts through your logs across services, correlates events, and builds a timeline of what happened. Beats grepping through CloudWatch for 30 minutes.
Freeze first, sort later
A brokerage-account incident response checklist for suspicious access, account takeover concerns, and evidence capture when something looks wrong.
Stop audio drift by quarantining variable-frame-rate clips at ingest
Audio slowly drifts out of sync or randomly desyncs in your timeline when footage is variable frame rate — common with iPhone footage, screen recordings, and some OBS workflows. This recipe catches VFR clips at ingest, transcodes them to constant frame rate, and quarantines the originals so drift never reaches your edit.