Hpc Hypre

Organization

SciMate-AI

hpc-hypre

Build, review, debug, and tune hypre-based sparse solver workflows. Use when working with hypre `IJ`, `Struct`, or `SStruct` interfaces, `BoomerAMG`, Krylov solvers, MPI-distributed sparse systems, PETSc integration, or hypre build and runtime failures.

Overview

Publisher	SciMate-AI
Repository	`HPC-Skills`
Skill name	`hpc-hypre`
Stars	97
Forks	10
Bundled files	13
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.
13 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by SciMate-AI on GitHub. Read the source before you install it.

Installation

Install the Hpc Hypre 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/SciMate-AI/HPC-Skills/tree/main/skills/hpc-hypre
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.

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/SciMate-AI/HPC-Skills.git /tmp/HPC-Skills
mkdir -p .claude/skills
cp -r /tmp/HPC-Skills/skills/hpc-hypre .claude/skills/hpc-hypre

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

Use it in TypingMind

Enable Hpc Hypre 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 Hpc Hypre 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 Hpc Hypre 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.

HPC hypre

Treat hypre as a solver-library family with distinct matrix interfaces and multigrid-heavy preconditioning workflows.

Start

Read references/interface-selection.md before creating or repairing a hypre solve path.
Read references/matrix-and-ownership-playbook.md when choosing row ownership, indexing, or assembly discipline.
Read references/boomeramg-and-krylov-playbook.md when choosing BoomerAMG, Krylov solvers, or a first stable parameter set.
Read references/boomeramg-parameter-matrix.md when changing coarsening, interpolation, smoothing, or cycle choices.
Read references/krylov-and-preconditioner-matrix.md when the operator class does not fit the default PCG plus BoomerAMG baseline.
Read references/structured-and-sstruct-playbook.md when the problem is logically structured or semi-structured.
Read references/build-and-integration.md when building hypre directly or consuming it through PETSc or another host code.
Read references/error-recovery.md when setup, solve, or parallel behavior fails.

Work sequence

Classify the discretization storage first:
- general sparse assembled system -> IJ
- structured grid with regular stencil semantics -> Struct
- mostly structured with coupled variable blocks or irregular pieces -> SStruct
Choose a baseline solver pair that matches the operator:
- SPD-like problems -> PCG with BoomerAMG
- general nonsymmetric problems -> GMRES or FlexGMRES with BoomerAMG as the preconditioner
Get the baseline solve working before changing coarsening, interpolation, or relaxation details.
Keep distributed row ownership and matrix assembly consistent across ranks.

Guardrails

Do not use the structured interfaces when the data model is really an unstructured sparse matrix.
Do not start by over-tuning BoomerAMG; defaults are the first checkpoint.
Do not debug multigrid parameters before confirming the operator, nullspace, and boundary conditions are coherent.
Do not assume host applications expose every hypre knob unless the integration layer documents it.

Additional References

Load these on demand:

references/matrix-and-ownership-playbook.md for IJ assembly and distributed indexing rules
references/boomeramg-parameter-matrix.md for practical AMG knob selection
references/krylov-and-preconditioner-matrix.md for solver-family selection beyond the default baseline
references/structured-and-sstruct-playbook.md for structured-grid cases and hybrid layouts
references/build-and-integration.md for PETSc-backed or direct-link builds
references/error-recovery.md for setup, memory, and divergence signatures
references/error-pattern-dictionary.md for quick matching of common hypre failure classes

Reusable Templates

Use assets/templates/ when a concrete starting point is faster than rebuilding the solve path from scratch, especially:

hypre_ij_pcg_boomeramg.c
hypre_build_example.sh
hypre_ij_slurm.sh

Outputs

Summarize:

chosen hypre interface
baseline solver and preconditioner path
multigrid knobs changed from default
integration path such as direct hypre or PETSc-mediated usage
the exact failure stage if the workflow is being repaired

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 Hpc Hypre AI skill do?

Why use Hpc Hypre on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/SciMate-AI/HPC-Skills/tree/main/skills/hpc-hypre. 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 Hpc Hypre?

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 Hpc Hypre?

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

Is the Hpc Hypre AI skill free?

Yes. It is published on GitHub by SciMate-AI 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

HPC hypre

Start

Work sequence

Guardrails

Additional References

Reusable Templates

Outputs

Bundled files

Frequently asked questions

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