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Comsol Motor Nvh Evidence

CommunityPopular
Cai-aa
comsol-motor-nvh-evidence

Evidence-first COMSOL Java API and batch workflow for permanent-magnet motor NVH. Use when Codex must build, resume, solve, validate, or report a 2D motor model that couples rotating magnetic machinery, Maxwell-force harmonics, structural eigenmodes, pressure acoustics, acoustic-structure interaction, far-field SPL, or Campbell sweeps; also use when converting an existing motor NVH prompt or run into public-safe scripts, checkpoints, exports, and acceptance evidence.

Overview

PublisherCai-aa
RepositoryCAE-Agent-Hub
Skill namecomsol-motor-nvh-evidence
Stars
1K
Forks
130
Bundled files
5
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Cai-aa on GitHub. Read the source before you install it.

Installation

Install the Comsol Motor Nvh Evidence AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Cai-aa/CAE-Agent-Hub/tree/main/Skill/comsol/comsol-motor-nvh-evidence
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Cai-aa/CAE-Agent-Hub.git /tmp/CAE-Agent-Hub
mkdir -p .claude/skills
cp -r /tmp/CAE-Agent-Hub/Skill/comsol/comsol-motor-nvh-evidence .claude/skills/comsol-motor-nvh-evidence
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Comsol Motor Nvh Evidence in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Comsol Motor Nvh Evidence on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Comsol Motor Nvh Evidence is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

COMSOL Motor NVH Evidence

Execute a checkpointed motor-NVH workflow and prove each completed stage with solver-native evidence. Treat reference models as read-only fact sources, never as the target model.

Required context

  1. Read MCP/COMSOL/README.md and inspect only the files needed for the selected COMSOL backend.
  2. Use the repository COMSOL MCP for installation detection, MPh session startup, model inspection, parameter changes, study execution, evaluation, export, and save-copy operations. Use comsolcompile plus COMSOL batch only when the independent Java API build requires capabilities not exposed by typed MCP tools.
  3. Read references/model-contract.md before creating geometry, physics, studies, or result nodes.
  4. Read references/evidence-gates.md before solving or claiming success.

Execution sequence

  1. Inspect the environment without writing to reference models.
  2. Create a new run directory. Use scripts/New-ComsolMotorNvhRun.ps1 when a project scaffold is needed.
  3. Extract source-backed facts once into facts.json; use that compact file in later stages.
  4. Create the target with ModelUtil.create() for the independent Java API route, or use the MCP MPh session route for an existing user-owned model. Parameterize geometry, selections, materials, physics, mesh, studies, and exports.
  5. Build without solving. Stop if environment, topology, selections, material tables, physics compilation, or mesh gates fail.
  6. Compile with comsolcompile; do not substitute ordinary javac.
  7. Solve structural modes and save a new checkpoint.
  8. Solve the time-periodic electromagnetic field and force harmonics; save a new checkpoint.
  9. Run the mandatory reduced NVH smoke matrix. Do not start the full sweep until every smoke value is finite and traceable.
  10. Run the full speed/harmonic sweep, in batches when memory is constrained.
  11. Postprocess from solved checkpoints. Fix result expressions or export nodes without rerunning physics unless solution evidence is invalid.
  12. Validate exported CSV data with scripts/validate_nvh_exports.py, scan run evidence, and generate the report.

For the MPh route, start with comsol_detect_tool, comsol_mph_availability_tool, comsol_start_session_async_tool, and comsol_start_session_status_tool. Continue with the session-scoped open, summary, parameter, solve, evaluate, export, and save tools. Detection alone is not license proof; require a real solver-native operation.

Checkpoint contract

Use monotonic checkpoints such as:

text
models/01_built.mph
models/02_modes_solved.mph
models/03_em_solved.mph
models/04_smoke_solved.mph
models/05_final_solved.mph

Resume only when the checkpoint, corresponding successful solver log, and validation record all exist. File presence alone is insufficient.

Modeling guardrails

  • Use named selections for physics assignments. Use numeric entity IDs only as topology diagnostics for the current build.
  • Preserve the verified finalization method, usually Form Assembly with identity pairs for the motor/acoustic partition. Do not switch to Form Union to silence a pairing problem.
  • Do not invent missing magnet, winding, B-H, support, boundary, or observation-point data.
  • Do not change materials, polarity, winding phase, force scale, damping, or acoustic pressure to match a reference curve.
  • Keep unsolved Campbell cells missing/null; never convert them to 0 dB.
  • Do not describe scan-line, interpolation, or dashboard effects as physical deformation.
  • Do not claim a study solved unless the COMSOL exit code, log, checkpoint, and expected numerical exports agree.

Failure policy

Use PENDING, INSPECTED, CONFIGURED, BUILT, COMPILED, SOLVED, EXPORTED, VERIFIED, FAILED, or BLOCKED for stages.

Allow automatic repair only for paths, quoting, version-specific API names, missing output directories, result expressions, export-node compatibility, and machine-specific solver allocation. Never auto-repair the physical definition to improve agreement.

Report final status only as PASSED, PASSED_WITH_LIMITATIONS, FAILED, or BLOCKED.

Public-safe output

  • Keep executable paths in environment variables or ignored private configuration.
  • Do not commit .mph, .class, solver logs, commercial manuals, source reference models, videos, or large generated datasets.
  • Remove usernames, attachment identifiers, workstation paths, license-server details, and temporary-directory names.
  • Commit only reusable scripts, relative-path examples, small synthetic/sample tables, and documentation.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Comsol Motor Nvh Evidence AI skill do?

Evidence-first COMSOL Java API and batch workflow for permanent-magnet motor NVH. Use when Codex must build, resume, solve, validate, or report a 2D motor model that couples rotating magnetic machinery, Maxwell-force harmonics, structural eigenmodes, pressure acoustics, acoustic-structure interaction, far-field SPL, or Campbell sweeps; also use when converting an existing motor NVH prompt or run into public-safe scripts, checkpoints, exports, and acceptance evidence.

Why use Comsol Motor Nvh Evidence on TypingMind?

Because you install it once and use it with any model. Comsol Motor Nvh Evidence is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Comsol Motor Nvh Evidence in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Cai-aa/CAE-Agent-Hub/tree/main/Skill/comsol/comsol-motor-nvh-evidence. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Comsol Motor Nvh Evidence?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Comsol Motor Nvh Evidence?

As many as you like. As long as a model supports skills, you can use Comsol Motor Nvh Evidence with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Comsol Motor Nvh Evidence AI skill free?

Yes. It is published on GitHub by Cai-aa under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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