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Flopy

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SteadfastAsArt
flopy

Build, run, inspect and diagnose MODFLOW groundwater flow models with FloPy. Use for aquifer discretization, recharge and boundary conditions, hydraulic heads, groundwater budgets and MODFLOW 6 output; use Pastas for statistical head time series.

Overview

PublisherSteadfastAsArt
Repositorygeoscience-skills
Skill nameflopy
Stars
75
Forks
9
Bundled files
1
LicenseMIT
Links
Skill sourceRepository
  • 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.

  • 1 bundled files

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

  • Open source

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

Installation

Install the Flopy 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/flopy
  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/flopy .claude/skills/flopy
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Flopy 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 Flopy 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 Flopy 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.

FloPy

Produce a reproducible groundwater model and a checked water budget. FloPy writes and reads MODFLOW files; solving also requires a compatible MODFLOW executable.

Inputs and choices

  • Establish horizontal CRS, vertical datum, length/time units, layer tops/bottoms, active cells, hydraulic properties, stresses and observation uncertainties.
  • Head is an elevation relative to a datum. Depth below a well collar must first be converted with the collar elevation; a LAS depth curve is not a head series.
  • Choose steady or transient conditions from the question and stress history. Distinguish hydraulic conductivity (length/time), transmissivity (length²/time), recharge (length/time) and pumping (length³/time; extraction is negative).
  • Keep MODFLOW 6 packages separate from older MODFLOW model/package APIs. Use an isolated project environment and record the executable version and origin.

Workflow

  1. Audit geometry, layer ordering and boundary coverage before assigning values.
  2. Build the simulation, temporal discretization, solver, flow model and packages. Specify convertible/confined cells deliberately, with storage for transients.
  3. Write inputs, run the declared executable, and fail if it reports failure.
  4. Inspect head/budget files and listing convergence; mask dry/inactive sentinel values before statistics. Do not treat a successful process as calibration.
  5. Compare observations with uncertainties, inspect budget discrepancy and run grid/time-step sensitivity. Hold out wells or periods before tuning parameters.

Small executable benchmark

Run scripts/confined_flow.py in an empty output folder:

bash
python confined_flow.py --workspace ./flow-check --exe /path/to/mf6

The script models a synthetic confined strip: 11 cells, each 10 m long and 10 m wide, 20 m thick, K = 1 m/day, with fixed heads of 10 and 9 m at the end cell centres. The 100 m separation implies a linear head profile and a flow of 2 m³/day. The layer spans elevations -20 to 0 m relative to an arbitrary local datum, so both imposed heads are above the aquifer top. It checks the head profile and flow against actual MODFLOW output. No recharge, pumping, storage or heterogeneous calibration is implied by this benchmark.

Outputs and interpretation

Keep the input deck, solver/listing output, heads, cell budget and a summary of units, boundary assumptions, executable and package versions. Report residuals and uncertainty separately from parameter plausibility and identifiability.

Read the FloPy documentation for package arguments and MODFLOW 6 documentation for governing equations and solver diagnostics.

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 Flopy AI skill do?

Build, run, inspect and diagnose MODFLOW groundwater flow models with FloPy. Use for aquifer discretization, recharge and boundary conditions, hydraulic heads, groundwater budgets and MODFLOW 6 output; use Pastas for statistical head time series.

Why use Flopy on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/SteadfastAsArt/geoscience-skills/tree/main/flopy. 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 Flopy?

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 Flopy?

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

Is the Flopy 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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