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Discretize

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SteadfastAsArt
discretize

Create finite-volume meshes and discrete differential operators with discretize. Use for TensorMesh or TreeMesh geometry, cell/face/edge ordering, divergence, interpolation and meshes supporting SimPEG; use a physics solver for inversion itself.

Overview

PublisherSteadfastAsArt
Repositorygeoscience-skills
Skill namediscretize
Stars
75
Forks
9
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 SteadfastAsArt on GitHub. Read the source before you install it.

Installation

Install the Discretize 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/SteadfastAsArt/geoscience-skills/tree/main/discretize
  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/SteadfastAsArt/geoscience-skills.git /tmp/geoscience-skills
mkdir -p .claude/skills
cp -r /tmp/geoscience-skills/discretize .claude/skills/discretize
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Discretize 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 Discretize 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 Discretize 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.

discretize

Construct the mesh and location-aware operators before attaching physical data. The library does not determine the CRS or the physical units of its coordinates.

Choose geometry and data locations

Use TensorMesh for separable rectangular grids; choose TreeMesh when local refinement substantially reduces cost. Finalize a tree before using its operators. Pad model boundaries and refine sources, receivers and property contrasts as required by the forward problem, then test domain and resolution sensitivity.

Record axis meanings, origin, units, cell widths and active-cell mask. In a 2D vertical section the second coordinate can mean elevation, but this is a project convention. A vector of length n_cells is not a face or edge vector.

Divergence with an independent answer

The synthetic velocity field is u = (2x, -3y) per second. Its divergence is -1 s⁻¹, including on this nonuniform mesh. Boundary fluxes are represented at their actual face locations; no zero boundary condition is imposed implicitly.

python
# example: nonuniform-divergence
import numpy as np
from discretize import TensorMesh

mesh = TensorMesh([[1.0, 2.0, 3.0], [2.0, 4.0]], origin=[0.0, 0.0])
face_velocity_m_s = np.r_[2 * mesh.faces_x[:, 0], -3 * mesh.faces_y[:, 1]]
divergence_s_inv = mesh.face_divergence @ face_velocity_m_s
integrated_divergence_m2_s = np.dot(mesh.cell_volumes, divergence_s_inv)

The rectangular area is 36 m², so the area-integrated divergence and outward boundary flux are both -36 m²/s. In 2D, cell_volumes are cell areas.

Handoffs and checks

  • Inspect cell_centers, faces_x, faces_y and the operator shape. Keep cell, face, node and edge quantities distinct when interpolating or plotting.
  • Cell ordering has the first coordinate varying fastest; reshape cell vectors with Fortran order when required. Test a spatially varying field, not a uniform array that could hide an ordering error.
  • Verify constants/linear fields, conservation and convergence against an independent analytic case before using a mesh in an inverse problem.
  • Preserve inactive/air masks and do not turn NaNs into zero conductivity.

See the operator tutorials and API for the chosen mesh.

Frequently asked questions

What does the Discretize AI skill do?

Create finite-volume meshes and discrete differential operators with discretize. Use for TensorMesh or TreeMesh geometry, cell/face/edge ordering, divergence, interpolation and meshes supporting SimPEG; use a physics solver for inversion itself.

Why use Discretize on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/SteadfastAsArt/geoscience-skills/tree/main/discretize. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Discretize?

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 Discretize?

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

Is the Discretize AI skill free?

Yes. It is published on GitHub by SteadfastAsArt 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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