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.
npx skills add SciMate-AI/HPC-Skills --skill hpc-foundationsgit clone --depth 1 https://github.com/SciMate-AI/HPC-SkillsWrote 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.
[](https://agentmods.dev/skills/scimate-ai/hpc-skills/hpc-foundations)<a href="https://agentmods.dev/skills/scimate-ai/hpc-skills/hpc-foundations"><img src="https://agentmods.dev/badge/skills/scimate-ai/hpc-skills/hpc-foundations/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.
<a href="https://agentmods.dev/skills/scimate-ai/hpc-skills/hpc-foundations"><img src="https://agentmods.dev/badge/skills/scimate-ai/hpc-skills/hpc-foundations.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00087 | $0.00506 |
| Opus 5 | $0.00044 | $0.00253 |
| Sonnet 5 | $0.00017 | $0.00101 |
| Haiku 4.5 | $0.00009 | $0.00051 |
Grade A, and why
hpc-foundations 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 10d ago.
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.
What it actually says
HPC Foundations
Use this skill as the general HPC knowledge hub that sits below solver-specific skills and beside hpc-orchestration.
Start
- Read
references/index-map.mdfirst for the knowledge graph and the shortest route to the right topic. - Read
references/foundations-architecture-and-programming.mdwhen the task starts from HPC concepts, system design, programming models, application development, or performance basics. - Read
references/schedulers-and-execution.mdwhen the task is about Slurm, PBS Pro, LSF, queue semantics, batch scripts, arrays, or accounting commands. - Read
references/linux-and-os.mdwhen the task is about Linux usage, login patterns, package management, glibc compatibility, or Linux versus Windows tradeoffs on clusters. - Read
references/software-environments.mdwhen the task is about Conda, containers, Singularity or Apptainer-style execution, or picking common HPC tools. - Read
references/storage-network-cloud-and-admin.mdwhen the task is about storage tiers, RDMA, hardware, cloud deployment, SSL, Hyper-V maintenance, or site-level administration topics. - Read
references/source-catalog.mdwhen a page-level lookup of the hpclib.com crawl is needed. - Read
references/skillization-roadmap.mdwhen splitting this index into narrower reusable skills.
Positioning
- Prefer this skill for orientation, taxonomy, and first-pass explanation.
- Prefer
hpc-orchestrationonce the question becomes an execution workflow on a real cluster. - Prefer solver-specific skills once the question becomes input-deck or application specific.
Guardrails
- Treat hpclib.com as a practical knowledge index, not as the final authority for production commands.
- Re-check scheduler flags, MPI launch syntax, package names, and admin procedures against official upstream documentation before applying them on a live cluster.
- Do not assume cluster policies, filesystem layouts, or security rules are portable across sites.
What ships with it
9 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.
- agents/openai.yaml 228 B
- references/foundations-architecture-and-programming.md 3.3 KB
- references/index-map.md 4.3 KB
- references/linux-and-os.md 3.8 KB
- references/schedulers-and-execution.md 4.3 KB
- references/skillization-roadmap.md 3.3 KB
- references/software-environments.md 2.8 KB
- references/source-catalog.md 7.8 KB
- references/storage-network-cloud-and-admin.md 3.5 KB
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.
- 10d ago First seen · 32 lines · 87 tokens per session scan A 02e71487e190
hpc-foundations is a skill published in the GitHub repository SciMate-AI/HPC-Skills (85 stars, last pushed 5mo ago), licensed MIT. It adds 87 tokens to every session and 506 once invoked, about $0.0004 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.
Other skills, from other repositories
compute-env-setup
Set up a reproducible Feynman compute environment for research jobs. Use when a task needs Python/R packages, GPU libraries, containers, Modal, SSH, caches, or managed model runtime setup.
ngs-pipeline-management
Next-generation sequencing pipeline management with Snakemake and Nextflow. Use for building, running, and debugging reproducible NGS workflows for RNA-seq, WGS, ChIP-seq, ATAC-seq, and amplicon sequencing. Covers pipeline design patterns, HPC/cloud execution, containerization, and workflow optimization.
pipeline-chipseq
Execute ENCODE ChIP-seq processing pipeline from FASTQ to peaks and signal tracks. Child of pipeline-guide. Provides stage-by-stage Nextflow execution with Docker containers and cloud deployment. Use when users need to process ChIP-seq data following ENCODE standards, run peak calling with MACS2, perform IDR analysis…
pipeline-dnaseseq
Execute ENCODE DNase-seq pipeline from FASTQ to hotspots and footprints. Child of pipeline-guide. Provides Nextflow execution with Docker and cloud deployment. Use when processing DNase-seq data, calling DNase hypersensitive sites, performing footprinting analysis. Trigger on: DNase-seq pipeline, DNase hypersensitive…
pipeline-wgbs
Execute ENCODE Whole Genome Bisulfite Sequencing (WGBS) pipeline from FASTQ to methylation calls. Child of pipeline-guide. Provides Nextflow execution with Docker and cloud deployment. Use when processing WGBS/bisulfite-seq data, calling methylation levels, generating bedMethyl files. Trigger on: WGBS pipeline…
nextflow-workflow-engine
Dataflow workflow engine for scalable bioinformatics pipelines. Defines processes (containerized tasks) connected by channels; runs local, HPC (SLURM/SGE), cloud (AWS/GCP/Azure), or Kubernetes via a single config change. Powers nf-core. Use Snakemake for rule-based Python workflows; use Nextflow for containerized…