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Calculix Fem

CommunityPopular
Cai-aa
calculix-fem

Workflow skill for driving the CalculiX MCP server over .inp decks — model inspection, in-place design-variable edits, ccx solve, .dat result reading, and result_mesh.json export for the viewer. Use when an agent must run an open-source linear-static FEM analysis on CalculiX, tune section/material/load parameters, or produce a viewer-renderable result.

Overview

PublisherCai-aa
RepositoryCAE-Agent-Hub
Skill namecalculix-fem
Stars
1K
Forks
130
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Calculix Fem 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/calculix/calculix-fem
  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/calculix/calculix-fem .claude/skills/calculix-fem
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Calculix Fem 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 Calculix Fem 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 Calculix Fem 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.

CalculiX FEM

CalculiX (ccx) is an open-source FEM solver (GPLv2, no license). It reads Abaqus-style .inp decks and writes text .dat results plus .frd field output. ccx returns exit code 0 even on *ERROR, so never judge success by the exit code — the solver layer checks stdout for *ERROR and requires a data row in the .sta.

When to Use

Use this skill when an agent must run an open-source linear-static FEA analysis on CalculiX: parse/inspect a .inp deck, tune section/material/load parameters, solve with ccx, read .dat results, or export a viewer-renderable result. For commercial solvers (Abaqus/Ansys), use those solvers' own skills instead.

Workflow

  1. fea_health — confirm meshio is available and ccx is detected.
  2. parse_inp — inspect the .inp (nodes, elements, sections, materials, loads).
  3. list_design_vars_tool — list tunable variables; each carries a var_id.
  4. modify_card_tool — change a variable by var_id (pure-text, in place).
  5. run_solver_tool — submit the deck to ccx.
  6. read_results_tool — read max von Mises (self-computed), max |U|, volume, mass.
  7. export_results_tool — write result_mesh.json for the viewer.

Rules

  • Units follow the .inp working system (commonly mm-t-s-MPa). Report them.
  • .dat has no von Mises column and no total volume/mass — these are derived.
  • Never rewrite a deck with meshio.write (it drops cards and corrupts element types). Use modify_card_tool.
  • For the deck to produce both text results and the field output the viewer needs, include *NODE PRINT/*EL PRINT (for .dat) and *NODE FILE/*EL FILE (for .frd).

Example

MCP/CalculiX/examples/cantilever.inp is a public cantilever benchmark (steel C3D8 solid bar, clamped end, transverse tip load). Hand-calc targets at P = 100 N: tip deflection ≈ 0.8 mm, root stress ≈ 140 MPa.

Frequently asked questions

What does the Calculix Fem AI skill do?

Workflow skill for driving the CalculiX MCP server over .inp decks — model inspection, in-place design-variable edits, ccx solve, .dat result reading, and result_mesh.json export for the viewer. Use when an agent must run an open-source linear-static FEM analysis on CalculiX, tune section/material/load parameters, or produce a viewer-renderable result.

Why use Calculix Fem on TypingMind?

Because you install it once and use it with any model. Calculix Fem 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 Calculix Fem in TypingMind?

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

Which AI models can use Calculix Fem?

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 Calculix Fem?

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

Is the Calculix Fem 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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