cluster-blame

Audit Slurm queue state to identify resource-stranding job submissions.

Updated Aug 27, 2026
One-click install
npx skills add https://github.com/olliecrow/codex --skill cluster-blame
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: cluster-blame
Source: https://github.com/olliecrow/codex/tree/main/skills/cluster-blame
Command: npx skills add https://github.com/olliecrow/codex --skill cluster-blame

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill helps identify why cluster resources (CPU, memory, GPU) appear idle and who might be blocking scheduling due to misconfigured job submissions.

Core Features & Use Cases

  • Resource Stranding Audit: Analyzes Slurm queue state to find jobs that unnecessarily occupy resources, preventing others from running.
  • Attribution & Evidence: Distinguishes between user misconfiguration and scheduler policy effects, providing confidence-ranked evidence.
  • Use Case: When users complain about slow job starts or idle GPUs, this Skill can pinpoint specific jobs or users whose resource requests are inefficiently shaped, leading to fragmented capacity.

Quick Start

Use the cluster-blame skill to quickly scan the current Slurm queue and identify likely users or jobs currently stranding CPU, GPU, or memory capacity.

Frequently Asked Questions about cluster-blame

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

FAQPage Schema
How do I identify which Slurm jobs are stranding cluster resources?

Identify Slurm resource stranding by auditing queue state and node packing to pinpoint inefficiently shaped job submissions that block scheduling. This analysis provides evidence-backed attribution distinguishing between user misconfiguration and scheduler policy effects.

Why are my Slurm jobs pending when GPUs appear idle?

Slurm jobs may remain pending with idle GPUs due to resource stranding, where inefficiently shaped job submissions fragment cluster capacity. Auditing node availability and queue state pinpoints specific users whose allocations block scheduling.

How do I diagnose HPC scheduling bottlenecks caused by user misconfiguration?

Diagnose HPC scheduling bottlenecks by analyzing job shapes, resource allocation, and node availability to provide evidence-backed attribution and confidence scoring for potential blockers. This distinguishes user misconfiguration from scheduler policy effects.

Can I check if Slurm scheduler policy is causing idle nodes instead of user jobs?

Check Slurm scheduler policy effects by auditing job shapes and node availability. The analysis distinguishes between scheduler policy effects and user misconfiguration, providing confidence-ranked evidence for potential scheduling blockers.

What is the best way to audit Slurm queue state for capacity planning?

Audit Slurm queue state for capacity planning by analyzing job shapes and resource allocation to identify resource-stranding submissions. This provides evidence-backed attribution showing who is blocking scheduling and why.

Does cluster-blame work without additional dependencies for HPC performance analysis?

Cluster-blame operates without additional dependencies to perform HPC performance analysis. It audits Slurm queue state and node packing internally to identify probable resource-stranding job submissions with confidence scoring.