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Fluent Cfd

Community
cavoiie
fluent-cfd

Guide Ansys Fluent and PyFluent CFD simulation workflows with MCP tool-use discipline. Use when Codex works on Fluent, PyFluent, Fluent TUI, CFD or fluid simulation setup, case/data files, mesh quality, boundary conditions, turbulence models, residual convergence, UDFs, post-processing, solver diagnostics, or validation of Ansys Fluent results.

Overview

Publishercavoiie
Repositoryfluent-cfd-skill
Skill namefluent-cfd
Stars
86
Forks
7
Bundled files
12
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.

  • 12 bundled files

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

  • Open source

    Published by cavoiie on GitHub. Read the source before you install it.

Installation

Install the Fluent Cfd 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/cavoiie/fluent-cfd-skill/tree/main
  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/cavoiie/fluent-cfd-skill.git \
  .claude/skills/fluent-cfd
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Fluent Cfd 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 Fluent Cfd 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 Fluent Cfd 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.

Fluent CFD

Use this skill as the workflow and judgment layer for Ansys Fluent work. Keep Fluent documentation as the fact source, use the local pyfluent MCP server as the execution layer, and use this skill to decide the order of operations, checks, and validation criteria.

Operating Rules

  • Define the physics before touching the solver: objective quantity, geometry, units, fluid, regime, steady/transient behavior, compressibility, heat transfer, multiphase, rotating zones, and expected validation evidence.
  • Do not treat residual decrease alone as convergence. Require monitor stabilization and conservation checks for the quantities that matter.
  • Do not skip mesh quality, units, boundary-zone naming, boundary-condition consistency, and wall-resolution checks.
  • Do not change turbulence models without a physical reason or a stated validation purpose.
  • Prefer safe MCP tools and explicit Fluent TUI commands. Use execute_python only when PYFLUENT_MCP_ENABLE_PYTHON=1 and the workflow is trusted.
  • Before starting Fluent or consuming a license, state that the action will launch Fluent and may occupy a license/session.
  • End live Fluent sessions with exit_fluent when the task is complete unless the user asks to keep the session open.

Workflow

  1. Frame the case: identify objective metrics, physics, known inputs, missing data, expected outputs, and acceptance criteria.
  2. Choose solver and models: decide pressure-based vs density-based, steady vs transient, energy, multiphase, turbulence, wall treatment, and reference values.
  3. Check the mesh and setup basis: units, cell/face zones, boundary names, quality metrics, non-orthogonality/skewness, and y+ target.
  4. Set or review materials, boundary conditions, operating/reference conditions, numerics, initialization, and monitors.
  5. Run a smoke test first: a short iteration/time-step run to catch setup errors before a full solve.
  6. Iterate or advance time while monitoring residuals, integral balances, and objective quantities.
  7. Save case/data and report validation status, remaining risks, and next checks.

Reference Loading

Load only the reference files needed for the current task:

  • references/workflow.md: complete Fluent workflow and task routing.
  • references/solver-selection.md: model and solver selection rules.
  • references/boundary-conditions.md: inlet, outlet, wall, symmetry, periodic, and rotating-zone boundary checks.
  • references/turbulence-and-wall-treatment.md: turbulence model, wall treatment, y+, and mesh implications.
  • references/numerics-and-convergence.md: discretization, initialization, monitors, residuals, and convergence.
  • references/pyfluent-mcp.md: local pyfluent MCP tools, call order, safety limits, and failure handling.
  • references/validation-checklist.md: conservation, mesh independence, physical plausibility, and post-processing checks.
  • references/error-recovery.md: divergence, reversed flow, negative volume, CFL, residual stalls, and launch/license failures.

MCP Tool Discipline

When a task requires real Fluent execution through the local MCP server:

  1. Call server_info first to confirm PyFluent, Ansys roots, and whether Python execution is enabled.
  2. Call launch_fluent only after stating that Fluent will start and may consume a license.
  3. Use read_case for existing .cas, .cas.h5, or equivalent case files.
  4. Use run_tui for explicit, auditable Fluent TUI commands.
  5. Use iterate for a defined iteration count; prefer a short smoke run before long runs.
  6. Use session_status after suspicious behavior or long operations.
  7. Use write_case_data before closing if the state should be preserved.
  8. Use exit_fluent at the end of the workflow unless the user explicitly wants the session left open.

If a tool returns an error, stop the automation sequence, summarize the failure, read references/error-recovery.md if relevant, and propose the smallest safe recovery.

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 Fluent Cfd AI skill do?

Guide Ansys Fluent and PyFluent CFD simulation workflows with MCP tool-use discipline. Use when Codex works on Fluent, PyFluent, Fluent TUI, CFD or fluid simulation setup, case/data files, mesh quality, boundary conditions, turbulence models, residual convergence, UDFs, post-processing, solver diagnostics, or validation of Ansys Fluent results.

Why use Fluent Cfd on TypingMind?

Because you install it once and use it with any model. Fluent Cfd 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 Fluent Cfd in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cavoiie/fluent-cfd-skill/tree/main. 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 Fluent Cfd?

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 Fluent Cfd?

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

Is the Fluent Cfd AI skill free?

Yes. It is published on GitHub by cavoiie 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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