databricks-jobs

Query Databricks job status and run histories to diagnose failures.

1|Updated Jan 6, 2026

Search Tags:#debugging #monitoring #workflows #databricks #jobs #system-tables

Ready to run

npx skills add https://github.com/mats16/briclaude --skill databricks-jobs-mats16

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: databricks-jobs
Source: https://github.com/mats16/briclaude/tree/main/skills/databricks-jobs
Command: npx skills add https://github.com/mats16/briclaude --skill databricks-jobs-mats16

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Streamlines Databricks job management by providing quick visibility into job health, run histories, and failure diagnostics to reduce MTTR.

Core Features & Use Cases

Real-time status checks, run history browsing, and failure investigation for Databricks jobs.
Unified workflows for repair, rerun, and historical analysis using system.lakeflow tables.
Use cases include debugging notebook or spark-task failures, auditing job performance, and optimizing scheduling across multiple jobs.

Quick Start

Ask to fetch a Databricks job overview and initiate a repair workflow for a failed run.

Frequently Asked Questions about databricks-jobs

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I investigate a failed Databricks job run?▼

Databricks job failure investigation uses CLI-based live checks to fetch run histories and current status. This Skill applies failure diagnostics and historical analysis to resolve job health issues and reduce MTTR.

What is the best way to monitor Databricks workflows for pipeline health issues?▼

Monitoring Databricks workflows for pipeline health issues is done by querying current status and run histories. It supports real-time visibility and historical analysis across multiple jobs to identify and resolve failures quickly.

Can I use system.lakeflow tables for historical analysis of Databricks jobs?▼

Yes, you can use system.lakeflow tables for historical analysis of Databricks jobs. This Skill supports querying these tables to audit job performance and browse historical run data across your data pipelines.

How do I initiate a repair workflow for a failed Databricks job?▼

To initiate a repair workflow for a failed Databricks job, ask to fetch a job overview and trigger a repair. The Skill supports CLI-based run-now commands to repair and rerun failed data pipeline tasks.

Does this approach support debugging notebook and spark-task failures in Databricks?▼

Yes, this approach supports debugging notebook and spark-task failures in Databricks. It provides unified workflows for failure investigation, allowing operations teams to troubleshoot and repair failed analytics workloads.