lamm-mit/symtorch
skills/symtorch/SKILL.md
Approximate deep learning model components with symbolic equations using PySR
$ install --global
skillsauthnpx skillsauth add lamm-mit/scienceclaw symtorchInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
Security Scan Results
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
3
Scanners Passed
9
Scanners in report
Clean
TrivyContainer and dependency vulnerability scanner
95%
Clean
SemgrepStatic code analysis for vulnerabilities
95%
Clean
mcp-scan (Snyk)Model Context Protocol security validation
95%
Skipped
Snyk (dep)Open source security scanning
50%
Skipped
Socket.devSupply chain security analysis
50%
Skipped
VirusTotalMulti-engine malware detection
50%
Skipped
CrowdStrikeAdvanced threat intelligence
50%
Skipped
OSV-ScannerOpen Source Vulnerability database check
50%
Skipped
OWASP Dep-Check
50%
Last scanned: Apr 6, 2026, 12:56 PM4.5s3 files scanned
SKILL.md
- 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
symtorch
Approximate deep learning model components with symbolic equations using PySR
Code repository
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.
Paper (arXiv — explanation)
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.
What “running” this client does
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.
How to run the method (from the source)
Extracted for operators and agents. Confirm against the upstream repository or paper before relying on it in production.
Prerequisites
- Python 3.7+
- pip or conda package manager
- PyTorch installation (typically installed as dependency)
Installation
Install SymTorch from PyPI:
pip install torch-symbolic
How to run
The README does not document specific CLI commands or entrypoints. Refer to the official documentation at ReadTheDocs for usage examples and API reference.
Configuration
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/.
Parameters
Usage
python3 scripts/symtorch_client.py None
Example Output
None
Related Skills
lamm-mit/paperclip
tools
VerifiedTrustedCommunity
Onboard and manage Paperclip AI for research-paper knowledge and agent orchestration
203SKILL.mdUpdated May 22, 2026
lamm-mit/perplexity-search
development
VerifiedTrustedCommunity
Perform AI-powered web searches with real-time information using Perplexity models via LiteLLM and OpenRouter. This skill should be used when conducting web searches for current information, finding recent scientific literature, getting grounded answers with source citations, or accessing information beyond the model knowledge cutoff. Provides access to multiple Perplexity models including Sonar Pro, Sonar Pro Search (advanced agentic search), and Sonar Reasoning Pro through a single OpenRouter API key.
184SKILL.mdUpdated Apr 6, 2026
lamm-mit/scientific-report-pdf
testing
VerifiedTrustedCommunity
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.
161SKILL.mdUpdated Apr 16, 2026
lamm-mit/python-exec
development
VerifiedTrustedCommunity
Execute arbitrary Python code and return stdout. NumPy, pandas, scipy, matplotlib, and other scientific libraries are available.
161SKILL.mdUpdated Apr 16, 2026