Which HPC schedulers are supported by this skill?

This skill is specifically designed for SLURM (Simple Linux Utility for Resource Management) environments, which are common in academic and research HPC clusters.

What is the benefit of using symlinks for job scripts?

By symlinking to the main repository rather than copying, your project scripts stay automatically updated whenever the core Kintsugi tools are updated, preventing version drift.

How does the auto-detection feature simplify setup?

The skill automatically scans your environment for variables like SLURM_ACCOUNT and identifies available GPU hardware using nvidia-smi, pre-filling your configuration files to reduce manual data entry.

Can I use this on an existing project that wasn't created with Kintsugi?

Yes, you can use the 'kintsugi slurm init' command to add SLURM support and directory structures to any existing project path.

Is there a way to test my submission before it hits the queue?

Yes, the skill includes a '--dry-run' flag for the submit command, allowing you to preview the exact shell commands that would be executed without actually submitting the job.

SLURM Workflow Integration

Name: SLURM Workflow Integration
Author: smith6jt-cop

bysmith6jt-cop

Productivity & Workflow

Automates SLURM job submission and project initialization for High-Performance Computing (HPC) clusters.

About

This skill streamlines the workflow for researchers and developers using SLURM-managed HPC clusters by integrating job scheduling directly into the Kintsugi project lifecycle. It eliminates manual configuration friction through intelligent auto-detection of environment variables, GPU hardware, and Conda environments, while providing a robust CLI for submitting multi-step batch jobs. By using symlinks for job scripts and generating optimized configurations, it ensures that project settings remain maintainable and up-to-date with the latest HPC best practices.

Key Features

CLI-driven batch submission with support for granular steps, cycles, and dry-run previews
Standardized resource allocation templates for memory, GPU count, and execution time
One-command project initialization with integrated SLURM support and README documentation
Intelligent auto-detection of SLURM accounts, GPU types via nvidia-smi, and Conda environments
Symlink-based script management to ensure local project updates propagate from the core repository
0 GitHub stars

Use Cases

Researchers initializing new computational experiments on HiperGator or similar SLURM clusters
Data scientists automating multi-step processing pipelines such as deconvolution or image stitching
HPC users looking to reduce manual config errors when setting up GPU-intensive batch jobs

About

Key Features

CLI-driven batch submission with support for granular steps, cycles, and dry-run previews
Standardized resource allocation templates for memory, GPU count, and execution time
One-command project initialization with integrated SLURM support and README documentation
Intelligent auto-detection of SLURM accounts, GPU types via nvidia-smi, and Conda environments
Symlink-based script management to ensure local project updates propagate from the core repository
0 GitHub stars

Use Cases

Researchers initializing new computational experiments on HiperGator or similar SLURM clusters
Data scientists automating multi-step processing pipelines such as deconvolution or image stitching
HPC users looking to reduce manual config errors when setting up GPU-intensive batch jobs