Calculix Modal Analysis

CommunityPopular

Cai-aa

calculix-modal-analysis

Workflow skill for CalculiX modal analysis (*FREQUENCY steps) — natural frequencies and mode shapes via the CalculiX MCP. read_results returns the frequency table; export_results with mode=N renders that mode shape in the browser viewer. Use when an agent must extract natural frequencies, check resonance margins, or visualize mode shapes on an open-source solver.

Overview

Publisher	Cai-aa
Repository	`CAE-Agent-Hub`
Skill name	`calculix-modal-analysis`
Stars	1K
Forks	130
Bundled files	Instructions only
License	MIT
Links	Skill source Repository

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 Modal Analysis AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/Cai-aa/CAE-Agent-Hub/tree/main/Skill/calculix/calculix-modal-analysis
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/Cai-aa/CAE-Agent-Hub/tree/main/Skill/calculix/calculix-modal-analysis

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

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-modal-analysis .claude/skills/calculix-modal-analysis

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Calculix Modal Analysis 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 Modal Analysis 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 Modal Analysis 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 Modal Analysis

Free-vibration eigenanalysis on a CalculiX deck: a *FREQUENCY step requests N eigenpairs; ccx prints the eigenvalue table and per-mode eigenvectors to the .dat, so both frequencies and mode shapes parse from text (no .frd needed).

When to Use

Use when an agent must extract natural frequencies, check resonance/vibration margins, or visualize mode shapes on a CalculiX model. Driven by the same MCP tools as static runs (run_solver → read_results → export_results).

Not for: static stress/deflection (use calculix-fem), transient/dynamic response (not yet supported), or sizing optimization (calculix-sizing-optimization).

Workflow

Confirm the deck has *DENSITY under its *MATERIAL — frequencies need mass; without density ccx fails the eigenvalue solve.
Confirm the step is *FREQUENCY with the wanted mode count on its data line, and *NODE PRINT, NSET=<all> / U so eigenvectors reach the .dat. No load is applied (free vibration); keep the *BOUNDARY clamp set.
run_solver_tool — submit the deck. Note: a *FREQUENCY run writes a header-only .sta (no increments in an eigenvalue solve); success accepts a non-empty .dat instead, so this is normal, not a failure.
read_results_tool — returns frequencies as [{mode, eigenvalue, freq_rad_s, freq_hz}] and n_modes (a doubly symmetric section gives degenerate pairs — f1 = f2 — which is expected).
export_results_tool with mode=N — writes result_mesh.json holding that mode's eigenvector as a stress-free displacement field; the viewer renders the mode shape with its usual auto-scaled deformation.

Rules

Units follow the .inp (commonly mm-t-s-MPa → frequencies in Hz).
Eigenvectors are mass-normalized; their magnitude carries no physical displacement meaning — only the shape does. The viewer auto-scales.
Fully-integrated C3D8 hexes shear-lock in bending: ccx frequencies run ~5-10% ABOVE the Euler-Bernoulli hand calc. For tight margins, refine the mesh through the thickness (or switch element type) and report the gap.
Hand-calc check for a clamped-free bar: f_n = (beta_n^2 / 2pi) * sqrt(E I / (rho A L^4)), beta_1 = 1.8751.

Example

MCP/CalculiX/examples/cantilever_modal.inp is the public cantilever benchmark with a 5-mode *FREQUENCY step. ccx gives f1 = f2 ~ 502 Hz (degenerate bending pair on the square section) vs the Euler-Bernoulli hand calc ~ 464 Hz (+8%, C3D8 shear locking). Mode 1 exports straight into the viewer as the classic half-sine bend.

Frequently asked questions

What does the Calculix Modal Analysis AI skill do?

Why use Calculix Modal Analysis on TypingMind?

Because you install it once and use it with any model. Calculix Modal Analysis 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 Modal Analysis 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-modal-analysis. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Calculix Modal Analysis?

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 Modal Analysis?

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

Is the Calculix Modal Analysis 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.

View all

Clawdtributor

openclaw

OrganizationPopular

Clawtributor PRs here, last week or another window: discover conversation refs, recheck GitHub, rank by impact.

Control Ui E2e

Use when designing, testing, fixing, or extending the OpenClaw Control UI GUI, including UI stress-test galleries with feedback inputs, Vitest + Playwright end-to-end checks, mocked Gateway flows,…

Discord E2e

Test Discord with Convex-leased QA bots: check credentials without a Gateway, exercise owned messages, or prove OpenClaw replies, files, threads, reactions, streaming, and typing. Distinguish API and…

Openclaw Qa Testing

Run, watch, debug, extend, or explain OpenClaw qa-lab and qa-channel scenarios, artifacts, and live lanes.

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

CalculiX Modal Analysis

When to Use

Workflow

Rules

Example

Frequently asked questions

Related AI Skills

Set up your own AI workspace now