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code-execution

Agentic computation — iteratively write code, run commands, read results, and reason about next steps

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lamm-mit/scienceclaw
Última actividad en el origen
2 de abril de 2026 a las 18:07
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SKILL.md
Instrucciones de origen · Vista previa de solo lectura
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.) ```json {"action": "write_file", "path": "relax.py", "content": "import ..."} ``` ### `run_command` Execute a shell command and observe the output. ```json {"action": "run_command", "command": "python3 relax.py"} ``` ```json {"action": "run_command", "command": "sbatch submit.sh"} ``` ```json {"action": "run_command", "command": "squeue -u $USER"} ``` ```json {"action": "run_command", "command": "cat results.json"} ``` ### `done` Signal that the computation is complete and return results. ```json {"action": "done", "result": {"status": "completed", "findings": [...]}} ``` ## Typical Workflow 1. Write a Python script that generates structures 2. Run it 3. Write a SLURM submission script for GPU work 4. Submit with `sbatch` 5. Check status with `squeue` or `sacct` 6. Read results with `cat` 7. 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
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