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SteadfastAsArt
rockhound

Maintain or migrate legacy RockHound geoscience dataset downloads, caches and loaders such as PREM. Use when an existing project imports RockHound; prefer maintained Ensaio fetchers for supported new dataset workflows.

Overview

PublisherSteadfastAsArt
Repositorygeoscience-skills
Skill namerockhound
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 Rockhound 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/rockhound
  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/rockhound .claude/skills/rockhound
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Rockhound 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 Rockhound 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 Rockhound 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.

RockHound maintenance and migration

RockHound's repository was archived in 2022 and recommends Ensaio. Preserve a working legacy environment while assessing supported replacements; there is no universal one-to-one mapping of fetcher names or return objects.

Inspect an existing workflow

Identify the exact fetcher, data version, source URL/hash, cache location, parser and scientific conventions. Set ROCKHOUND_DATA_DIR before importing RockHound if a separate cache is needed. Never delete a user's cache to test a migration.

Legacy PREM load

This fragment uses the upstream registry and may download from EarthScope/IRIS. It loads the 1 s PREM radial Earth model, not a local field observation. The registry URL returned HTTP 404 during the 2026-09-14 check. This example currently needs an existing hash-verified cache or a separately verified source; it is not a successful fresh-download recipe. The test suite exercises the real loader with a clearly synthetic cache fixture, without claiming PREM validation.

python
# example: legacy-prem
import rockhound

prem = rockhound.fetch_prem()
radius_km = prem["radius"].to_numpy()
depth_km = prem["depth"].to_numpy()
density_g_cm3 = prem["density"].to_numpy()
vpv_km_s = prem["Vpv"].to_numpy()
vsv_km_s = prem["Vsv"].to_numpy()

The table contains discontinuities and distinct vertical/horizontal velocities. Preserve repeated boundary radii and choose a side explicitly for interpolation. Zero shear velocity in a fluid region is meaningful, not a missing value. Do not send the full radial model directly to a plane-layer surface-wave solver.

Migration steps

  1. Find a maintained dataset release, its redistribution license and immutable identifier. If Ensaio has no matching dataset, use a documented Pooch registry and the original provider instead of substituting unrelated sample data.
  2. Compare downloaded hashes or document changed releases; inspect units, grid registration, coordinates, missingness and source preprocessing.
  3. Adapt the return contract: an Ensaio path still needs a CSV/NetCDF reader, while RockHound may return a DataFrame or xarray object.
  4. Verify representative values and downstream results before replacing imports. Diagnose failed old URLs; do not bypass TLS or checksum checks.

See the archived project notice, PREM loader contract and Ensaio catalog.

Frequently asked questions

What does the Rockhound AI skill do?

Maintain or migrate legacy RockHound geoscience dataset downloads, caches and loaders such as PREM. Use when an existing project imports RockHound; prefer maintained Ensaio fetchers for supported new dataset workflows.

Why use Rockhound on TypingMind?

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

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

Which AI models can use Rockhound?

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 Rockhound?

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

Is the Rockhound 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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