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Approximate deep learning model components with symbolic equations using PySR
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
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 | symtorch |
| description | Approximate deep learning model components with symbolic equations using PySR |
| source_type | github |
| auth_required | false |
| repository_url | https://github.com/elizabethsztan/InterpretSR |
| reference_url | https://arxiv.org/abs/2602.21307 |
Approximate deep learning model components with symbolic equations using PySR
https://github.com/elizabethsztan/InterpretSR
Use this as the implementation source: clone the repo and follow its README for install, dependencies, and how to run code or experiments. The generated client prints JSON with a suggested git clone command.
https://arxiv.org/abs/2602.21307
This is the paper reference. The client can optionally fetch live Atom metadata (title, abstract) for agents; it does not run training or upstream research code by itself.
The *_client.py script prints JSON that combines a GitHub repository (clone URL + suggested git clone) with optional paper context from arXiv (live Atom metadata when reference_url is arXiv). Run the real code by cloning the repo and following its README — the skill is your agent-facing entrypoint, not a substitute for the repo’s install steps.
To call a REST API instead, set BASE_URL in scripts/symtorch_client.py or wrap the upstream CLI with subprocess after clone.
Extracted for operators and agents. Confirm against the upstream repository or paper before relying on it in production.
Install SymTorch from PyPI:
pip install torch-symbolic
The README does not document specific CLI commands or entrypoints. Refer to the official documentation at ReadTheDocs for usage examples and API reference.
No environment variables or configuration files are documented in the README. See the accompanying website and full documentation for configuration details.
The same text lives in scripts/USAGE.md for tools that prefer reading files under scripts/.
python3 scripts/symtorch_client.py None
None