agentmods.dev

hpc-orchestration

hpc-orchestration is a skill for Codex from SciMate-AI/HPC-Skills. It costs 64 tokens per session (655 once invoked), scanned A, original, MIT.

A guide for coordinating jobs on high-performance computing clusters, which are groups of computers used for large calculations. It covers schedulers such as Slurm, PBS, and LSF, along with multi-stage workflows.

In plain words
What is it for?
Use it to size MPI runs, submit and track jobs, recover failed work, move and verify data, manage scratch storage, build software, debug live jobs, and connect remote tools.
Why use it?
It helps manage submission, monitoring, logs, files, environments, failures, and handoffs between calculation and post-processing steps.

Skill for Codex

Written for Codex: agents/openai.yaml present.

not rated 86repo +2 5mo ago A scan 64 tokens original MIT

Good fit Use it to size MPI runs, submit and track jobs, recover failed work, move and verify data, manage scratch storage, build software, debug live jobs, and connect remote tools.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/scimate-ai/hpc-skills/hpc-orchestration
View source ↗ SciMate-AI/HPC-Skills
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add SciMate-AI/HPC-Skills --skill hpc-orchestration
Clone the repo
git clone --depth 1 https://github.com/SciMate-AI/HPC-Skills

Made for: Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for hpc-orchestration

README.md
[![agentmods](https://agentmods.dev/badge/skills/scimate-ai/hpc-skills/hpc-orchestration/github.svg)](https://agentmods.dev/skills/scimate-ai/hpc-skills/hpc-orchestration)
Your own site
<a href="https://agentmods.dev/skills/scimate-ai/hpc-skills/hpc-orchestration"><img src="https://agentmods.dev/badge/skills/scimate-ai/hpc-skills/hpc-orchestration/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for hpc-orchestration

Your own site · 80×15
<a href="https://agentmods.dev/skills/scimate-ai/hpc-skills/hpc-orchestration"><img src="https://agentmods.dev/badge/skills/scimate-ai/hpc-skills/hpc-orchestration.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 64 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 655 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00064 $0.00655
Opus 5 $0.00032 $0.00328
Sonnet 5 $0.00013 $0.00131
Haiku 4.5 $0.00006 $0.00065

Measured 11d ago against content hash 9db61651ee44, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

hpc-orchestration scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.

The scan reads SKILL.md. This mod also ships 13 executable files (assets/templates/jupyter-lab-compute.sh, assets/templates/lsf-basic.sh, assets/templates/pbs-basic.sh, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/hpc-orchestration/SKILL.md · 63 lines

How it starts

The opening of the file, as written. The whole thing — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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

Read the full file on GitHub · 63 lines

Files

What ships with it

30 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 11d ago First seen · 63 lines · 64 tokens per session scan A 9db61651ee44

Subscribe to this mod's changes

hpc-orchestration is a skill published in the GitHub repository SciMate-AI/HPC-Skills (86 stars, last pushed 5mo ago), licensed MIT. It adds 64 tokens to every session and 655 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

DevOps and cloudScientific
Related

Other skills, from other repositories

instrument-data-to-allotrope

Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…

anthropics/knowledge-work-plugins · 123 tokens

exploratory-data-analysis

Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…

K-Dense-AI/scientific-agent-skills · 83 tokens

matlab

Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.

K-Dense-AI/scientific-agent-skills · 42 tokens

phylogenetics

Build and analyze phylogenetic trees using MAFFT (multiple alignment), IQ-TREE 2 (maximum likelihood), and FastTree (fast NJ/ML). Visualize with ETE3 or FigTree. For evolutionary analysis, microbial genomics, viral phylodynamics, protein family analysis, and molecular clock studies.

K-Dense-AI/scientific-agent-skills · 68 tokens

research-engineer

An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.

davila7/claude-code-templates · 43 tokens

mapping-to-snomed

Maps clinical concept spans extracted by OpenMed to SNOMED CT concepts through a USER-SUPPLIED terminology server (the user's own Ontoserver, Snowstorm, or UMLS/UTS), never a bundled vocabulary. Use when the user wants to code findings, disorders, procedures, body structures, or substances to SNOMED CT, run an ECL…

maziyarpanahi/openmed · 205 tokens