Agent skill · AI & Agents

slurm-job-script-generator

Generate SLURM `sbatch` job scripts and sanity-check HPC resource requests (nodes, tasks, CPUs, memory, GPUs) for simulation runs. Use when preparing submission scripts, deciding MPI vs MPI+OpenMP layouts, standardizing `#SBATCH` directives, or debugging job launch configuration (`sbatch`/`srun`).

FreedomIntelligencegithub.com/FreedomIntelligenceGitHub ↗
claude-codecan modify filesships scripts
Install
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill slurm-job-script-generator --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 3
SKILL.md size: 5 KB
Bundled scripts: yes
Allowed tools: ReadBashWriteGrepGlob
Path: skills/slurm-job-script-generator/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 2,909
Language: Python
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the 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 | Input | Description | Example | |-------|-------------|---------| | Job name | Short identifier for the job | `phasefield-strong-scaling` | | Walltime | SLURM time limit | `00:30:00` | | Partition | Cluster partition/queue (if required) | `compute` | | Account | Project/account (if required) | `matsim` | | Nodes | Number of nodes to allocate | `2` | | MPI tasks | Total tasks, or tasks per node | `128` or `64` per node | | Threads | CPUs per task (OpenMP threads) | `2` | | Memory | `--mem` or `--mem-per-cpu` (cluster policy dependent) | `32G` | | GPUs | GPUs per node (optional) | `4` | | Working directory | Where the run should execute | `$SLURM_SUBMIT_DIR` | | Modules | Environment modules to load (optional) | `gcc/12`, `openmpi/4.1` | | Run command | The command to launch un

What's inside
Steps it walks through
  1. Goal
  2. Requirements
  3. Inputs to Gather
  4. Decision Guidance
  5. MPI vs MPI+OpenMP layout
  6. Memory flag selection
  7. Script Outputs (JSON Fields)
  8. Workflow
  9. CLI Examples
  10. Conversational Workflow Example
  11. Error Handling
  12. Limitations
  13. References
  14. Version History
Ships with 2 files
  • references/slurm_directives.md
  • scripts/slurm_script_generator.py
Commands it runs
Preview a job script (prints to stdout)
python3 skills/hpc-deployment/slurm-job-script-generator/scripts/slurm_script_generator.py \
Write to a file and also emit structured JSON
python3 scripts/slurm_script_generator.py --job-name run --time 02:00:00 --nodes 2 --ntasks-per-node 64 --cpus-per-task 2 -- -- ./simulate
More from OpenClaw-Medical-Skills
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About this skill
What does the slurm-job-script-generator skill do?

Generate SLURM `sbatch` job scripts and sanity-check HPC resource requests (nodes, tasks, CPUs, memory, GPUs) for simulation runs. Use when preparing submission scripts, deciding MPI vs MPI+OpenMP layouts, standardizing `#SBATCH` directives, or debugging job launch configuration (`sbatch`/`srun`).

How do I install it?

Run `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill slurm-job-script-generator --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From FreedomIntelligence/OpenClaw-Medical-Skills, a repository with 2,909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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