What is the main purpose of the hpc-context skill?

It provides Claude with specialized knowledge to help users configure and run Mycelia multiomic analysis workflows on high-performance computing clusters using SLURM schedulers.

Can I use this for performance benchmarking?

Yes, the skill contains specific commands and submission patterns for running small, medium, and large-scale benchmarks using Julia and SLURM sbatch.

How does it help with resource management?

It provides patterns for memory estimation via dedicated utility functions and sets guidelines for isolating external tool calls to prevent resource bottlenecks during large-scale analysis.

Is it compatible with job schedulers other than SLURM?

While it focuses on SLURM, the skill emphasizes portability practices, such as environment testing and generic path handling, to facilitate compatibility with other schedulers like PBS.

Where should new helper functions be added when using this skill?

To maintain codebase integrity, the skill mandates that broadly useful helper functions be added to the 'src/' directory rather than within tutorial or benchmarking scripts.

HPC Context for Mycelia

Name: HPC Context for Mycelia
Author: cjprybol

bycjprybol

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データサイエンスとML

Optimizes Mycelia multiomic analysis workflows for High Performance Computing (HPC) environments and SLURM workload managers.

概要

This skill provides specialized guidance for executing the Mycelia multiomic analysis framework on large-scale HPC systems. It streamlines benchmark management, SLURM job submissions, and resource allocation while ensuring cross-platform portability and codebase maintainability. By incorporating best practices for memory estimation, directory structure, and environment-agnostic path handling, it allows researchers to scale complex biological characterization and prediction tasks from local machines to enterprise-grade clusters efficiently.

主な機能

SLURM workload management and sbatch submission patterns
Cross-platform portability with standardized path handling and environment testing
Architectural standards for extending Mycelia functionality within HPC constraints
6 GitHub stars
Tiered benchmarking support for small, medium, and large-scale runs
Memory estimation and resource allocation guidance for multiomic data

ユースケース

Optimizing SLURM job scheduling for resource-intensive biological characterization and design
Executing automated performance benchmarks across various computational resource tiers
Scaling multiomic data integration workflows from local development to HPC clusters

概要

主な機能

SLURM workload management and sbatch submission patterns
Cross-platform portability with standardized path handling and environment testing
Architectural standards for extending Mycelia functionality within HPC constraints
6 GitHub stars
Tiered benchmarking support for small, medium, and large-scale runs
Memory estimation and resource allocation guidance for multiomic data

ユースケース

Optimizing SLURM job scheduling for resource-intensive biological characterization and design
Executing automated performance benchmarks across various computational resource tiers
Scaling multiomic data integration workflows from local development to HPC clusters