slurm-job-script-generator
About
This skill generates validated SLURM sbatch job scripts with automatic sanity checking for HPC resource requests. It helps developers configure MPI/OpenMP layouts, GPU jobs, and debug launch issues while preventing directive conflicts. Use it when preparing cluster submissions, standardizing team scripts, or troubleshooting failed jobs.
Quick Install
Claude Code
Recommendednpx skills add HeshamFS/materials-simulation-skills -a claude-code/plugin add https://github.com/HeshamFS/materials-simulation-skillsgit clone https://github.com/HeshamFS/materials-simulation-skills.git ~/.claude/skills/slurm-job-script-generatorCopy and paste this command in Claude Code to install this skill
Documentation
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 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
--memunits are integer MB by default, or an integer with suffixK/M/G/T(and--mem=0commonly means “all memory on node”).
Script Outputs (JSON Fields)
| Script | Key Outputs |
|---|---|
scripts/slurm_script_generator.py | results.script, results.directives, results.derived, results.warnings |
Workflow
- Gather cluster constraints (partition/account, GPU policy, memory policy).
- Choose a process layout (MPI-only vs hybrid MPI+OpenMP).
- Generate the script with
slurm_script_generator.py. - Inspect warnings (conflicts, suspicious layouts).
- Save the generated script as
job.sbatch. - Submit with
sbatch job.sbatchand monitor withsqueue.
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:
- Confirm partition/account and whether GPUs are needed.
- 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 - Explain the mapping:
- Total ranks = 128
- Threads per rank = 2 (
OMP_NUM_THREADS=2)
- If the user provides node core counts, sanity-check oversubscription using
--cores-per-node.
Error Handling
| Error | Cause | Resolution |
|---|---|---|
time must be HH:MM:SS or D-HH:MM:SS | Bad walltime format | Use 00:30:00 or 1-00:00:00 |
nodes must be positive | Non-positive nodes | Provide --nodes >= 1 |
Provide either --mem or --mem-per-cpu, not both | Conflicting memory directives | Choose one memory style |
Provide a run command after -- | Missing launch command | Add -- ./simulate ... |
Security
Input Validation
--timeis validated against strictHH:MM:SSorD-HH:MM:SSformat via regex--nodes,--ntasks,--ntasks-per-node,--cpus-per-task,--gpusare validated as positive integers with upper bounds--memand--mem-per-cpuare validated against SLURM's accepted format (<int>[K|M|G|T]); providing both simultaneously is rejected--job-nameis validated against[a-zA-Z0-9_.-]+(no shell metacharacters)--partitionand--accountare validated against safe-character allowlists--modulevalues are validated to prevent shell injection (no;,|,&, backticks, or$)
File Access
- The script reads no external files; all inputs are provided via CLI arguments
--outwrites the generated sbatch script to a single specified file path- The generated script is a plain-text shell script with
#SBATCHdirectives; 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.pywith explicit argument lists; the generated script itself is NOT executed by the agent - Write: Used to save the generated
.sbatchfile; 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 loaddirectives - Generated scripts use
set -euo pipefailfor 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#SBATCHdirectives and mapping tips
Version History
- v1.0.0 (2026-02-25): Initial SLURM job script generator
GitHub Repository
Related Skills
content-collections
MetaThis skill provides a production-tested setup for Content Collections, a TypeScript-first tool that transforms Markdown/MDX files into type-safe data collections with Zod validation. Use it when building blogs, documentation sites, or content-heavy Vite + React applications to ensure type safety and automatic content validation. It covers everything from Vite plugin configuration and MDX compilation to deployment optimization and schema validation.
polymarket
MetaThis skill enables developers to build applications with the Polymarket prediction markets platform, including API integration for trading and market data. It also provides real-time data streaming via WebSocket to monitor live trades and market activity. Use it for implementing trading strategies or creating tools that process live market updates.
creating-opencode-plugins
MetaThis skill helps developers create OpenCode plugins that hook into 25+ event types like commands, files, and LSP operations. It provides the plugin structure, event API specifications, and implementation patterns for JavaScript/TypeScript modules. Use it when you need to intercept, monitor, or extend the OpenCode AI assistant's lifecycle with custom event-driven logic.
sglang
MetaSGLang is a high-performance LLM serving framework that specializes in fast, structured generation for JSON, regex, and agentic workflows using its RadixAttention prefix caching. It delivers significantly faster inference, especially for tasks with repeated prefixes, making it ideal for complex, structured outputs and multi-turn conversations. Choose SGLang over alternatives like vLLM when you need constrained decoding or are building applications with extensive prefix sharing.
