Skip to content

VeryMath/AI4Math-Sagemath-skill

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

30 Commits
 
 
 
 
 
 
 
 
 
 

Repository files navigation

AI4Math · SageMath Skill

Verified SageMath computation for mathematical agents: reference lookup, executable Python code, and reproducible results.

中文说明 · Contributors · Skill package · Installation · Quick start · References · Security model

version skills license

If this project helps your work, please consider giving the repository a Star ⭐ GitHub stars

Overview

sagemath-skill helps mathematical agents use SageMath for verified computation. The agent searches the bundled SageMath reference first, writes SageMath code in ordinary Python form, executes the code locally, and reports results backed by real runtime output.

It is designed for exact computation in algebra, number theory, combinatorics, graph theory, polynomial rings, matrices, calculus, finite fields, elliptic curves, Galois groups, coding theory, cryptography, manifolds, modular forms, and related mathematical domains.

Contributors

VeryMath / AI4Math project.

Contributions are welcome for examples, installation notes, reference indexing, and runner improvements.

Skill package

OpenCode / Codex should install the repository's sagemath-skill/ directory as the complete skill package.

Core capabilities:

  • Search the bundled SageMath reference before writing code.
  • Write SageMath code in Python form and avoid Sage REPL preparser-only syntax.
  • Verify computations on macOS / Linux with scripts/sagemath_runner.py.
  • Verify computations on Windows from inside WSL with scripts/sagemath_runner_wsl.py.
  • Guide Miniconda and SageMath 10.9 installation when SageMath is missing.

Installation

In OpenCode Desktop or Codex, ask:

Please install the VeryMath SageMath Skill from this GitHub repository:

https://github.com/VeryMath/AI4Math-Sagemath-skill

The skill directory is sagemath-skill/. Please download the repository and install that directory as the complete skill.

If GitHub access is slow, download the archive first and ask the agent to install sagemath-skill/ from the local path.

Windows users should install WSL first, then install and run SageMath inside WSL. Do not use Windows-native conda for SageMath.

Quick start

After installation, try:

Use sagemath-skill to compute the factorization, discriminant, Galois group, Galois group order, and splitting field degree of x^4 - 2 over QQ. Provide SageMath code, the real runtime output, and a mathematical explanation.

Graph theory smoke test:

Use sagemath-skill to construct a small directed network, verify max-flow min-cut by both the maximum flow algorithm and exhaustive cut enumeration, and report the SageMath runtime output.

References

sagemath-skill/references/api/ contains the processed SageMath text reference. The agent uses scripts/sage_ref_search.py to locate relevant constructors, methods, and examples before writing code.

Useful entry points:

  • sagemath-skill/SKILL.md
  • sagemath-skill/references/domain_index.md
  • sagemath-skill/references/install_sagemath.md
  • sagemath-skill/scripts/sage_ref_search.py

Security model

This skill may guide the agent to execute local commands for reference search, SageMath code execution, or environment installation. Installation, downloads, and conda environment creation should be confirmed by the user first.

On Windows, the intended path uses Linux conda inside WSL. The WSL runner refuses Windows conda exposed under /mnt/c/.... Mathematical results should come from runner output, not from language-model guessing.

About

Teach LLM-agents to use SageMath.

Resources

Stars

Watchers

Forks

Releases

Packages

Contributors

Languages