Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/wolframresearch/system-modeler-ai-toolkit/simulate-and-plot-modelicanpx skills add WolframResearch/system-modeler-ai-toolkit --skill simulate-and-plot-modelicagit clone --depth 1 https://github.com/WolframResearch/system-modeler-ai-toolkitWrote 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.svg" alt="Measured on agentmods" 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 | $0.00170 | $0.04296 |
| Opus 5 | $0.00085 | $0.02148 |
| Sonnet 5 | $0.00034 | $0.00859 |
| Haiku 4.5 | $0.00017 | $0.00430 |
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 5d 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 — 334 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.
- 5d 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 (10 stars, last pushed 15d ago), licensed MIT. It adds 170 tokens to every session and 4,296 once invoked, about $0.0009 per session on Opus 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.
Other skills, from other repositories
instrument-data-to-allotrope
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…
exploratory-data-analysis
Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…
matlab
Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
phylogenetics
Build and analyze phylogenetic trees using MAFFT (multiple alignment), IQ-TREE 2 (maximum likelihood), and FastTree (fast NJ/ML). Visualize with ETE3 or FigTree. For evolutionary analysis, microbial genomics, viral phylodynamics, protein family analysis, and molecular clock studies.
mapping-to-snomed
Maps clinical concept spans extracted by OpenMed to SNOMED CT concepts through a USER-SUPPLIED terminology server (the user's own Ontoserver, Snowstorm, or UMLS/UTS), never a bundled vocabulary. Use when the user wants to code findings, disorders, procedures, body structures, or substances to SNOMED CT, run an ECL…
auditing-subgroup-fairness
Audit an OpenMed NER or de-identification model for performance disparities across demographic subgroups (sex, age band, race/ethnicity when available) using openmed.eval.fairnessreport. Use when the user wants per-subgroup recall and leakage, wants to check whether de-identification under-protects a group, wants to…