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slurm-job-script-generatorslurm 作业脚本生成器

Agent Skill

slurm-job-script-generator 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

514

周安装

21

GitHub Stars

31

下载量

165
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:slurm-job-script-generator(slurm 作业脚本生成器)
来源仓库:https://github.com/heshamfs/materials-simulation-skills
仓库路径:skills/slurm-job-script-generator
安装命令:
npx skills add https://github.com/heshamfs/materials-simulation-skills --skill slurm-job-script-generator
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/heshamfs/materials-simulation-skills --skill slurm-job-script-generator

简介

用于处理 GitHub 仓库、Issue、Pull Request 等协作信息,整理代码变更状态。

  • 适合在需要围绕仓库状态、代码变更或协作事项进行整理的场景中使用。
  • 可结合来源仓库和原始 README 继续核验具体用法和功能细节。
  • 安装前建议确认权限范围和是否涉及网络请求或文件系统操作。
  • 维护状态不明时,建议先评估稳定性再投入生产使用。

SKILL.md

SLURM Job Script Generator

Goal

Generate a correct, copy-pasteable SLURM job script (.sbatch) for running a simulation, and surface common configuration mistakes (bad walltime format, conflicting memory flags, oversubscription hints).

Requirements

  • Python 3.8+
  • No external dependencies (Python standard library only)
  • Works on Linux, macOS, and Windows (script generation only)

Inputs to Gather

InputDescriptionExample
Job nameShort identifier for the jobphasefield-strong-scaling
WalltimeSLURM time limit00:30:00
PartitionCluster partition/queue (if required)compute
AccountProject/account (if required)matsim
NodesNumber of nodes to allocate2
MPI tasksTotal tasks, or tasks per node128 or 64 per node
ThreadsCPUs per task (OpenMP threads)2
Memory--mem or --mem-per-cpu (cluster policy dependent)32G
GPUsGPUs per node (optional)4
Working directoryWhere the run should execute$SLURM_SUBMIT_DIR
ModulesEnvironment modules to load (optional)gcc/12, openmpi/4.1
Run commandThe command to launch under SLURM./simulate --config cfg.json

Decision Guidance

MPI vs MPI+OpenMP layout

Does the code use OpenMP / threading?
├── NO  → Use MPI-only: cpus-per-task=1
└── YES → Use hybrid: set cpus-per-task = threads per MPI rank
          and export OMP_NUM_THREADS = cpus-per-task

Rule of thumb: if you see diminishing strong-scaling efficiency at high MPI ranks, try fewer ranks with more threads per rank (and measure).

Memory flag selection

  • Use either --mem (per node) or --mem-per-cpu (per CPU), not both.
  • Follow your cluster’s documentation; some sites enforce one style.
  • SLURM --mem units are integer MB by default, or an integer with suffix K/M/G/T (and --mem=0 commonly means “all memory on node”).

Script Outputs (JSON Fields)

ScriptKey Outputs
scripts/slurm_script_generator.pyresults.script, results.directives, results.derived, results.warnings

Workflow

  1. Gather cluster constraints (partition/account, GPU policy, memory policy).
  2. Choose a process layout (MPI-only vs hybrid MPI+OpenMP).
  3. Generate the script with slurm_script_generator.py.
  4. Inspect warnings (conflicts, suspicious layouts).
  5. Save the generated script as job.sbatch.
  6. Submit with sbatch job.sbatch and monitor with squeue.

CLI Examples

# Preview a job script (prints to stdout)
python3 skills/hpc-deployment/slurm-job-script-generator/scripts/slurm_script_generator.py \
  --job-name phasefield \
  --time 00:10:00 \
  --partition compute \
  --nodes 1 \
  --ntasks-per-node 8 \
  --cpus-per-task 2 \
  --mem 16G \
  --module gcc/12 \
  --module openmpi/4.1 \
  -- \
  ./simulate --config config.json

# Write to a file and also emit structured JSON
python3 skills/hpc-deployment/slurm-job-script-generator/scripts/slurm_script_generator.py \
  --job-name phasefield \
  --time 00:10:00 \
  --nodes 1 \
  --ntasks 16 \
  --cpus-per-task 1 \
  --out job.sbatch \
  --json \
  -- \
  /bin/echo hello

Conversational Workflow Example

User: I need an sbatch script for my MPI simulation. I want 2 nodes, 64 ranks per node, 2 OpenMP threads per rank, and 2 hours.

Agent workflow:

  1. Confirm partition/account and whether GPUs are needed.
  2. Generate a hybrid job script: python3 scripts/slurm_script_generator.py --job-name run --time 02:00:00 --nodes 2 --ntasks-per-node 64 --cpus-per-task 2 -- --./simulate
  3. Explain the mapping:

- Total ranks = 128 - Threads per rank = 2 (OMP_NUM_THREADS=2)

  1. If the user provides node core counts, sanity-check oversubscription using --cores-per-node.

Error Handling

ErrorCauseResolution
time must be HH:MM:SS or D-HH:MM:SSBad walltime formatUse 00:30:00 or 1-00:00:00
nodes must be positiveNon-positive nodesProvide --nodes >= 1
Provide either --mem or --mem-per-cpu, not bothConflicting memory directivesChoose one memory style
Provide a run command after --Missing launch commandAdd --./simulate...

Security

Input Validation

  • --time is validated against strict HH:MM:SS or D-HH:MM:SS format via regex
  • --nodes, --ntasks, --ntasks-per-node, --cpus-per-task, --gpus are validated as positive integers with upper bounds
  • --mem and --mem-per-cpu are validated against SLURM's accepted format (<int>[K|M|G|T]); providing both simultaneously is rejected
  • --job-name is validated against [a-zA-Z0-9_.-]+ (no shell metacharacters)
  • --partition and --account are validated against safe-character allowlists
  • --module values are validated to prevent shell injection (no ;, |, &, backticks, or $)

File Access

  • The script reads no external files; all inputs are provided via CLI arguments
  • --out writes the generated sbatch script to a single specified file path
  • The generated script is a plain-text shell script with #SBATCH directives; it contains no dynamically generated code

Tool Restrictions

  • Read: Used to inspect script source, references, and existing job scripts
  • Bash: Used to execute slurm_script_generator.py with explicit argument lists; the generated script itself is NOT executed by the agent
  • Write: Used to save the generated .sbatch file; writes are scoped to the user's working directory
  • Grep/Glob: Used to locate existing scripts, configs, and cluster documentation

Safety Measures

  • No eval(), exec(), or dynamic code generation
  • All subprocess calls use explicit argument lists (no shell=True)
  • The run command (after --) is included verbatim in the generated script but is never executed by the skill itself
  • Module names are sanitized to prevent injection into module load directives
  • Generated scripts use set -euo pipefail for safe shell execution on the cluster

Limitations

  • Does not query cluster hardware or site policies; it can only validate internal consistency.
  • SLURM installations vary (GPU directives, QoS rules, partitions). Adjust directives for your site.

References

  • references/slurm_directives.md - Common #SBATCH directives and mapping tips

Version History

  • v1.0.0 (2026-02-25): Initial SLURM job script generator

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

37.57%
按下载量换算62

Claude

28.84%
按下载量换算48

Cursor

18.97%
按下载量换算31

Gemini CLI

9.67%
按下载量换算16

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/heshamfs/materials-simulation-skills --skill slurm-job-script-generator 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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