Open Infra Skills is a vendor-aware library of portable agent skills for building, operating, profiling, and optimizing AI infrastructure, currently focusing on accelerator training optimization.

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License

Apache-2.0

Last updated

2026-07-10

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FR-AI / ANALYSIS

Why it is worth attention

It packages repeatable, evidence-based engineering workflows as self-contained agent skills, promoting reproducibility, correctness gating, and portable orchestration across diverse AI infrastructure environments.

Who it is for

  • AI infrastructure engineers
  • Machine learning performance engineers
  • Cluster operators and DevOps for AI systems
  • Developers working with GPU/accelerator bring-up and optimization

Use cases

  • Establishing reproducible baselines before system changes
  • Profiling and optimizing distributed training on MUSA GPUs
  • Diagnosing performance across application, system trace, and kernel levels
  • Validating numerical correctness alongside performance improvements

Strengths

  • Evidence-first design with explicit measurement and separation of facts from hypotheses
  • Portable across local machines, SSH, schedulers, containers, and clouds without client OS assumptions
  • Progressive disclosure: focused main skill file with deeper references only when needed
  • Quality bar requires actionable workflows, verification steps, and honest negative results

Considerations

  • Early development with only one published skill (MUSA training optimization)
  • Relies on the Skills CLI and compatible agents, limiting immediate adoption to the Open Agent Skills ecosystem
  • Currently narrow accelerator scope; other categories (distributed training, inference, etc.) are planned but not yet implemented

README quick start

Open Infra Skills

Open, portable agent skills for building, operating, profiling, and optimizing AI infrastructure.

This repository is intended to grow into a comprehensive, vendor-aware library for the full AI systems lifecycle: accelerator bring-up, distributed training, inference serving, kernels and compilers, data pipelines, observability, benchmarking, and cluster operations.

Why This Repository Exists

AI infrastructure work is full of environment-specific details, expensive experiments, and performance claims that are easy to mismeasure. Open Infra Skills packages repeatable engineering workflows as self-contained agent skills so that an assistant can:

  • establish a reproducible baseline before changing a system;
  • choose the right diagnostic layer, from application timing to system traces and kernel counters;
  • preserve numerical behavior while optimizing performance;
  • distinguish measured evidence from hypotheses;
  • work across local machines, SSH hosts, schedulers, containers, and clouds without assuming one client operating system.

Skill Catalog

SkillCategoryWhat it helps with
optimize-musa-trainingAcceleratorsProfile, debug, and optimize AI training on Moore Threads MUSA GPUs, including MFU/HFU, Moore Perf, FA2, torch.compile, FSDP, MCCL, and kernel-level analysis.

The machine-readable catalog is available in catalog.json.

Install

The repository follows the open Agent Skills directory convention. Use the Skills CLI to inspect or install it:

# List all skills in the repository.
npx skills add open-infra-skills/infra-skills --list

# Install one skill interactively.
npx skills add open-infra-skills/infra-skills --skill optimize-musa-training

# Install one skill globally for Codex without prompts.
npx skills add open-infra-skills/infra-skills \
  --skill optimize-musa-training \
  --global \
  --agent codex \
  --yes

You can also clone the repository and point a compatible agent at a skill directory directly. Each skill keeps its instructions and optional scripts, references, and agent metadata together.

Repository Layout

infra-skills/
|-- skills/
|   `-- /
|       `-- /
|           |-- SKILL.md
|           |-- agents/       # Optional agent-facing metadata
|           |-- references/

Description

Open, portable agent skills for AI infrastructure engineering.

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