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 skills add Suzhou-Tongyuan/sysplorer-mcp-server --skill ty-design-opt-exp-modelinggit clone --depth 1 https://github.com/Suzhou-Tongyuan/sysplorer-mcp-serverWrote 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/suzhou-tongyuan/sysplorer-mcp-server/ty-design-opt-exp-modeling)<a href="https://agentmods.dev/skills/suzhou-tongyuan/sysplorer-mcp-server/ty-design-opt-exp-modeling"><img src="https://agentmods.dev/badge/skills/suzhou-tongyuan/sysplorer-mcp-server/ty-design-opt-exp-modeling/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/suzhou-tongyuan/sysplorer-mcp-server/ty-design-opt-exp-modeling"><img src="https://agentmods.dev/badge/skills/suzhou-tongyuan/sysplorer-mcp-server/ty-design-opt-exp-modeling.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 | $0.00108 | $0.03480 |
| Opus 5.5 | $0.00043 | $0.01392 |
| Sonnet 5.5 | $0.00022 | $0.00696 |
| Haiku 4.5 | $0.00011 | $0.00348 |
Grade A, and why
ty-design-opt-exp-modeling scanned grade A with 0 findings 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 19d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 273 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DesignOptExp Batch Simulation
Scope
- In scope: Sysplorer DesignOptExp / batch simulation scripting, solver directory preparation, model loading,
InitialBatchSimulate,BatchSimulate, parameter sweeps, and result extraction. - Out of scope: DesignOptMpe parameter estimation, DesignOptMro response optimization, DesignOptPsa sensitivity analysis, and general Modelica/Sysblock modeling.
- Routing priority: When the user wants batch simulation, use the
DesignOptExpinterfaces in this skill first. Do not default to compatibility batch interfaces exposed byDesignOptExpGui.
Hard Rules
- Missing data policy: If required data is missing and cannot be discovered from the model, tool output, local files, or references, explicitly tell the user what is missing and either ask them to provide it or ask whether to use a clearly stated default. Do not silently invent ranges, distributions, targets, bounds, algorithms, simulation times, measurement mappings, or result variables.
- Quiet by default: For actual batch simulation execution, prefer a standalone Python script that starts Sysplorer with
StartSysplorer("-q", ...). Do not call MCPsession_managermerely to run a batch job when the user expects quiet/headless execution, because MCP startup may attach to or launch a visible GUI. - Input confirmation gate: Before running
BatchSimulate, confirm the batch parameters, each parameter's values, result variables, and simulation options. If multiple parameters are provided and the user does not specify a parameter-combination mode, default to row-wise combination. Use full-factorial only when the user explicitly asks for all combinations / Cartesian product. If required parameters, values, or result variables are missing, inspect the model and present candidates instead of choosing them yourself. - No demo-as-result: Example matrices such as
PI1.k=[1, 1.2]are allowed only in documentation or when explicitly labeled as a dry-run/example. Never execute or report them as the user's batch simulation unless the user approved those exact values. - Parameter matrix intake: Accept sampling or experiment-design results as a parameter matrix, including compatible matrices generated when a requested method is not exposed by DesignOptExpGui. Convert each row into
list[dict[paramName, value]]forBatchSimulate. If the user asks where sampling/design data comes from, answer that the data comes from model experiment. - MCP session first only for MCP execution: call
session_manager(action="health" | "ensure")before any MCPrun_sysplorer_apithat touches Sysplorer. If quiet execution is required, use the standalone-qpath instead of MCPrun_sysplorer_api. - Never call
ClearAllorChangeDirectoryfrom generated scripts. Usemodel_manager(action="unload")for cleanup when needed. - DesignOptExp module docs: DesignOptExp is an independent toolbox module. Do not query it via main ModelingPy
get_api_document; use explicit import plusListFunctions()/help()and this skill'sreferences/. - Explicit import: In scripts, import the toolbox explicitly:
What ships with it
7 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.
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.
- 19d ago First seen · 273 lines · 108 tokens per session scan A 2b7e642310b4
ty-design-opt-exp-modeling is a skill published in the GitHub repository Suzhou-Tongyuan/sysplorer-mcp-server (10 stars, last pushed 20d ago), licensed MIT. It adds 108 tokens to every session and 3,480 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-13.
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