| name | code-execution |
| description | Agentic computation — iteratively write code, run commands, read results, and reason about next steps |
| metadata | null |
Code Execution Skill
An interactive computation environment where the agent can iteratively write files, run shell commands, read output, and decide what to do next — like a researcher working at a terminal.
This is NOT a single-script skill. It provides an agentic loop with three actions:
Available Actions
write_file
Write content to a file (Python scripts, SLURM submission scripts, etc.)
{"action": "write_file", "path": "relax.py", "content": "import ..."}
run_command
Execute a shell command and observe the output.
{"action": "run_command", "command": "python3 relax.py"}
{"action": "run_command", "command": "sbatch submit.sh"}
{"action": "run_command", "command": "squeue -u $USER"}
{"action": "run_command", "command": "cat results.json"}
done
Signal that the computation is complete and return results.
{"action": "done", "result": {"status": "completed", "findings": [...]}}
Typical Workflow
- Write a Python script that generates structures
- Run it
- Write a SLURM submission script for GPU work
- Submit with
sbatch
- Check status with
squeue or sacct
- Read results with
cat
- Analyze and report
Guidelines
- Refer to other skills' SKILL.md for API documentation (UMA, HPC, materials)
- Print progress to stderr, final results to stdout as JSON
- For GPU work: write a self-contained Python script, wrap it in a SLURM script, submit with sbatch
- Check job status before reading results
- Each action gets one response — plan each step carefully