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/annotate-control-panelnpx skills add WolframResearch/system-modeler-ai-toolkit --skill annotate-control-panelgit 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/annotate-control-panel)<a href="https://agentmods.dev/skills/wolframresearch/system-modeler-ai-toolkit/annotate-control-panel"><img src="https://agentmods.dev/badge/skills/wolframresearch/system-modeler-ai-toolkit/annotate-control-panel.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 | $0.00142 | $0.05307 |
| Opus 5 | $0.00071 | $0.02653 |
| Sonnet 5 | $0.00028 | $0.01061 |
| Haiku 4.5 | $0.00014 | $0.00531 |
Grade C, and why
annotate-control-panel 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 — 402 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Annotate Control Panel
This skill writes the Wolfram control-panel (Explore) annotation into a Modelica .mo file so a
model carries its own interactive panels — the sliders, checkboxes, input fields and popup menus that
Simulation Center's Explore view shows for driving parameters and start values, plus the plots to
display:
annotation(__Wolfram(ControlPanels(
Panel(
identifier = "id1",
title = "Controller tuning",
elements = {
Slider(variable = Kp, label = "Proportional gain", min = 0, max = 100),
Slider(variable = Kd, min = 0, max = 20, scale = Log()),
Checkbox(variable = useFeedforward, label = "Feedforward")},
figures = {"response"})));
It is a self-contained source transform: Claude assembles a control-panel spec (JSON) — from the
user's request, or suggested from the model's parameters — and the Python engine renders it to the
__Wolfram(ControlPanels(...)) vendor annotation and splices it into the class annotation. The engine
is pure Python (standard library only); WSMKernelX is used only afterwards as a validation gate.
This is a vendor-specific annotation (Modelica spec §18.1): other tools ignore it and preserve it
on save, and it does not affect flattening — it just tells System Modeler how to build the Explore
panels. It sits directly inside annotation(...), as a sibling of experiment and Documentation
(never nested inside Documentation).
It is idempotent: a class that already has control panels is skipped unless you pass --force
(which strips the old ControlPanels block and regenerates). By default it prints a dry-run diff;
nothing is written until --write.
--forceis destructive — ask the user first. It deletes the class's existing control panels before regenerating, so any hand-tuned panels there are lost. Don't pass--forceon a class that already carries control panels without confirming that discarding them is intended. Review the dry-run diff (no--write) to see exactly what would be removed.
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- ControlPanel/__init__.py 533 B runs code
- ControlPanel/discover.py 4.3 KB runs code
- ControlPanel/inject.py 3.7 KB runs code
- ControlPanel/main.py 10 KB runs code
- ControlPanel/panels.py 9.3 KB runs code
- ControlPanel/parser.py 1.1 KB runs code
- ControlPanel/simvars.py 1.8 KB runs code
- ControlPanel/suggest.py 2.4 KB runs code
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 · 402 lines · 142 tokens per session scan C b403af3928df
annotate-control-panel is a skill published in the GitHub repository WolframResearch/system-modeler-ai-toolkit (10 stars, last pushed 15d ago), licensed MIT. It adds 142 tokens to every session and 5,307 once invoked, about $0.0007 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…