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Hpc Orchestration

Organization
SciMate-AI
hpc-orchestration

Coordinate end-to-end HPC execution workflows across scheduler submission, job monitoring, log tracking, self-healing, and post-processing handoff. Use when a task spans Slurm, PBS, LSF, MPI sizing, queue operations, cluster-safe execution, or multi-stage HPC workflow orchestration.

Overview

PublisherSciMate-AI
RepositoryHPC-Skills
Skill namehpc-orchestration
Stars
97
Forks
10
Bundled files
30
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.

  • 30 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 Orchestration 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/SciMate-AI/HPC-Skills/tree/main/skills/hpc-orchestration
  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/SciMate-AI/HPC-Skills.git /tmp/HPC-Skills
mkdir -p .claude/skills
cp -r /tmp/HPC-Skills/skills/hpc-orchestration .claude/skills/hpc-orchestration
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Hpc Orchestration 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 Orchestration 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 Orchestration 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 Orchestration

Use this skill as the repository-level execution layer above solver-specific HPC skills.

Start

  1. Read references/cluster-operations-manual.md first for any end-to-end cluster workflow.
  2. Read references/lifecycle-manual.md when coordinating solver skills with orchestration stages.
  3. Read references/scheduler-and-parallelism.md when choosing scheduler directives or MPI sizing.
  4. Read references/slurm-launch-patterns.md when the main decision is how to launch work inside a Slurm allocation.
  5. Read references/environment-and-storage-hygiene.md when environment modules, scratch policy, or filesystem behavior may decide workflow reliability.
  6. Read references/data-transfer-and-staging.md when files must be synchronized, staged, archived, or verified.
  7. Read references/software-build-and-reproducibility.md when compilation, package stacks, or rebuildability are in scope.
  8. Read references/interactive-debugging-and-profiling.md when the task needs live diagnosis, scaling studies, or profiler evidence.
  9. Read references/remote-development-and-notebooks.md when VS Code Remote SSH, Jupyter, or port forwarding is involved.
  10. Read references/container-workflows.md when Apptainer or Singularity-style execution is in scope.
  11. Read references/public-protocol.md when aligning solver skills with the repository lifecycle contract.
  12. Read references/error-pattern-dictionary.md when scheduler, monitoring, or log-tracking failures occur.

Additional References

Load these on demand:

  • references/tools-and-scripts.md for shared execution tool roles
  • references/ecosystem-roadmap.md for repository-wide coverage goals
  • references/legacy-template.md when adapting an older solver skill draft into the current format

Reusable Templates

Use assets/templates/ when a concrete scheduler scaffold is needed:

  • slurm-basic.sh
  • slurm-array.sh
  • slurm-packed-single-node.sh
  • slurm-apptainer.sh
  • slurm-perf-report.sh
  • pbs-basic.sh
  • lsf-basic.sh
  • rsync-stage-in.sh
  • jupyter-lab-compute.sh
  • ssh-config-compute-proxy.example

Shared Scripts

Use scripts/ for deterministic orchestration tasks:

  • hpc_job_submitter.py
  • hpc_job_monitor.py
  • hpc_log_tracker.py
  • hpc_slurm_deploy.py

Guardrails

  • Do not run heavy MPI workloads on login nodes.
  • Do not separate queue submission from runtime monitoring in a production workflow.
  • Do not resubmit unchanged failing jobs when a solver-specific error dictionary exists.
  • Do not choose core counts without a scale-based heuristic.

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 Orchestration AI skill do?

Coordinate end-to-end HPC execution workflows across scheduler submission, job monitoring, log tracking, self-healing, and post-processing handoff. Use when a task spans Slurm, PBS, LSF, MPI sizing, queue operations, cluster-safe execution, or multi-stage HPC workflow orchestration.

Why use Hpc Orchestration on TypingMind?

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

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

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 Orchestration?

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

Is the Hpc Orchestration 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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