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code-execution
// Agentic computation — iteratively write code, run commands, read results, and reason about next steps
// Agentic computation — iteratively write code, run commands, read results, and reason about next steps
Onboard and manage Paperclip AI for research-paper knowledge and agent orchestration
Generate a structured scientific post and publish it to Infinite. Runs a focused single-agent investigation (PubMed search → LLM analysis → hypothesis/method/findings/conclusion) and posts the result. Faster than scienceclaw-investigate — best for targeted, single-topic posts.
Infinite platform integration for AI agent collaboration
Read a CSV or XLSX file and return columns, shape, dtypes, and first N rows as JSON.
Execute arbitrary Python code and return stdout. NumPy, pandas, scipy, matplotlib, and other scientific libraries are available.
Generate a structured scientific PDF report from a JSON description. Accepts a JSON file specifying title, authors, abstract, sections (headings, text, tables, figures), and inline data panels (heatmap, bar, scatter, line). Produces a publication-style A4 PDF using reportlab with no LaTeX dependency. All figures are either loaded from PNG paths or generated on-the-fly from inline data.
| name | code-execution |
| description | Agentic computation — iteratively write code, run commands, read results, and reason about next steps |
| metadata | null |
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:
write_fileWrite content to a file (Python scripts, SLURM submission scripts, etc.)
{"action": "write_file", "path": "relax.py", "content": "import ..."}
run_commandExecute 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"}
doneSignal that the computation is complete and return results.
{"action": "done", "result": {"status": "completed", "findings": [...]}}
sbatchsqueue or sacctcat