Sagemath

SageMath

Windows Runtime Commands

On native Windows, use the managed Windows runner and the native runtime command target. Set $runtime to the installed runtime root. Multi-agent installs usually use %LOCALAPPDATA%\ai-agents-skills\runtime. Then run:

$runtime = if ($env:AAS_RUNTIME_ROOT) { $env:AAS_RUNTIME_ROOT } else { "$env:LOCALAPPDATA\ai-agents-skills\runtime" }
& "$runtime\run_skill.ps1" "skills/sagemath/run_sage.ps1" <args>

POSIX examples below use run_skill.sh and .sh command targets; use the Windows command target above on native Windows.

This uses the managed ai-agents-skills runtime copy of the SageMath workflow.

If sage is not on the non-interactive WSL PATH, set the executable path explicitly before running the wrapper. Bash aliases in ~/.bashrc are not visible to the Windows runtime wrapper.

$env:AAS_SAGE_WSL_DISTRO = "Ubuntu-24.04"
$env:AAS_SAGE_BIN = "/path/to/sage-10.4/sage"
& "$runtime\run_skill.ps1" "skills/sagemath/run_sage.ps1" "print(2+2)"

Alternatively, make sage a real executable in WSL:

wsl.exe -d Ubuntu-24.04 -e bash -lc 'sudo ln -sfn "$HOME/sage-10.4/sage" /usr/local/bin/sage && sage --version'

When to use

• chromatic polynomial or chromatic number computations on nontrivial graph families
• Tutte polynomial
• automorphism groups and isomorphism-heavy checks
• spectral analysis
• finite fields or polynomial algebra
• exhaustive or batch mathematical verification that is beyond lightweight local Python

For simple checks such as connectivity, bipartiteness, or small ad hoc scripts, prefer local Python first.

Base path

• $AAS_RUNTIME_WORKSPACE/skills/sagemath/

Use the managed runtime runner rather than invoking run_sage.sh directly.

Shared runner:

• bash "${AAS_RUNTIME_ROOT:-$HOME/.local/share/ai-agents-skills/runtime}/run_skill.sh"

Core commands

Use functions.exec_command.

bash "${AAS_RUNTIME_ROOT:-$HOME/.local/share/ai-agents-skills/runtime}/run_skill.sh" skills/sagemath/run_sage.sh "<sage_code>"

bash "${AAS_RUNTIME_ROOT:-$HOME/.local/share/ai-agents-skills/runtime}/run_skill.sh" skills/sagemath/run_sage.sh --timeout 1800 "<sage_code>"

bash "${AAS_RUNTIME_ROOT:-$HOME/.local/share/ai-agents-skills/runtime}/run_skill.sh" skills/sagemath/run_sage.sh --file skills/sagemath/templates/<template>.sage

bash "${AAS_RUNTIME_ROOT:-$HOME/.local/share/ai-agents-skills/runtime}/run_skill.sh" skills/sagemath/run_sage.sh --file skills/sagemath/templates/reconfiguration_check.sage

bash "${AAS_RUNTIME_ROOT:-$HOME/.local/share/ai-agents-skills/runtime}/run_skill.sh" skills/sagemath/run_sage.sh --plot "<sage_code>"

bash "${AAS_RUNTIME_ROOT:-$HOME/.local/share/ai-agents-skills/runtime}/run_skill.sh" skills/sagemath/run_sage.sh --session "<name>" "<sage_code>"

bash "${AAS_RUNTIME_ROOT:-$HOME/.local/share/ai-agents-skills/runtime}/run_skill.sh" skills/sagemath/run_sage.sh --cancel <job_id>

Templates

Common templates in skills/sagemath/templates/:

• enumerate_chromatic.sage
• counterexample_search.sage
• spectral_analysis.sage
• reconfiguration_check.sage

Operational notes

• The OpenClaw SageMath job runs inside Docker with no network access.
• Results are returned as JSON.
• Prefer this skill when correctness depends on SageMath-native graph or algebra routines rather than lightweight heuristics.
• Treat this SKILL.md and sage_reference.md as the primary quick reference for the wrapper; the wrapper’s default interface is execution-oriented rather than documentation-oriented.

Recommended templates

When this skill is involved, consider this workflow template (install via the workflow-templates artifact profile, or --with-deps to pull backing skills):

• tikz-figure-verification-runbook -- Bounded draw-compile-verify-redraw loop for a TikZ figure that guarantees it is free of overlap, wrong meaning, and bad layout, with Sage-assisted graph realization and fresh-agent visual confirmation before the strict approval gate.