Individual skill
databricks-cluster-forensics
Diagnose broken or unexplained Databricks compute — slow cold starts, failed cluster launches, Photon paying its premium without the speedup, DBR-upgrade landmines, and spot-interruption shuffle aborts — by correlating a cluster's live event stream across API surfaces. Use when a Databricks cluster won't start, died mid-run, is randomly slow to start, when planning a Databricks Runtime upgrade, or when a job keeps failing on spot loss. Trigger with "databricks cluster won't start", "cluster failed", "why is my cluster slow", "NPIP_TUNNEL_SETUP_FAILURE", "databricks runtime upgrade", "photon not helping".
plugins/saas-packs/databricks-pack/skills/databricks-cluster-forensics/SKILL.md
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- ai Repository topic
- automation Repository topic
- claude-code Repository topic
- devops Repository topic
- mcp Repository topic
- ai-agents Repository topic
- anthropic Repository topic
- claude-code-plugins Repository topic
- developer-tools Repository topic
- llm Repository topic
- saas Declared skill tag
- databricks Declared skill tag
- clusters Declared skill tag
- sre Declared skill tag
- forensics Declared skill tag
- engineering Derived category
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# Databricks Cluster Forensics
The operational SRE spine of the pack — what a Databricks engineer reaches for at
2 AM when the compute layer is broken or unexplained. It correlates a cluster's
live event stream across API surfaces to name the failure with its **actual error
code** and its **version-specific mitigation**, not "network problem, try again".
## Overview
Six real compute-layer failures live in this skill; each has a deterministic
detector and an on-demand reference:
1. **Cold-start long-tail** (D01) — on VNet/VPC-injected workspaces a 5-minute
start randomly takes 20-35, and Databricks reports only the aggregate.