Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/WolframResearch/system-modeler-ai-toolkitnpx agentmods add skills/wolframresearch/system-modeler-ai-toolkit/simulate-and-plot-modelicaWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/wolframresearch/system-modeler-ai-toolkit/simulate-and-plot-modelica)<a href="https://agentmods.dev/skills/wolframresearch/system-modeler-ai-toolkit/simulate-and-plot-modelica"><img src="https://agentmods.dev/badge/skills/wolframresearch/system-modeler-ai-toolkit/simulate-and-plot-modelica/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/wolframresearch/system-modeler-ai-toolkit/simulate-and-plot-modelica"><img src="https://agentmods.dev/badge/skills/wolframresearch/system-modeler-ai-toolkit/simulate-and-plot-modelica.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00170 | $0.05797 |
| Opus 5.5 | $0.00068 | $0.02319 |
| Sonnet 5.5 | $0.00034 | $0.01159 |
| Haiku 4.5 | $0.00017 | $0.00580 |
Grade C, and why
simulate-and-plot-modelica scanned grade C with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
shell-agnostic. For cleanup use `Remove-Item -Recurse -Force`, not `rm -rf`. How it starts
The opening of the file, as written. The whole thing — 446 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Simulate and Plot
This skill simulates a Modelica model and plots the results: it runs the
simulation to produce a .mat, then reads chosen variables from it with DyMat
and plots them over time. No reference data is needed.
Scope check before starting: if the request goes beyond plotting —
verifying limits or requirements, finding violations, parameter sweeps, Monte
Carlo, fitting/calibration — and Wolfram Language is available (e.g. a Wolfram
MCP tool or wolframscript), hand the task to the wolfram-language-modelica
skill instead: the built-in SystemModel* functions do that analysis natively.
Use this skill's Python analysis path only when Wolfram Language is not
available.
Prerequisites
plot_mat.py needs DyMat (reads .mat), matplotlib, numpy and scipy. You do not
install these by hand: the script self-provisions a managed venv on first use (it never touches
system Python). To pre-warm it: python3 "<scripts-dir>/bootstrap_env.py".
Before you run anything
This skill drives WSMKernelX through the shared launcher
../scripts/wsm_run.py. Read the shared-conventions appendix at the end of this file
first — launcher resolution, the Windows-vs-Unix shell/Python rules, the
temp-dir and cleanup conventions, and the MSL 4.x dialect notes that every
step below assumes.
Temporary Directory
The launcher writes into _wsm_simulate_temp/ next to the model file (or pass
--tempdir "<repo-root>/_wsm_simulate_temp" to reuse one dir across models in a
session). Tell the user: "Working in _wsm_simulate_temp/. Will be deleted at
end of session."
Workflow
1. Identify the model
The user may provide a path to a .mo file, or a fully qualified model name (for
a model that lives in a loaded library).
2. Simulate
Run the launcher to produce the .mat:
python3 "<scripts-dir>/wsm_run.py" --mode simulate \
--model "<path-to-ModelFile.mo>" --name <FullyQualifiedModelName> \
--tempdir "<temp-dir>" --timeout 180 2>&1 | grep -E "succeeded|stopped|events:|out.json"
mat_file=$(ls "<temp-dir>"/*.mat | head -1)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago Changed · +4 lines 840f9c6c76a3
- 23d ago Changed · +108 lines 5b4b523cfb00
- 1mo ago First seen · 334 lines · 170 tokens per session scan C ef51d03d5b42
simulate-and-plot-modelica is a skill published in the GitHub repository WolframResearch/system-modeler-ai-toolkit (17 stars, last pushed 9d ago), licensed MIT. It adds 170 tokens to every session and 5,797 once invoked, about $0.0007 per session on Opus 5.5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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