Getting it into your agent
There is no command for this one: it runs only inside a plugin, and the catalogue could not identify which plugin ships it. The source is linked below.
Wrote 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/validate-modelica)<a href="https://agentmods.dev/skills/wolframresearch/system-modeler-ai-toolkit/validate-modelica"><img src="https://agentmods.dev/badge/skills/wolframresearch/system-modeler-ai-toolkit/validate-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 5.1 | $0.00106 | $0.03392 |
| Opus 5 | $0.00053 | $0.01696 |
| Sonnet 5 | $0.00021 | $0.00678 |
| Haiku 4.5 | $0.00011 | $0.00339 |
Grade C, and why
validate-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 6d 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 — 252 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Validate Modelica Model
This skill validates Modelica models (.mo files) by flattening them with WSMKernelX, which checks for structural errors (equations, types, connections). A successful flatten means the model is structurally valid.
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, the JSON-array output gotcha, and the MSL 4.x
dialect notes that every step below assumes.
The launcher writes everything into _wsm_validate_temp/ next to the .mo
file and leaves validate.out.json there for you to parse. Tell the user:
"Working in temporary directory _wsm_validate_temp/. This will be deleted
after validation."
Workflow
1. Identify the model file and name
Identify the .mo file and extract the model name — see Appendix → Picking the model name. For a directory-form (multi-file) library, point --model at the library folder (not one class file) and pass the full dotted --name — see Appendix → Directory-form (multi-file) libraries.
2. Run the launcher
python3 "<scripts-dir>/wsm_run.py" --mode validate \
--model "<path-to-ModelFile.mo>" --name ModelName --timeout 60
The launcher auto-detects whether the model needs the Modelica Standard
Library and loads the right MSL version; force it with --msl yes|no or pin a
version with --msl-version 4.1.0. If the model uses an installed non-MSL
library (e.g. Hydraulic), add --load-library <Name> — see
Appendix → Using non-MSL libraries.
Timeout: allow up to 60 seconds — complex models with many components can take a while to flatten.
If the launcher can't find the System Modeler install, see Appendix → When the install or compiler isn't found.
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.
- 6d ago First seen · 252 lines · 106 tokens per session scan C 2403abc2a8b1
validate-modelica is a skill published in the GitHub repository WolframResearch/system-modeler-ai-toolkit (10 stars, last pushed 16d ago), licensed MIT. It adds 106 tokens to every session and 3,392 once invoked, about $0.0005 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…
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.
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…
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.
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
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…